Created By: Avery Horne
Accuracy of genomic breeding values for residual feed intake in crossbred beef cattle1
F. D. N. Mujibi*,
J. D. Nkrumah*,
O. N. Durunna*,
D. H. Crews Jr.*,‡ and
S. S. Moore*,2
+ Author Affiliations
*Department of Agriculture, Food and Nutritional Science, University of Alberta, Edmonton, Alberta, T6G 2P5, Canada;
†Alberta Agriculture and Rural Development, 6000 C & E Trail, Lacombe Research Centre, Lacombe, Alberta T4L 1W8, Canada; and
‡Department of Animal Sciences, Colorado State University, Fort Collins 80523-1171
↵2Corresponding author: email@example.com
The benefit of using genomic breeding values (GEBV) in predicting ADG, DMI, and residual feed intake for an admixed population was investigated. Phenotypic data consisting of individual daily feed intake measurements for 721 beef cattle steers tested over 5 yr was available for analysis. The animals used were an admixed population of spring-born steers, progeny of a cross between 3 sire breeds and a composite dam line. Training and validation data sets were defined by randomly splitting the data into training and testing data sets based on sire family so that there was no overlap of sires in the 2 sets. The random split was replicated to obtain 5 separate data sets. Two methods (BayesB and random regression BLUP) were used to estimate marker effects and to define marker panels and ultimately the GEBV. The accuracy of prediction (the correlation between the phenotypes and GEBV) was compared between SNP panels. Accuracy for all traits was low, ranging from 0.223 to 0.479 for marker panels with 200 SNP, and 0.114 to 0.246 for marker panels with 37,959 SNP, depending on the genomic selection method used. This was less than accuracies observed for polygenic EBV accuracies, which ranged from 0.504 to 0.602. The results obtained from this study demonstrate that the utility of genetic markers for genomic prediction of residual feed intake in beef cattle may be suboptimal. Differences in accuracy were observed between sire breeds when the random regression BLUP method was used, which may imply that the correlations obtained by this method were confounded by the ability of the selected SNP to trace breed differences. This may also suggest that prediction equations derived from such an admixed population may be useful only in populations of similar composition. Given the sample size used in this study, there is a need for increased feed intake testing if substantially greater accuracies are to be achieved.
genomic breeding value
residual feed intake
single nucleotide polymorphism
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A large number of genomic tools have become available because of the rapid advancement of DNA marker technology after the mapping (and sequencing) of the bovine genome. This has led to increasing demands for inclusion of DNA marker tools in traditional evaluation systems, to yield marker-assisted EBV, often with greater accuracy compared with traditional EBV (Johnston et al., 2008). Various strategies have been suggested for inclusion of marker information in genetic evaluations, but so far none of the methods is optimal (VanRaden, 2001; Dekkers, 2007; Kachman, 2008). Results from a DNA test can be used to create a molecular score (MS) or a molecular breeding value, which is often a weighted sum of the number of copies of the frequent alleles of several polymorphisms with the weights estimated in a reference data set (Kachman, 2008). Because MS will likely account for only a small portion of the total genetic variance, it will be necessary to combine polygenic and molecular breeding value into a single selection tool (VanRaden, 2001; Dekkers, 2007; Kachman, 2008). Selection index methodologies have been shown in simulation to be useful in combining polygenic and molecular breeding values (Dekkers, 2007; Crews, 2008). Genomic selection is also seen as a viable option where selection is based solely on genomic breeding values (GEBV; Meuwissen et al., 2001). Recently, Bayesian estimation has emerged as the method of choice for genomic selection because it allows different variances to be fitted to each SNP (Fernando et al., 2007; Moser et al., 2009). Genomic selection has been successfully applied in the prediction of performance in dairy cattle, but such success has not been realized in beef cattle populations (MacNeil et al., 2010). In this study, Bayesian-based methods and the theory underlying genomic selection were used to select a subset of markers, and ultimately to derive GEBV whose ability to predict RFI, DMI, and ADG was then evaluated using data from an admixed population of beef cattle steers.
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MATERIALS AND METHODS
The Canadian Council on Animal Care (1993) protocols and guidelines were followed when caring for the animals.
Animal Resource and Study Design
(1)Data consisted of 721 crossbred steers sired by Angus, Charolais, or University of Alberta hybrid bulls with a composite dam line. The composition of the dam line is described in detail by Goonewardene et al. (2003). Feed intake data were collected over a 5-yr period, with 2 groups (fall-winter and winter-spring) tested every year for the first 3 yr. In yr 4, 1 group of animals was tested for 2 consecutive periods (fall-winter, and then winter-spring), first on a low-energy feedlot diet in period 1 (fall-winter), and then a high-energy feedlot diet in period 2 (winter-spring). In yr 5, 2 groups of animals were tested in 2 consecutive periods as follows: The first group was put on a high-energy feedlot diet for both periods, whereas the second group was first tested on a lower energy diet and then switched to a high-energy diet in period 2. Animals had free-choice access to feed and water. In total, 9 batches of animals were available for analysis, with a batch being a combination of year and season of testing. All batches were placed into 3 groups as follows: Fall-winter tested animals were in group 1, winter-spring test animals were in group 2, and diet-switch animals were in group 3.
Individual animal feed intake and feeding behavior data were collected using the GrowSafe automated feeding system (GrowSafe Systems Ltd., Airdrie, Alberta, Canada) at the University of Alberta Kinsella ranch. Daily feed intake was converted into daily DMI by multiplying intake by the DM content of the diet. Daily DMI was then standardized across the different years to 10 MJ of ME/kg of DM by multiplying daily DMI with the diet ME content and then dividing by 10 (Basarab et al., 2003). Average daily gain was calculated as the slope from the regression of BW on test day. Metabolic midweight was obtained as the midweight on test raised to the power of 0.75.
Residual feed intake (RFI) was calculated within group using the following formula:RFI = DMI − (β0 + β1batch + β2ADG + β3MMWT),where β1, β2, and β3 are partial regression coefficients; β0 is the intercept; and MMWT is metabolic midweight.
Training and validation data sets were defined by randomly splitting the data into a training set (2/3, n = 485) and a testing set (1/3, n = 243) based on sire family so that there was no overlap of sires in the 2 sets. This random split was replicated 5 times such that there were 5 training and 5 testing data sets. Random splitting by sire family reduces the ability of genetic markers to approximate the relationship between individuals in the training and testing data, thereby minimizing chances of an inflated correlation of GEBV and trait phenotype in the prediction process (Habier et al., 2007). The first replicate of the training data was used for SNP preselection, and the selected SNP were then reanalyzed in all replicates of the training data. The association between genotypes and phenotypes was tested in the training set, whereas the accuracy of prediction of the marker-derived breeding value explored in the testing set was tested as the correlation between GEBV and phenotypes.
Approximately 50,000 SNP were genotyped for 745 beef steers by using the Illumina Infinium II (Illumina Inc., San Diego, CA) platform. These SNP were tested for Hardy-Weinberg equilibrium (P > 0.05), minor allele frequency (>5%), and SNP call frequency (>88%), with nonqualifying SNP being discarded. Ultimately, a total of 38,158 SNP were selected for further analysis. Genotypes were coded as 0, 1, and 2, with 0 being the SNP allele with the lesser frequency and 1 being the allele with the greater frequency, respectively, such that the 2 homozygotes were represented as 0 and 2, and 1 was the heterozygote. Missing genotypes (about 1% of all genotypes) were imputed by submitting SNP genotype calls as well as missing genotype information to fastPHASE (Scheet and Stephens, 2006) chromosome by chromosome, the SNP having been ordered according to their chromosomal position. The parameters used were as follows: 10 random starts of the expectation-maximization (EM) algorithm (T), 30 iterations of the EM algorithm (C), 15 cross-validation clusters (K), and no sampling of haplotypes from the posterior distribution of each random start of the EM algorithm (H). The most probable genotype imputed by fastPHASE was considered the true genotype. All SNP with unknown chromosomal positions were discarded. A final 37,959 SNP were included in the analysis.
The following animal model was used in the whole data set to estimate polygenic breeding values, variance components, and genetic parameters using ASReml (Gilmour et al., 2008). The model included fixed effects of contemporary group (breed, batch, and test group combinations), with age at the start of test as a covariate, as shown below:y1 = X1β + Z1a + e, where the design matrices X1 and Z1 relate phenotypic observations in the vector y1 to fixed (β) and polygenic (a) effects, respectively. The vector e contains random residual terms specific to animals. The parameters a and e were assumed to be normally distributed, with a mean of 0 and variances Graphic and Graphic respectively. The matrix In is an identity matrix of order equal to the number of animals with RFI observations, whereas A is the additive relationship matrix, Graphicis the random polygenic effect variance, and Graphic the residual variance, respectively. Accuracy was calculated using the formula Graphic with se2 being the prediction error variance and Graphic being the additive genetic variance (Gilmour et al., 2008). A bivariate model was used to compute genetic correlations between the traits by extending Eq.  to include a second trait.
Bayesian Estimation of Marker Effects
Estimation of marker effects was performed using 2 models:
1. Random regression BLUP (RR-BLUP), which assumes the same prior variance for all random SNP, as described by Meuwissen et al. (2001).
2. BayesB, in which a locus-specific variance is estimated but the loci are divided into 2 groups: one group of a relatively small number of SNP with large effects that contribute to the genetic variance with probability (1 − π), and a second group of a large number of SNP with no effect, with probability π (Meuwissen et al., 2001). The BayesB model used was similar to that of Meuwissen et al., (2001), except that effects of SNP genotypes and not haplotype were fitted.
The BayesB model makes strong assumptions about the prior distribution of marker effects, namely, a large proportion of SNP have no effect. The BayesB and RR-BLUP models used are implemented in the AlphaBayes software (Hickey and Tier, 2009), which uses a modified version of the Gibbs sampling algorithm to solve for model effects. The SnpBlup and BayesBFast implementations in AlphaBayes were used for RR-BLUP and BayesB analyses, respectively. Even though the real value of π was unknown for this data set, π was set at 0.95 for all analyses, such that 5% of SNP were fitted simultaneously in each cycle of the Gibbs chain.
The model of analysis used for RR-BLUP and BayesB was as follows:y1 = X1β + Z1a* + Z2g + e, where the design matrices X1, Z1, and Z2 relate phenotypic observations in the vector y1 to fixed (β), residual polygenic (a*), and SNP (g) effects, with elements Z2ij = 0, 1, or 2, corresponding to the genotype of animal i at locus j, with g normally distributed with mean 0, and variance Graphic for RR-BLUP, and drawn from an inverse χ2 distribution with probability π in BayesB. The variance Graphic in RR-BLUP, and was estimated for each instance of j in BayesB. The vector e contains random residual terms specific to animals. The parameters a* and e were treated as random. The matrix In is an identity matrix of order equal to the number of animals with trait observations, whereas A is the additive relationship matrix, Graphicis the random residual polygenic effect variance, and Graphic is the residual variance. Fixed effects fitted included contemporary group (breed-batch-test group combinations), whereas age at the start of test was used as a covariate.
(2)The first 20,000 iterations from the total 100,000 iterations were discarded as burn-in. Mean SNP substitution effects were obtained from the posterior samples for each trait, and SNP ranked from greatest to least based on the magnitude of the allele substitution effect. From this ranking, the top 200 SNP were selected for further analysis. Allele substitution effects for the selected SNP were reestimated in each of the 5 replicates of the training data, with the first 5,000 iterations of the total of 20,000 discarded as burn in. For this analysis, π was set to 0.0005 so that estimates for all 200 SNP could be obtained.
Genomic Value Estimation
Trait-specific marker panels were obtained from analysis using the various methods outlined above. The SNP were subsequently used to derive marker scores. Marker scores were calculated as a weighted sum of the number of copies of the more frequent allele at each SNP locus, with the weights being the allele substitution effects (β) estimated. The summation of all MS for each individual yielded a GEBV:Formulawhere Tij represents the marker genotype of animal i at SNP j, coded 0, 1, and 2 as described previously; Graphic is the estimate of SNP effect j; and Nm is the number of SNP. The following nomenclature Graphicwas used for clarity. The GEBV were derived for panels with all 37,959 markers as well as the top 200 SNP for each trait.
The accuracy of prediction for the GEBV was assessed as the correlation between GEBV and the phenotype both within and across sire breeds.
Candidate Gene Analysis for RFI
For the trait of RFI, the 1:2 ratio of validation to training records was randomly replicated 5 times, and each replicate was analyzed using both RR-BLUP and BayesB methods so as to obtain SNP that consistently ranked within the top 200 because these were likely viable candidate genes for RFI. The number of times that an SNP was ranked within the top 200 after the 5 analyses yielded the “detection” frequency, expressed as a percentage. The positions of SNP with the greatest detection frequency were used to search for gene annotations and associated publications in Entrez Gene, HomoloGene, and PubMed.
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Genetic Parameters and Variance Components
Phenotypic and genetic correlations between the 3 traits analyzed are shown in Table 1. Correlations were greatest between ADG and DMI and were least between ADG and RFI. There were significantly high phenotypic and genetic correlations for DMI with both RFI and ADG.
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Table 1. Genetic (below diagonal) and phenotypic (above diagonal) correlations between feed intake and efficiency traits1
Table 2 gives variance components and genetic parameters for the traits evaluated. Estimates of phenotypic and genetic variance were greatest for DMI and least for ADG. Subsequently, single-trait heritability estimates for RFI and ADG were moderate to low, whereas DMI heritability was in the medium range.
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Table 2. Variance components and parameter estimates for feed intake and efficiency traits
Accuracy of GEBV Prediction
Table 3 shows trait-specific as well as between-trait correlations for GEBV with RFI, DMI, and ADG. For both BayesB and RR-BLUP with the 200 SNP panel, the highest correlation was observed between RFI and Graphic whereas the lowest correlation was observed between DMI and Graphic Accuracies between ADG with Graphic(GEBV obtained from estimates for association with ADG, but using SNP identified by training on RFI) were very low, whereas association between DMI and Graphic(GEBV obtained from estimates for association with DMI but using SNP identified by training on RFI) yielded higher correlations than trait-specific values. Correlations between traits and GEBV with all 37,959 markers included yielded lower correlations than those using only a subset of the top 200 SNP for both BayesB and RR-BLUP (Table 3). Generally, the RR-BLUP method yielded greater prediction accuracies than did BayesB, whereas prediction accuracy for RFI was greater than for DMI and ADG.
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Table 3. Correlations of GEBV200 and GEBV37959 with trait phenotypes for BayesB and RR-BLUP analyses1
In Table 4, trait-specific correlations for different sire breeds are shown, for panels trained using both BayesB and RR-BLUP. The correlation of GEBV and RFI was slightly different within sire breed compared with the value obtained in across-breed comparisons. Further, for RR-BLUP, there was a pattern of differential accuracy within sire breed, with differences observed depending on what trait was being evaluated. For ADG, the Hybrid and Angus breeds tended to differ from each other, whereas for RFI, the Charolais sire breed tended to have a correlation pattern different from the Hybrid and Angus breeds (Table 4).
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Table 4. Correlations (±SE, as the average of 5 replications) between GEBV200 and trait phenotypes by sire breed for GEBV trained using BayesB and RR-BLUP1
Candidate Genes for RFI
Eleven SNP associated with RFI were consistently ranked within the top 200 in 3 of 5 replicates (detection frequency of 60%) when the training data were analyzed using the RR-BLUP model. The greatest detection frequency obtained using the BayesB method was 40% (a total of 28 SNP had this detection frequency), whereas 92 SNP had a detection frequency of 40% or greater with the RR-BLUP method. Seven of the 11 SNP with detection frequency 60% were located either within a gene or close to a gene whose function could affect feed intake or feed efficiency (Table 5). Further, 4 of the 11 SNP were identified with a 40% detection frequency when using the BayesB method, whereas all 92 SNP from RR-BLUP had a detection frequency of at least 20% with the BayesB method. A total of 6 SNP were common between the 92 from RR-BLUP and the 28 from BayesB.
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Table 5. Locations, closest genes, and associated gene functions for SNP that ranked within the top 200 in 3 of 5 replicates of the training data analyzed using the random regression BLUP (RR-BLUP) method1
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Diagnosis of convergence for the posterior estimates of SNP effects obtained after burn in was not carried out in this study. This is because the software program used for this analysis did not lend itself to such interrogation. However, AlphaBayes has been extensively tested for convergence in several simulated and real data sets. For data sets with 60,000 SNP, 60,000 Markov chain Monte Carlo samples with a burn-in of 10,000 samples was always enough to obtain convergence (J. Hickey, Animal Genetics and Breeding Unit, University of New England, Armidale, New South Wales, Australia, personal communication). In this study, we used 100,000 Markov chain Monte Carlo samples, with the first 20,000 samples discarded as burn in to ensure that convergence was likely to be reached. As a further confirmation, 2 independent runs with different starting values were applied, and the estimates of SNP effects obtained thereafter (data not shown) had negligible differences between the runs. This gave an indication that the number of iterations chosen and the burn-in threshold were sufficient.
The strategy used in this analysis, to limit the number of SNP used for GEBV estimation to the top 200, was to maximize the chance of capturing a large number of SNP in greater linkage disequilibrium (LD) with underlying QTL as well as to reduce the number of redundant markers. Studies by Kizilkaya et al. (2010) and Zhong et al. (2009) have shown that panels that include QTL or markers in greater LD with QTL perform better when predicting across breeds or across multiple generations. The foregoing assumption is that markers with a large effect signify markers in greater LD with the trait, and thus account for a larger portion of the trait variance. This strategy in itself has a practical implication in that by using a subset of SNP instead of the whole range of markers available in the analysis, equivalent prediction accuracy can be achieved without incurring the costs of genotyping associated with high-density SNP chips when used in a commercial application. In any case, it is very probable that for the 50K bovine SNP chip, only a subset of markers are useful for prediction purposes for various traits, and inclusion of additional SNP increases noise without a substantial change in prediction accuracy. This has been demonstrated in several studies (Luan et al., 2009; Kizilkaya et al., 2010) in which smaller subsets of markers have achieved accuracies equivalent to or greater than those of larger sets.
In this study, for all traits with 200 SNP markers, the BayesB method performed marginally less well than the RR-BLUP method. When allele substitution effects of SNP selected using RFI were reestimated using ADG as the training phenotype, the resulting GEBV Graphic could not predict ADG for either BayesB or RR-BLUP. However, the same process with DMI resulted in a greater predictive accuracy than when using trait-specific GEBV Graphic The RFI SNP panel was able to achieve greater accuracies with DMI than when using the within-trait panel. This offers the prospect of a multitrait panel that can be used for both DMI and RFI. When using all available SNP (37,959), the predictive accuracy was much less than that observed with a smaller subset of 200 SNP. This informed the decision not to evaluate all 5 replicates with the full SNP panel (37,959), but rather to concentrate on the top 200 SNP.
Differences Between Methods
The performances of BayesB and RR-BLUP were quite varied, given the differences in assumptions for the Bayesian and BLUP methods. In the Bayesian methods, posterior estimates are influenced to a large extent by the choice of parameters given by the prior distribution. The biggest difference between the methods is in the assumptions associated with SNP variances. Typically, the genetic variance associated with each SNP in RR-BLUP is assumed to be small, and a uniform value of Graphic is often used (as in this study because it is the one implemented in AlphaBayes), where Graphic is the total genetic variance estimated by REML, Graphicthe variance associated with each SNP, and n is the number of loci. This SNP variance structure has been deemed unrealistic because many of the SNP are believed to have a small or no effect on trait variance, and many effects are fitted compared with the number of records present (Xu, 2003). An alternative definition, Graphic has been proposed (with pj being the frequency of an allele at locus j), under assumptions of Hardy-Weinberg equilibrium and linkage equilibrium between QTL (Fernando et al., 2007).
Given that RR-BLUP fits all marker effects in the model, with marker variances obtained as a fraction of the total genetic variance, a larger number of markers would be needed to account for substantial genetic variance, especially for traits with low genetic variance. This means that for the RR-BLUP method, to achieve equivalent prediction accuracy compared with the Bayesian methods, larger SNP panels would be necessary, especially for ADG and RFI, whose trait variance is small compared with DMI, whereas n is the same. Therefore, the results obtained in this study run contrary to that expectation. Such a result as seen in this study is possible where the SNP selected actually capture a reasonable proportion of QTL underlying the traits, which in turn reduces the number of SNP markers required in the prediction panel. The ability of the selected markers to be effective in prediction can be tested only by validation in an independent population.
Further, based on the suggestion by Meuwissen et al. (2001) that large QTL are heavily regressed back to the mean in RR-BLUP, the effects estimated by RR-BLUP will typically be small in comparison with those from Bayesian analyses, which fit only a fraction Graphic of the total numbers of SNP available. This means that given that the SNP selection was accomplished by ranking SNP from greatest to least based on the magnitude of the allelic substitution effect, such regression would lower the rank of erstwhile larger QTL.
The use of a Bayesian model that includes a polygenic effect is expected to aid in effect estimation by properly partitioning the phenotypic variance to the various components. However, some studies (e.g., Calus and Veerkamp, 2007) have alluded to the minimal influence of including polygenic effects on accuracy in genomic selection analyses.
(3)In all instances, the RR-BLUP method obtained greater correlations than the BayesB method. This difference may be related to the underlying genetic architecture of the traits. The infinitesimal model applied by RR-BLUP may fit the RFI and DMI data quite well compared with the notion of a few key QTL underlying the traits, as implemented in BayesB. Given that the range of metabolic processes that underlie RFI is quite large (Richardson and Herd, 2004) and considering recent discoveries suggesting that many putative genes may be associated with feed intake (Barendse et al., 2007; Chen et al., 2009), there is increasing evidence to suggest that a larger portion of the trait variance is under the influence of many QTL of small effect. This lends support to assertions that the assumptions underpinning RR-BLUP may closely approximate the genetic architecture for RFI and DMI compared with Bayesian models. Still, a substantial number of QTL of large effect may be affecting these 2 traits.
On the other hand, given that little variation typically exists in ADG between animals both in this study and in similar studies, it is logical to assume that the genetic contribution toward this trait may be limited to a smaller number of QTL compared with RFI and DMI. Thus, the assumptions of the Bayesian model would be expected to favor a trait such as ADG. It is not immediately clear why this is not the case in this study, and further analysis with a larger data set will be necessary to verify this result. Estimates of variance components obtained from the 5 replicates of the training data are shown in Tables 6 and 7. Estimates obtained with the BayesB method were substantially greater than those obtained for RR-BLUP, and the proportion of the variance attributable to the SNP in BayesB was quite high. However, the correlations observed using both BayesB and RR-BLUP were less than those observed for the polygenic EBV (0.575, 0.504, and 0.602 for ADG, DMI, and RFI, respectively).
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Table 6. Estimates of variance components for ADG, DMI, and residual feed intake (RFI) obtained in the 5 replicates of the training data with the random regression BLUP (RR-BLUP) method
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Table 7. Estimates of variance components for ADG, DMI, and residual feed intake (RFI) obtained in the 5 replicates of the training data with the BayesB method
The admixed population of crossbred animals used in this analysis consisted of steers sired by bulls of various breeds. Accuracy of prediction within sire breed showed greater variation between breeds when using the RR-BLUP method that when using the BayesB method. There was also greater prediction accuracy within breed than across breed.
This pattern of greater within-breed accuracy with RR-BLUP was clearly different from that observed using BayesB, for which the within-breed correlations were closer to the across-breed estimates. A possible reason for this may be the possibility that SNP selected using RR-BLUP may trace breed differences (SNP are optimized to capture breed differences) such that the accuracy observed across breeds is confounded and not purely attributable to LD between SNP and underlying QTL.
Given that varying amounts of shrinkage are applied to SNP on the basis of differences in allele frequencies (the shrinkage term is the same for all SNP for the RR-BLUP method), any differences in allele frequencies between breeds for any locus will affect the size of the allele substitution effect and, by extension, the prediction accuracy. Habier et al. (2007) showed that for RR-BLUP, genetic relationships captured by the genetic markers affect prediction accuracy to a larger extent than in Bayesian methods because more markers are fit in the model. The consequence of this is that there would be an increase in prediction accuracy if validation animals became more related to training animals, especially if the markers were able to resolve relatedness more than the average relationship matrix.
(4)A key issue in genomic selection of RFI is the utility of GEBV in selecting unphenotyped animals. In this study, the accuracies obtained were low compared with those seen in studies using dairy breeds, for which more accurate phenotypes are used to train SNP. A framework that allows incorporation of EPD and GEBV into a single unit of merit after appropriate weighting will be useful. The weights used could be derived from the reliability of the polygenic EBV and the percentage of genetic variance accounted for by the marker panels (VanRaden, 2001; Dekkers, 2007; Cerón-Rojas et al., 2008; Moser et al., 2009). A model that uses BLUP (Kachman, 2008) has also been proposed. Such a combined index for selection seems to be the best option, especially for beef cattle until such a time when large populations of animals have been tested for feed intake and GEBV accuracies are greater than the EBV accuracies obtained using traditional BLUP evaluations.
The number of animals in the training set also has a bearing on the accuracy of GEBV (Hayes et al., 2009). For RFI, a need therefore exists for increased testing of feed intake, despite the cost associated with such an undertaking. This is a priority for several Canadian collaborations involving the Universities of Alberta and Guelph, Alberta Agriculture and Rural Development, and Agriculture and Agri-Food Canada.
Candidate Genes for RFI
Several studies have attempted to characterize the molecular basis of RFI. Barendse et al. (2007) and Sherman et al. (2008, 2010) describe a series of polymorphisms associated with RFI, but the usefulness of these SNP and associated genes in explaining the total RFI variance has yet to be determined. In this study, several SNP with a high detection frequency were in close proximity to genes that may be useful in controlling feed efficiency. Despite the fact that these SNP are associated with some genes of interest, their individual contribution was small. So far, no study involving RFI has shown a gene(s) with a significantly large effect, such that a candidate gene approach may not be the best strategy in characterizing the molecular basis of RFI. The SNP identified in this study may be more useful when seen as key elements of a gene network controlling RFI because the contribution of individual genes is likely to be small. Further research and analysis of gene networks for RFI is therefore warranted and is currently at an advanced stage in our laboratory.
In this study, accuracy of prediction, defined as the correlation between ADG, DMI, and RFI and trait-specific GEBV, was compared between SNP panels derived using 2 genomic selection methods, namely, BayesB and RR-BLUP. The RR-BLUP-derived GEBV achieved greater correlations with trait phenotypes, with accuracy being greatest for RFI. Differences in accuracy between sire breeds were observed with the RR-BLUP method. This may imply that significant differences may exist in SNP associated with RFI between the component breeds in the study population, and the SNP selected are consensus SNP that seem to be inadequate for some breeds that are part of the composite population used. The accuracies obtained for all 3 traits were low, signaling a need for continued feed intake testing to acquire a large number of phenotyped animals, which may aid in better selection of SNP markers to be used for prediction as well as the continued evaluation of whether an admixed population such as ours can be useful in providing an across-breed prediction panel for RFI.
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↵1 The study was made possible by grants awarded to Stephen Moore from the Canadian Cattleman’s Association (Calgary, Alberta, Canada), Alberta Agricultural Research Institute (Edmonton, Alberta, Canada), Alberta Beef Producers (Calgary, Alberta, Canada), Canada–Alberta Beef Industry Development Fund (Calgary Alberta, Canada), and the Beef Cattle Research Council (Calgary, Alberta, Canada). The authors thank Jason Grant (Department of Agriculture, Food and Nutritional Science, University of Alberta, Edmonton) for Perl programming. The authors also thank John Hickey and Bruce Tier (Animal Genetics and Breeding Unit, University of New England, Armidale, New South Wales, Australia) for providing the software (AlphaBayes) for genomic analysis.
Received July 27, 2010.
Accepted May 24, 2011.
This article is available free under the terms of the journal's open access po
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Created By: Avery Horne
The Economics of Food Insecurity in the United States
Brent Kreider and
+ Author Affiliations
Craig Gundersen is Professor in the Department of Agricultural and Consumer Economics, University of Illinois. Brent Kreider is Professor in the Department of Economics, Iowa State University. John Pepper is Associate Professor in the Department of Economics, University of Virginia.
↵*Correspondence may be sent to Email: firstname.lastname@example.org.
Received April 27, 2010.
Accepted July 11, 2011.
Food insecurity is experienced by millions of Americans and has increased dramatically in recent years. Due to its prevalence and many demonstrated negative health consequences, food insecurity is one of the most important nutrition-related public health issues in the U.S. In this article, we address three questions where economic insights and models have made important contributions: What are the determinants of food insecurity?; What are the causal effects of food insecurity on health outcomes?; and What is the impact of food assistance programs on food insecurity? We conclude with a discussion of the policy implications of the answers to these questions and future research opportunities in this research venue.
Supplemental Nutrition Assistance Program
Food Stamp Program
National School Lunch Program
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(1)Food insecurity is a serious challenge facing millions of Americans. In 2009, more than 50 million persons in the United States lived in households classified as food insecure, with over one-third of these households experiencing a more serious level of food insecurity termed “very low food security.” These rates have soared to unprecedented levels, having increased by more than one-third since 2007. The prevalence of food insecurity is of great concern to policy-makers and program administrators, a concern heightened by its many demonstrated negative health consequences. The alleviation of food insecurity is a central goal of the Supplemental Nutrition Assistance Program (SNAP, formerly known as the Food Stamp Program), the largest food assistance program in the United States (USDA, 1999).
Due in large part to food insecurity's status as one of the most important and high profile nutrition-related public health issues in the United States today, a vast body of literature has emerged on the topic. This literature has developed across several fields, with especially large contributions coming from the areas of nutrition and public health. In recent years, economists have made key contributions across two main dimensions. First, economists have provided a more cogent perspective on how resources and budget constraints impact food insecurity. Second, by highlighting and addressing key selection issues, economists have generated new insights for policy-makers, with an emphasis on identifying the causal impacts of food assistance programs on food insecurity. Of particular concern from a methodological perspective, households are not randomly assigned to food assistance programs, but instead choose whether to participate based in part on characteristics unobserved in the data, including their anticipated food insecurity status. If not carefully addressed, such endogenous selection issues can seriously compromise our understanding of the efficacy of such programs.
We begin with a description of how food insecurity is measured, followed by an overview of the extent of food insecurity in the United States. We then turn to three key questions where economists have made contributions:
(2)1 What are the determinants of food insecurity?After covering the central non-income determinants of food insecurity, we consider the role of economic shocks, the importance of assets, and the role of the macro-economy.
2 What are the causal effects of food insecurity on health outcomes? While there is intense concern about the direct implications of food insecurity (for example, that children may be skipping meals), increasing attention is also being paid to associated negative health outcomes. We review the existing literature along with a discussion of the identification problem that arises in the likely case where unobserved factors are associated with food insecurity and also associated with health outcomes. This issue is referred to as the endogeneity or selection problem, and if not addressed, limits the policy usefulness of these findings.
3 What is the impact of food assistance programs on food insecurity? A central objective of food assistance programs in the United States is to alleviate food insecurity. After an overview of such programs, we summarize the research on the effects of the largest program, SNAP, on food insecurity. We then discuss what has been learned about the effects of the National School Lunch Program (NSLP).
Economists have made contributions to the food insecurity literature for roughly a decade. This work has generated some concrete policy recommendations that we highlight in the conclusion. We follow up with several new areas of research in which economists' perspectives and methods have the potential to yield new, policy-relevant insights.
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Defining Food Insecurity
A series of questions designed to measure food insecurity debuted in the Current Population Survey in 1996. After some modifications, the official set of 18 questions used to measure food insecurity in the United States was established as the Core Food Security Module (CFSM). The measure is based on a set of 18 questions for households with children and a subset of 10 of these 18 questions for households without children. Some of the conditions people are asked about include: “I worried whether our food would run out before we got money to buy more,” (the least severe item);” Did you or the other adults in your household ever cut the size of your meals or skip meals because there wasn't enough money for food?”; “Were you ever hungry but did not eat because you couldn't afford enough food?”; and “Did a child in the household ever not eat for a full day because you couldn't afford enough food?” (the most severe item for households with children). A complete list of questions is provided in table 1.1
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Food insecurity questions in the Core Food Security Module
Each of the questions on the CFSM is qualified by the proviso that the conditions are due to financial constraints. As a consequence, persons who have reduced food intakes due to, say, fasting for religious purposes or dieting, should not respond affirmatively to these questions.
(3)Using the 18 questions, the USDA delineates households into food insecurity categories. The idea underlying the use of multiple questions is that no single question can accurately portray the concept of food insecurity. The number of affirmative responses is held to reflect the level of food hardship experienced by the family. Based on the number of affirmative responses, the following thresholds are established: (a) food security (defined as cases in which all household members had access at all times to enough food for an active, healthy life); (b) low food security (cases in which at least some household members were uncertain of having, or unable to acquire, enough food because they had insufficient money and other resources for food); and (c) very low food security (cases in which one or more household members were hungry, at least some time during the year, because they couldn't afford enough food).2
Categories (b) and (c) are often combined into the category of “food insecure.” Households responding affirmatively to two or fewer questions are classified as food secure, those responding affirmatively to three to seven questions are classified as low food secure (three to five questions for households without children), and those responding affirmatively to eight or more questions are classified as very low food secure (six or more for households without children). Consistent with the language employed in the literature, a household responding affirmatively to three or more questions is identified as food insecure. One should note that all households defined as very low food secure are also food insecure, but the converse is not true.
Two other sets of food security categories have been established by researchers. The first is “marginal food insecure”, which includes all households that respond affirmatively to one or more of the questions. This is in contrast to the usual definition of food security described above, whereby households responding affirmatively to one or two questions are defined as food secure. One justification for this measure is that marginally food insecure households often appear more similar to food insecure households with respect to health outcomes and other characteristics (for example, income) than to food secure households further from the margin.
The second set of food insecurity questions is defined with respect to children in a household. As a consequence, only the eight child-specific questions (that is, those of the set of 18 questions that refer to the children in the household) are used. Under this set, a household is said to be “child food insecure” if two or more questions are answered affirmatively and “very low child food secure” if five or more questions are answered affirmatively. (For a discussion of the child food insecurity measures see, for example, Nord and Hopwood, 2007.)
In this review, we concentrate on binary measures of food insecurity, since nearly all research has focused on such indicators (for example, food insecure versus food secure). These comparisons are clear and straightforward, and they are relatively easy to implement in treatment effect models. Still, considerable information is being suppressed in such cases. In particular, information is not being utilized when broad categories are created from the 18 questions on the CFSM. Consider, for example, two households with one responding affirmatively to eight questions and the other responding affirmatively to 18 questions. Both households are classified as very low food secure yet, arguably, the latter household has a higher level of food insecurity. In response, a series of food insecurity measures based on the Foster Greer Thorbecke class of poverty measures was developed in Dutta and Gundersen (2007) and applied empirically in, e.g., Gundersen (2008).3 While we focus on binary indicators in this review, we encourage researchers to consider more refined measures when feasible.
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The Extent of Food Insecurity
In this section, we describe food insecurity trends for the United States from 2001 to 2009 based on the most recent available data from the CPS. Specifically, these data come from the 2001-2009 December supplements, a monthly survey of approximately 50,000 households. The CPS represents the official data source for official poverty and unemployment rates, and official food insecurity rates for the United States are calculated using the CFSM component. The CFSM has been included in the CPS in at least one month every year since 1995.
Figure 1 displays the proportion of all persons living in households that are (a) food insecure and (b) very low food secure. As discussed above, those who are very low food secure comprise a subset of the food insecure group. The figures only use data since 2001 to avoid issues of seasonality and changes in the screening questions.4 From 2001 to 2007, the food insecurity rate remained relatively steady at about 12%, with very low food security rates ranging from 3-4%. These rates increased dramatically in 2008. For the food insecurity category, there was an almost 35% increase (from 12.2% to 16.4%), and for the very low food security category, rates rose by almost 50% (from 4.0% to 5.8%). These unprecedented increases probably reflect the economic recession during this time period. The rates continued to stay high in 2009, increasing slightly for both categories.
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Food insecurity in the United States, 2001-2009
In figure 2, the results for the proportions of children living in food insecure households, food insecure children, and the very low food secure children are displayed. As in figure 1, the rates remained relatively static from 2001 to 2007. The proportion of children in food insecure households ranged from 16.9% to 19.0%, the proportion of food insecure children from 9.1% to 10.7%, and the proportion of very low food secure children was always under 1%. Consistent with what occurred for the full population, in 2008 there were increases of over 30% in children living in food insecure households and food insecure children, and an over 60% increase in the number of very low food secure children. The levels remained high in 2009.
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Food insecurity among children in the United States, 2001-2009
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The determinants of food insecurity
The literature has established socioeconomic and demographic factors associated with food insecurity in the United States. For example, as seen in Nord et al. (2010), households headed by an African American, Hispanic, a never married person, a divorced or separated person, a renter, younger persons, and less educated persons are all more likely to be food insecure than their respective counterparts. In addition, households with children are more likely to be food insecure than households without children. Research using multivariate methods has generally found that, even after controlling for other factors, these characteristics are either positively associated with food insecurity or are statistically insignificant. This general set of findings holds whether the sample is all households, households with children, or households without children.5 These findings have used data from each of the nationally representative data sets, which include the CFSM (or the full or portions of the six-item scale), namely the CPS, Panel Study of Income Dynamics (PSID), the Early Childhood Longitudinal Study – Birth Cohort (ECLS-B), the Early Childhood Longitudinal Study – Kindergarten Cohort (ECLS-K), the Survey of Income and Program Participation (SIPP), the Three City Study (TCS), and the National Health and Nutrition Examination Survey (NHANES). Along with these datasets, a series of other smaller-scale datasets that are based on limited geographic areas have been used in these studies.
(4)Along with all these factors, perhaps the most important are the resources available to a household; this is the research area where economists have especially advanced the food insecurity literature. The relationship between food insecurity and income (normalized by the poverty line) can be found in figure 3. (This is a nonparametric representation with a bandwidth of 0.6. See Fox (2000) for details on the estimation methods.) The figure is based on all observations in the 2009 December Supplement of the CPS with incomes between 0 and 400% of the poverty line. We emphasize three things from this figure. First, the probability of food insecurity declines with income and the decline is more marked for food insecurity and marginal food insecurity than for very low food security. Second, that poverty is not synonymous with food insecurity is reflected in the high proportions of households that are food secure and poor. For example, about 65% of households close to the poverty line are food secure. Third, conversely, a non-trivial portion of households with incomes above the poverty line are food insecure: as the income-to-poverty ratio approaches two, food insecurity rates are slightly over 20%, and, even as the ratio approaches three, food insecurity rates hover around 10%.
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Relationship between food insecurity and income, 2009
The inverse relationship between income and food insecurity is not surprising. What is surprising, perhaps, is the large number of poor households that are food secure and the large number of non-poor households that are food insecure.
One conjecture for why these households are food insecure is that current income (that is, what is observed in datasets like the CPS) does not adequately portray the ability of families to avoid food insecurity. Using a sample of households from the Survey of Income and Program Participation with current incomes below 200% of the poverty line taken, Gundersen and Gruber (2001) find that average household income over a two-year period is a better predictor of whether a household is food insecure than current income. In addition, they found that households without any liquid assets are substantially more likely to be food insecure than those with liquid assets. Using a larger number of years and combining information from the SIPP with the Survey of Program Dynamics (SPD), Ribar and Hamrick (2003) analyzed the dynamics of poverty and food insecurity. These authors found that assets were protective against food insecurity for poor households and that income volatility is associated with food insecurity. Finally, using data from the 2001 SIPP, Leete and Bania (2010) demonstrate that liquidity-constrained households are more likely to be food insecure than unconstrained households. They also found that negative income shocks, but not positive income shocks, lead to increased probabilities of food insecurity.
Using food insecurity (FI) data aggregated to the state level, Gundersen, Engelhard, Brown, and Waxman (2011) use the following model to examine the role of economic factors beyond poverty status: Formula (1) where s is a state, t is year, UN is the unemployment rate, POV is the poverty rate, MI is median income, HISP is the percentage Hispanic, BLACK is the percentage African-American, μt is a year fixed effect, υs is a state fixed effect, and εst is an error term. Estimating equation (1) with data from combined cross sections from the 2001-2009 CPS, the authors find that the elasticity of the food insecurity rate with respect to the unemployment rate is greater than the elasticity with respect to the poverty rate. Since many unemployed persons are not poor, this is further evidence of why information beyond poverty status is relevant for understanding food insecurity. Earlier work looking at state-level determinants using different methods and a shorter time horizon includes Bartfeld and Dunifon, 2006.
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Consequences of food insecurity
(5)The consequences of food insecurity are numerous and occur across the age spectrum. An extensive body of literature that has focused on correlation rather than casual relationships has found that food insecurity is associated with a wide range of health outcomes. Food insecurity during pregnancy is associated with higher risks of some birth defects (Carmichael et al., 2007), and households suffering from food insecurity are more likely to have children who suffer from anemia (Eicher-Miller et al., 2009; Skaliky et al. 2006), lower nutrient intakes (Cook et al., 2004), greater cognitive problems (Howard, 2011), higher levels of aggression and anxiety (Whitaker et al., 2006), higher probabilities of being hospitalized (Cook et al., 2006), poorer general health (Cook et al., 2006), higher probabilities of dysthymia and other mental health issues (Alaimo, Olson, and Frongillo, 2002), higher probabilities of asthma (Kirpatrick et al., 2010), higher probabilities of behavioral problems (Huang, Matta Oshima, and Kim, 2010), and more instances of oral health problems (Muirhead et al., 2009). Households suffering from food insecurity are more likely to have adults who have lower nutrient intakes (Kirkpatrick and Tarasuk, 2007; McIntyre et al., 2003), greater probabilities of mental health problems (Heflin, Siefert, and Williams, 2005), long-term physical health problems (Tarasuk, 2001), higher levels of depression (Whitaker et al., 2006), diabetes (Seligman et al., 2007), higher levels of chronic disease (Seligman et al., 2009), and lower scores on physical and mental health exams (Stuff et al., 2004). Food insecure seniors have lower nutrient intakes (Lee and Frongillo, 2001a; Ziliak, Gundersen, and Haist, 2008), are more likely to be in poor or fair health (Lee and Frongillo, 2001a; Ziliak, Gundersen, and Haist, 2008), and are more likely to have limitations in activities of daily living (ADL) (Ziliak, Gundersen, and Haist, 2008). Some studies, however, have emphasized that food insecurity is not always associated with poor health outcomes. For example, Bhattacharya, Currie, and Haider (2004) find that while poverty is associated with nutritional outcomes, food insecurity is not. As another example, Gundersen and Ribar (forthcoming) find that while subjective measures of additional food expenditures required to be food secure are associated with food insecurity, self-reports of food expenditures are not.
As noted above, this literature has focused on examining whether food insecurity is correlated with a range of health-related outcomes. While food insecurity may cause health problems, there may also exist unobserved factors that jointly influence whether a person is food insecure and in poor health. Thus, any observed relationships between food insecurity and poor health could be spurious. A selection problem results from the fact that the data alone cannot reveal the health outcomes of a person who is food insecure if instead that person were to have not been food insecure.
To formalize this identification problem, consider drawing inferences on the average treatment effect: Formula (2) where H(FI=1) denotes a “bad health” outcome if a person were to be in a food insecure household, and H(FI=0) denotes a “bad health” outcome if a person were to be in a food secure household. Thus, the average treatment effect reveals how the probability of a bad health outcome would differ if all persons were food insecure, P[H(FI=1)], versus the probability if all persons were food secure. Even if FI were accurately observed (that is, there is construct validity such that there wereno conceptual or practical measurement issues), the outcome H(FI=1) is counterfactual for all persons who were food secure. Similarly, the outcome H(FI=0) is counterfactual for all persons who were food insecure. A statistical “selection” problem arises in that households become food secure or food insecure based in part on factors unobserved to the researcher. As a consequence, the mean health outcomes of the currently food insecure, should they become food secure, may not reflect the mean health outcomes of the currently food secure.
To address this selection issue, Gundersen and Kreider (2009) estimate equation (2) for children health outcomes through the use of three assumptions. The first is the Monotone Treatment Response (MTR) assumption (Manski, 1997). In the present context, MTR requires that a child's health status would not decline by becoming food secure. This seems relatively innocuous for most health outcomes in that most scenarios in which becoming food secure leads to worse health outcomes do not seem plausible.6 The second is the Monotone Treatment Selection (MTS) assumption (Manski and Pepper, 2000). This assumption has been used to place restrictions on the selection mechanism through which children become food secure or insecure. Under the MTS assumption, food insecure children under the status quo would tend to remain less healthy than their food secure counterparts under the status quo under a policy that made all households food secure or all households food insecure. This assumption is based on well-established documentation that food insecure children are disadvantaged over other dimensions (for example, they have lower incomes), and these disadvantages are associated with worse health outcomes (see, for example, Currie, Shields, and Wheatley Price, 2007).
Third, as in Kreider, Pepper, Gundersen, and Jolliffe (2011), they impose a Monotone Instrumental Variable (MIV) assumption introduced by Manski and Pepper (2000). In this context, children residing in lower-income households, on average, are assumed to have no better health outcomes than children residing in higher-income households. The MIV assumption is weaker than the standard mean independence instrumental variable (IV) assumption in that the MIV assumption does not imply any exclusion restriction. In particular, the mean health outcome is allowed to vary (monotonically) with income through avenues distinct from the impact of income on food security. The difficulty in finding credible standard instruments for food insecurity status that satisfy mean independence makes the MIV assumption an appealing alternative.
Data from the 2001-2006 NHANES, when analyzed under the unrealistic assumption of exogenous selection into food insecurity, indicate that children who are food insecure are 6.1 percentage points less likely to be in good, very good, or excellent health than those who are food secure.7 When these three assumptions above are imposed, Gundersen and Kreider (2009) find that food insecure children are between 1.4 percentage points and 3.5 percentage points less likely to be in good or better health.
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Alleviating food insecurity
The United States has a wide variety of food assistance programs designed to address numerous health and nutrition outcomes. One outcome in particular is food insecurity. A great deal of work by economists has examined the impact of SNAP on food insecurity; we review this work after providing some background on the program. We then review some recent work that has examined the impact of the second largest food assistance program, the National School Lunch Program (NSLP).
Background on the Supplemental Nutrition Assistance Program
The Supplemental Nutrition Assistance Program (SNAP, formerly known as the Food Stamp Program) is by far the largest U.S. food assistance program. Participants receive benefits for the purchase of food in authorized retail food outlets. Benefits are distributed via an Electronic Benefit Transfer (EBT) card. Recipients can use these benefits at approved retail food outlets. The level of benefits received by a household is determined by income level and family size. SNAP, with a few exceptions, is available to all families and individuals who meet income and, in some states, asset tests.
The program is large, both in terms of benefit size and in number of people served. In 2010, the average monthly benefit was $288/month for a family of four, with the maximum benefit for a family of this size being $668. These benefits can represent a substantial component of low-income households' total income. In terms of number of people served, the program reached about 40.3 million individuals in each month in 2010, with an annual benefit distribution of about $68.3 billion. A recent study demonstrated that almost half of all American children will have resided in a household that received food stamps by the time they reach 20 years of age (Rank and Hirschl, 2009).
To receive SNAP, households must meet a gross-income test, a net-income test, and, in about 20% of states, an asset test. In the majority of cases, the eligibility criteria are as follows. First, a household's gross income before taxes in the previous month cannot exceed 130% of the poverty line. Second, net monthly income must be below the poverty line. Net income is calculated by subtracting a standard deduction from a household's gross income. In addition to this standard deduction, households with labor earnings deduct 20% of those earnings from their gross income. Deductions are also taken for child care and/or care for disabled dependents, medical expenses, and excessive shelter expenses. Third, the federal guidelines stipulate that assets must be less than $2,000. In many states, though, the gross income criterion is higher than 130% of the poverty line (up to 200% in some cases) and even more states waive the asset criteria.
The amount of SNAP benefits received depends on net income. Households with a net income of zero receive the maximum benefit. As noted above, for a family of four in 2011, this amounted to $668. As income increases, the benefit declines: for every additional dollar of income, the amount of SNAP benefits is reduced by 30 cents (except income that comes in the form of earnings, in which case the reduction is 24 cents).
Despite the potentially large benefit levels, a large fraction of households eligible for SNAP do not participate. The most recently calculated food stamp participation data show that about 67% of eligible people in the United States received SNAP in 2008 (USDA, 2009). The decision to not participate is often ascribed to three main factors. First, there may be stigma associated with receiving SNAP, ranging from a person's own distaste for receiving food stamps to the fear of disapproval from others when redeeming food stamps, to the possible negative reaction of caseworkers (Ranneyand Kushman, 1987; Moffitt, 1983). Recent initiatives such as fingerprinting can also increase the stigma associated with SNAP participation. Second, transaction costs can diminish the attractiveness of SNAP participation. Examples of such costs include travel time to a SNAP office and time spent in the office, the burden of transporting children to the office or paying for childcare services, and the direct costs of transportation. A household faces these costs on a repeated basis when it must recertify its eligibility. Information costs – including overcoming language barriers and gaining understanding about the validity of immigration consequences – are included under transaction costs. Third, the benefit level can be quite small–for some families as low as $17 a month.
Effect of SNAP on Food Insecurity
As noted above, the central goal of SNAP is the reduction in food insecurity. Of concern, then, is that rates of food insecurity among recipients are about double the rates among eligible non-recipients (Nord et al., 2010), and these higher rates remain even after controlling for observed factors (for example, Gundersen, Jolliffe, and Tiehen, 2009). This is a counterintuitive result, both from a theoretical standpoint (it is difficult to see how shifting out the budget constraint can lead to an increase in food insecurity), from an empirical standpoint (see figure 3 above), and from the perspective of what policy-makers and program administrators expect the program to do.
Not surprisingly, economists have suggested that participation in SNAP is likely to be endogenous, arguing that SNAP recipients are likely to differ from non-recipients across unobserved factors. In response, a series of papers have considered this selection effect. The first paper on this topic was Gundersen and Oliveira (2001). Using data from the Survey of Income and Program Participation (SIPP) and a simultaneous equation model, they found that SNAP participants were no more likely to be food insecure than non-participants once they control for selection into SNAP and selection into food insecurity. As instruments for SNAP participation, Gundersen and Oliveira used imputed information on recipient's perceptions of stigma associated with SNAP receipt. Another identification approach exploits variation over time and across states in the implementation of restrictions on the eligibility of immigrants for SNAP. Such restrictions were put into place through the Personal Responsibility and Work Opportunity Reconciliation Act (PRWORA) of 1996. This work (for example, Van Hook and Balistreri, 2006) has generally found that immigrants not facing restrictions on SNAP participation were less likely to be food insecure than immigrants facing restrictions. Van Hook and Ballistreri (2006) therefore conclude that SNAP leads to reductions in food insecurity for the general population as well. Other work has also exploited variation in SNAP policy. A recent example is Nord and Prell (2011), who showed that the temporary increase in SNAP benefits due to the passage of the American Recovery and Reinvestment Act (ARRA) of 2009 led to reductions in food insecurity among those in the SNAP-eligible population, but not among the SNAP-ineligible population.8
This previous work addressed selection issues in the context of SNAP. Non-random classification errors in SNAP participation can also confound identification of the program's effects. SNAP participation is systematically under-reported in major surveys, with errors of omission (that is, responding that one doesn't receive SNAP when one really does) being substantially more likely than errors of commission (Bollinger and David, 1997; 2001). As a result, a positive estimated sign of the effect of SNAP on food insecurity should be viewed as valid only if the researcher is willing to place a great deal of confidence in the reporting of SNAP participation within the dataset being used. As shown in Gundersen and Kreider (2008), even when one imposes strong assumptions restricting the patterns of classification errors, SNAP participation error rates much smaller than 12% (a lower bound on the extent of SNAP misreporting derived from the literature) are sufficient to prevent one from being able to draw firm conclusions about the relationship between SNAP participation and food insecurity.
Background on the National School Lunch Program
(6)The National School Lunch Program (NSLP) is a federally-assisted meal program that operates in over 101,000 public and non-profit private schools and residential child care institutions, and provides nutritionally-balanced, low-cost, or free lunches to children each school day. In 2010, over 31 million students participated in NSLP. Of these, over half received free lunches and about one-tenth received reduced price lunches. In addition to any commodities the schools received, cash payments to schools for the NSLP in 2010 exceeded $10 billion.
Generally, public or non-profit private schools and residential childcare institutions may participate in the NSLP. School districts and independent schools that choose to participate in the lunch program receive cash subsidies and donated commodities from the USDA for each meal they serve. In return, the districts must serve lunches that meet federal requirements: providing no more than 30% of a student's calories from fat, less than 10% from saturated fat, and at least one-third of the Recommended Dietary Allowances of protein, vitamin A, vitamin C, iron, calcium, and calories. School districts must offer free or reduced-price lunches to eligible children. In addition, school food authorities can also be reimbursed for snacks served to children through the age of 18 years in after-school educational or enrichment programs.
Eligibility for the NSLP begins at the individual level. Any child at a participating school may purchase a meal through the NSLP. Children who are home-schooled or no longer attend school are not eligible. Among children in these schools, families with incomes at or below 130% of the poverty level are eligible for free meals. Children living in a household with an income between 130% and 185% of the poverty level are eligible for reduced-price meals, which are not allowed to cost more than 40 cents.
Effect of NSLP on Food Insecurity
Relatively few studies examine the impact of the NSLP on food insecurity. Nord and Kantor (2006), for example, provide indirect evidence of the importance of NSLP in alleviating food insecurity. A central difference between the summer and the rest of the school year is that children do not participate in school meal programs. Prior to 2001, the month in which the CFSM was placed in the CPS varied from year to year. Using this variation, they established that food insecurity rates are higher for school-aged children during the summer months.
Gundersen, Kreider, and Pepper (forthcoming) directly estimate the effect of NSLP. Like SNAP, food insecurity rates are substantially higher among participants than among nonparticipants – 39.9% versus 26.3%.9 Also like SNAP, it seems implausible that providing children an extra meal each day would lead to higher probabilities of food insecurity. Assessing the true effectof NSLP is made difficult, however, due to two fundamental identification problems. First, children receiving free or reduced-price meals are likely to differ from eligible non-participants in ways that are not observed in the data. Second, the association between participation in the NSLP and food insecurity may be, at least partly, an artifact of household misreporting of program participation. Meyer, Mok, and Sullivan (2009), for example, find evidence of aggregate underreporting rates of 45% in the CPS and 27% in the PSID.
The authors impose the MTS, MTR, and MIV assumptions to address the selection problem. The MTS formalizes the notion that the unobserved factors positively associated with participation in the NSLP are also positively associated with food insecurity, while MTR posits that receiving NSLP cannot increase the probability of food insecurity.10 An Income MIV assumption posits that children residing in higher income households have no higher probabilities of food insecurity than children residing in lower income households. An Ineligible Comparison Group MIV assumption posits that: (a) income-ineligible children have no higher probabilities of food insecurity than income-eligible children; (b) children attending schools without a school lunch program (which tend to be private, well-off schools) have no higher probabilities of food insecurity than children attending schools with a school lunch program; and (c) children who have dropped out of school (and, hence, cannot participate in the NSLP) have at least as high probabilities of food insecurity. The Ineligible Comparison Group MIV approach is conceptually similar to the regression discontinuity approaches that have been used elsewhere in evaluations of school meal programs (for example, Schanzenbach, 2009; Bhattacharya, Currie, and Haider, 2006; Gleason and Suitor, 2003), albeit not for analyses of the effect of these programs on food insecurity.
To address the problem of misreporting NSLP participation, the authors impose restrictions on the extent of reporting error using information on the difference between the self-reported participation rate and estimated true participation rate. This method, which was developed in Kreider, Pepper, Gundersen, and Jolliffe (2011), uses auxiliary administrative data on the size of the NSLP caseload to restrict the magnitudes and patterns of NSLP reporting errors. The true participation rate is calculated by taking the ratio of the number of children receiving NSLP as reported in administrative data to the number of eligible children in the relevant age ranges in the NHANES. The latter is calculated by taking the ratio of the number of children receiving NSLP as reported in the NHANES to the number of eligible children in the relevant age ranges in the NHANES. Combining the classification error restrictions with the MTS, MTR, and MIV monotonicity assumptions, the authors find that the NSLP alleviates food insecurity. If the data are treated as perfectly accurate, the program is estimated to decrease the prevalence of food insecurity between 2.3 and 9.0 percentage points. In the presence of participation classification errors, the estimated impacts range from 3.2 to 15.8 percentage point declines. Thus, after controlling for selection and measurement error problems, Gundersen, Kreider, and Pepper (forthcoming) find persuasive evidence that the NSLP leads to substantial reductions in food insecurity.
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The extensive literature on food insecurity in the United States has given policy-makers and program administrators numerous insights into the causes and consequences of food insecurity, and some approaches that seem to be effective in alleviating food insecurity. Here, we emphasize four key insights that can be drawn from this literature. First, there is a small but growing body of evidence that the Supplemental Nutrition Assistance Program reduces the prevalence of food insecurity. This should be kept in mind as reconstructions of SNAP are being proposed. In particular, some have proposed changes to the structure of SNAP with respect to what types of food should be available for purchase. While these proposals have the goal of enhancing nutrition among SNAP participants, the effectiveness of the program on the whole could be compromised if more restricted food options discourage participation and lead to subsequent increases in food insecurity. Since SNAP has an explicit goal of alleviating food insecurity and is considered the leading program in the fight against hunger, proposals to modify the program should carefully consider the possibility of unintended consequences.
Second, recent evidence suggests that the National School Lunch Program may also reduce food insecurity, even though it is not explicitly designed for that purpose. While the program focuses on specific nutritional objectives, policy-makers contemplating proposals to modify the program should keep in mind its potential to alleviate food insecurity. As for the case of SNAP, proposals to modify the NSLP should consider the possibility that changes in the program could have the unintended effect of increasing the prevalence of food insecurity by discouraging participation in the program. As always, policy-makers should carefully weigh all anticipated benefits and costs.
Third, the negative health outcomes associated with food insecurity have been well-established. Alongside the direct benefits associated with reducing food insecurity (for example, as a society, we may wish to avoid having children go to bed hungry due to economic constraints), potential reductions in medical expenditures should be incorporated into relevant benefit-cost considerations of programs like SNAP and NSLP.
Fourth, millions of food insecure households in the United States have sufficiently high incomes to render them ineligible for food assistance programs. Research findings regarding the role of assets and income shocks can provide some guidance for what types of policies might most effectively impact food insecurity among middle-income households.
While the existing literature has provided a solid foundation for policy-makers and program administrators, there are numerous avenues for future research. We concentrate on several questions we believe to be especially well-suited to economic analyses:
How is food insecurity distributed within a household? As discussed above, food insecurity measures are generally defined at the household level rather than for each individual in the household.11 At least based on evidence derived from studies of intra-household allocation developing countries, there are likely to be differences in the distribution of food insecurity within households (see, for example, Hadley, Lindstrom, Tessema, and Belachew, 2008; Kuku, Gundersen, and Garasky, 2011). At a minimum, these differences are apparent in the aggregate, where food insecurity rates among children in a household are observed to be substantially lower than food insecurity rates for households with children (see figure 2). Some recent work has utilized measures that include questions about food insecurity specifically for children (Framm et al., 2011; Connell, Lofton, Yadrick, and Rehner, 2005). Child-specific responses can lead to new insights into how families distribute food security status or, at the very least, how individuals within a household perceive this distribution.
How do food prices influence food insecurity? As discussed above, analyses of the determinants of food insecurity have generally concentrated on factors defined at the individual or household level. With the exception of analyses studying the role of social capital (for example, Martin, Rogers, Cook, and Joseph, 2004) and the effects of broader macroeconomic conditions discussed above, there appears to be a gap in the literature regarding the environment facing low-income consumers. In particular, there appears to be no research on the effects of food prices. In the development economics literature, there has been extensive research on the influence of food prices on well-being (see, for example, Ivanic and Martin, 2008). While the proportion of total expenditures spent on food among low-income Americans is substantially lower, on average, than in developing countries, food prices may still make a significant difference. There exists an enormous amount of variation in food prices across the United States. (For a description of this variation at the county level, see the maps at http://feedingamerica.org/hunger-in-america/hunger-studies/map-the-meal-gap.aspx.). A report by Feeding America (2011) shows at least some correlation between food prices and food insecurity at a county level: 44 counties in the United States are in the top 10% of food prices and food insecurity rates. In addition, research by, e.g., Beatty (2010) and Broda, Leibtag, and Weinstein (2009) has found that food prices have an influence on the well-being of low-income consumers in developed countries. Based on the Feeding America report, and work done in other areas, future work may wish to more fully consider the effects of food prices over time and across areas on food insecurity.
How does food access influence food insecurity? The ability to purchase sufficient amounts of food to avoid food insecurity depends on the prices faced by consumers. Prices faced by consumers, though, do not take into consideration the transactions costs consumers may incur in obtaining food. As summarized in Bitler and Haider (2010), there is some evidence that so-called “food deserts” (that is, geographic areas with more limited numbers of food outlets) may influence the food consumption patterns of low-income households. The extent to which they matter is an open question. It is perhaps worth exploring the impact of the availability of food on food insecurity status, net of food prices and other factors. The effects of food deserts may be especially significant for three groups that may face mobility restrictions and/or live in remote areas: seniors, persons with disabilities, and American Indians living on reservations.
What types of coping mechanisms do low-income foodsecure families utilize, and what are the effects of these mechanisms? As seen in figure 3, a large proportion of poor households are able to avoid food insecurity and even avoid marginal food insecurity. Similarly, a large proportion of those households with incomes below 50% of the poverty line are able to avoid food insecurity. The construction of the poverty line in the United States is such that the presumption is that income-poor households will have to forego at least some necessities. In other words, to be food secure, they may be deprived in some other dimension of well-being. Two main issues could be explored in this context. The first is with respect to what commodities food secure families are giving up to be food secure. For example, seniors may be foregoing prescription drugs to feed themselves and other members of the household. In such contexts, food security combined with poverty should signal to policy-makers and program administrators that assistance may be needed; in other words, food security does not indicate an absence of need.12 The second issue regards the coping strategies used by food secure families. These coping strategies can, in essence, lower the probability of being food insecure at any given income level. There has been some qualitative work based on small-scale datasets that illuminates how low-income families maintain food security (see, for example, Olson et al., 2004; Swanson, Olson, Miller, Lawrence, 2008). Conducting similar research using a broader sample with an economic lens could provide further insights into the effectiveness of various coping mechanisms. A related and important question is whether these coping mechanisms have unintended effects on health and well-being. For example, families concerned about the possible onset of food insecurity might cope by purchasing storable, high-calorie foods that are potentially associated with increased weight. As another example, a family might engage in illegal activities to avoid food insecurity.
Besides SNAP and NSLP, what are the effects of social safety net programs on food insecurity?As discussed above, recent evidence suggests that SNAP and NSLP lead to reductions in food insecurity. Less understood is how other food assistance programs – especially WIC and the School Breakfast Program (SBP)– affect food insecurity. While these programs are significantly smaller than SNAP, their impacts could be comparable per recipient. There has also not been much research on the effects of other social safety net programs such as unemployment insurance and in-kind programs such as Medicaid and housing assistance.13
How do the experiences of food insecurity in other countries differ from the United States? To date, the vast majority of studies on food insecurity in developed countries have concentrated on the United States. The only other country with multiple studies of which we are aware is Canada (in addition to the papers cited above, see, for example, Kirkpatrick and Tarasuk, 2011; McIntyre, Connor, and Warren, 2000.) Given the differences across countries in demographics, food prices, geography, assistance programs, etc., cross-country comparison may yield new insights akin to the new insights that have been drawn from cross-country comparisons of poverty (for example, Rainwater and Smeeding, 2003). The possibility of engaging in these cross-country comparisons is enabled by the increasing usage of either the full CFSM, or questions taken from the CFSM on nationally-representative surveys in other countries.
How do health limitations affect food insecurity? In the main, the literature on the effects of food insecurity on health outcomes has implicitly assumed that food insecurity has an influence on health outcomes, rather than the other way around. In some instances, this assumption seems valid. For example, it is not obvious how nutrient intakes would affect food insecurity. In other cases, this assumption may be untenable. For example, one would anticipate that ADL limitations lead to food insecurity rather than the other way around. (Work that does consider reverse causation includes Lee and Frongillo (2001b) and Casey, Goolsby, Berkowitz, et al., (2004).) Causality might often run in both directions. For example, the limited food intakes associated with food insecurity could lead to diabetes, while having diabetes and its concordant medical costs might make someone more likely to be food insecure. Research on the impact of health care limitations on food insecurity would be of interest, especially when the causal direction is mixed, both in terms of improved estimates of the impact of food insecurity and in terms of further delineating the causes of food insecurity.
What are the effects of private food assistance programs on food insecurity?Alongside public food assistance programs like SNAP, there is a substantial private food assistance network in the United States. This network is overseen by Feeding America, which is comprised of 202 food banks (approximately 80% of all the food banks in the United States) and the tens of thousands of agencies they serve. These food banks receive food directly from major food companies, grocery stores, restaurants, commodity exchanges, individual donors, and food purchased with donations. Food is distributed through emergency food pantries that distribute non-prepared foods and other grocery products, emergency soup kitchens that provide prepared meals and are served on-site, and emergency shelters that provide residential shelter on a short-term basis and serve one or more meals per day. The Feeding America system served an estimated 37 million people in 2009 (Mabli, Cohen, Potter, & Zhao, 2010). Given the size of this program, research on the impact of these private food assistance programs on food insecurity would be of interest, especially to donors to these programs. Such research could further consider how these programs interact with public food assistance programs.
Research on food insecurity will play an important role for policy-makers and program administrators in the United States for many years. Food insecurity rates are likely to remain high for some time, and the consequences of food insecurity will concurrently remain. Moreover, substantial public and private sector resources are aimed at reducing the incidence of food insecurity. Thus, there is a critical need for credible research into the causes and consequences of food insecurity and the efficacy of different approaches for alleviating food insecurity. As reviewed in this paper, we believe that this relatively new but growing body of literature has begun to answer many key questions, and economists have played several important roles in developing this literature. There is also much more to learn. The research we have reviewed in this paper and the further questions we have posed demonstrate the many ways economists will continue to inform our understanding of the food insecurity landscape and the myriad efforts to address the problem and its consequences.
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Organic foods: Are they safer? More nutritious?
Discover the real difference between organic foods and their traditionally grown counterparts when it comes to nutrition, safety and price.
By Mayo Clinic staff
(1)Once found only in health food stores, organic food is now a regular feature at most supermarkets. And that's created a bit of a dilemma in the produce aisle. On one hand, you have a conventionally grown apple. On the other, you have one that's organic. Both apples are firm, shiny and red. Both provide vitamins and fiber, and both are free of fat, sodium and cholesterol. Which should you choose?
Conventionally grown produce generally costs less, but is organic food safer or more nutritious? Get the facts before you shop.
Conventional vs. organic farming
The word "organic" refers to the way farmers grow and process agricultural products, such as fruits, vegetables, grains, dairy products and meat. Organic farming practices are designed to encourage soil and water conservation and reduce pollution. Farmers who grow organic produce and meat don't use conventional methods to fertilize, control weeds or prevent livestock disease. For example, rather than using chemical weedkillers, organic farmers may conduct more sophisticated crop rotations and spread mulch or manure to keep weeds at bay.
Here are some key differences between conventional farming and organic farming:
Apply chemical fertilizers to promote plant growth. Apply natural fertilizers, such as manure or compost, to feed soil and plants.
Spray insecticides to reduce pests and disease. Use beneficial insects and birds, mating disruption or traps to reduce pests and disease.
Use herbicides to manage weeds. Rotate crops, till, hand weed or mulch to manage weeds.
Give animals antibiotics, growth hormones and medications to prevent disease and spur growth. Give animals organic feed and allow them access to the outdoors. Use preventive measures — such as rotational grazing, a balanced diet and clean housing — to help minimize disease.
Organic or not? Check the label
The U.S. Department of Agriculture (USDA) has established an organic certification program that requires all organic foods to meet strict government standards. These standards regulate how such foods are grown, handled and processed.
(2)Any product labeled as organic must be USDA certified. Only producers who sell less than $5,000 a year in organic foods are exempt from this certification; however, they're still required to follow the USDA's standards for organic foods.
If a food bears a USDA Organic label, it means it's produced and processed according to the USDA standards. The seal is voluntary, but many organic producers use it.
Illustration of the USDA organic seal
Products certified 95 percent or more organic display this USDA seal.
Products that are completely organic — such as fruits, vegetables, eggs or other single-ingredient foods — are labeled 100 percent organic and can carry the USDA seal.
Foods that have more than one ingredient, such as breakfast cereal, can use the USDA organic seal plus the following wording, depending on the number of organic ingredients:
(3)100 percent organic. To use this phrase, products must be either completely organic or made of all organic ingredients.
Organic. Products must be at least 95 percent organic to use this term.
Products that contain at least 70 percent organic ingredients may say "made with organic ingredients" on the label, but may not use the seal. Foods containing less than 70 percent organic ingredients can't use the seal or the word "organic" on their product labels. They can include the organic items in their ingredient list, however.
Do 'organic' and 'natural' mean the same thing?
No, "natural" and "organic" are not interchangeable terms. You may see "natural" and other terms such as "all natural," "free-range" or "hormone-free" on food labels. These descriptions must be truthful, but don't confuse them with the term "organic." Only foods that are grown and processed according to USDA organic standards can be labeled organic.
Organic food: Is it more nutritious?
Probably not, but the answer isn't yet clear. A recent study examined the past 50 years' worth of scientific articles about the nutrient content of organic and conventional foods. The researchers concluded that organically and conventionally produced foodstuffs are comparable in their nutrient content.
Organic food: Other considerations
Many factors influence the decision to choose organic food. Some people choose organic food because they prefer the taste. Yet others opt for organic because of concerns such as:
(4)Pesticides. Conventional growers use pesticides to protect their crops from molds, insects and diseases. When farmers spray pesticides, this can leave residue on produce. Some people buy organic food to limit their exposure to these residues. According to the USDA, organic produce carries significantly fewer pesticide residues than does conventional produce. However, residues on most products — both organic and nonorganic — don't exceed government safety thresholds.
Food additives. Organic regulations ban or severely restrict the use of food additives, processing aids (substances used during processing, but not added directly to food) and fortifying agents commonly used in nonorganic foods, including preservatives, artificial sweeteners, colorings and flavorings, and monosodium glutamate.
Environment. Some people buy organic food for environmental reasons. Organic farming practices are designed to benefit the environment by reducing pollution and conserving water and soil quality.
Are there downsides to buying organic?
One common concern with organic food is cost. Organic foods typically cost more than do their conventional counterparts. Higher prices are due, in part, to more expensive farming practices.
Because organic fruits and vegetables aren't treated with waxes or preservatives, they may spoil faster. Also, some organic produce may look less than perfect — odd shapes, varying colors or smaller sizes. However, organic foods must meet the same quality and safety standards as those of conventional foods.
Food safety tips
Whether you go totally organic or opt to mix conventional and organic foods, be sure to keep these tips in mind:
Select a variety of foods from a variety of sources. This will give you a better mix of nutrients and reduce your likelihood of exposure to a single pesticide.
Buy fruits and vegetables in season when possible. To get the freshest produce, ask your grocer what day new produce arrives. Or check your local farmers market.
Read food labels carefully. Just because a product says it's organic or contains organic ingredients doesn't necessarily mean it's a healthier alternative. Some organic products may still be high in sugar, salt, fat or calories.
Wash and scrub fresh fruits and vegetables thoroughly under running water. Washing helps remove dirt, bacteria and traces of chemicals from the surface of fruits and vegetables. Not all pesticide residues can be removed by washing, though. You can also peel fruits and vegetables, but peeling can mean losing some fiber and nutrients.
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Organic Agriculture and Production
(1)Organic refers to the way agricultural products are grown and processed. It includes a system of production, processing, distribution and sales that assures consumers that the products maintain the organic integrity that begins on the farm.
Setting the stage for U.S. National organic standards, the U.S. Congress adopted the Organic Foods Production Act (OFPA) in 1990 as part of the 1990 Farm Bill. This action was followed by over a decade of public input and discussion, which resulted in a National Organic Program final rule published by the U.S. Department of Agriculture (USDA) in December 2000 and implemented in October 2002.
These stringent standards put in place a system to certify that specific practices are used to produce and process organic agricultural ingredients used for food and non-food purposes.
National organic standards set out the methods, practices and substances used in producing and handling crops, livestock and processed agricultural products. The standards include a national list of approved synthetic and prohibited non-synthetic substances for organic production. See http://www.ota.com/listbackground05.html for more details.
(2)Organic production is based on a system of farming that maintains and replenishes soil fertility without the use of toxic and persistent pesticides and fertilizers. Organically produced foods also must be produced without the use of antibiotics, synthetic hormones, genetic engineering and other excluded practices, sewage sludge, or irradiation. Cloning animals or using their products would be considered inconsistent with organic practices. Organic foods are minimally processed without artificial ingredients, preservatives, or irradiation to maintain the integrity of the food.
National organic standards require that organic growers and handlers be certified by third-party state or private agencies or other organizations that are accredited by USDA. Although farmers and handlers who sell less than $5,000 a year in organic agricultural products and retailers that do not process these products are exempt from certification, they must meet all certified organic grower and handler requirements to maintain the organic integrity of the organic products they sell. Anyone who knowingly sells or mislabels as organic a product that was not produced and handled in accordance with the regulations can be subject to a civil penalty of up to $10,000 per violation.
Consumers can look for the “USDA Organic” seal or other approved labeling, and for the name of the certifier on the label of the products they consider for purchase. Products labeled “100% Organic” and carrying the “USDA Organic” seal are just that – they contain all organically produced ingredients. Products that are made from at least 95% organic ingredients, and have remaining ingredients that are approved for use in organic products may also carry the “USDA Organic” seal. In addition, products that contain at least 70% organic ingredients may label those on the ingredient listing. Producers and processors voluntarily use these labels, and may use organic ingredients without being required to label them.
Organic products can be found in grocery stores, cooperatives, specialty stores, farmer’s markets, farm stands, online, in many restaurants, and many other outlets.
For more information from USDA on labeling and other issues go to http://www.ams.usda.gov/nop/Consumers/brochure.html.
U.S. Organic Sales
The U.S. organic industry grew 21 percent overall to reach $17.7 billion in consumer sales in 2006, according to The Organic Trade Association’s 2007 Manufacturer Survey. Organic foods grew 16.2 percent in 2005 and accounted for $13.8 billion in sales. Nonfood organic products (personal care products, nutritional supplements, household cleaners, flowers, pet food, and clothing, bedding and other products from organic fibers such as flax, wool, and cotton) grew 26 percent, to total $938 million in U.S. sales in 2006.
(3)Organic foods and beverages continue to be one of the fastest growing segments in the overall $598 billion food market. According to the OTA survey, the $16.7 million in consumer sales of organic foods and beverages in 2006 represents an increase in market penetration from 2.5 percent of total U.S. food saels in 2005 to 2.8 percent in 2006. This represents a two percentage point increase from 0.8 percent in 1997 when organic food sales tracking began. The fastest growing food categories and their rates of growth over the previous year are organic meat (29 percent), organic dairy products (25 percent), and organic fruits and vegetables (24 percent). The fastest-growing non-food categories are organic pet food (36.7 percent), household products/cleaners (31.6 percent), and fiber linens and clothing (26.9 percent).
Organic foods are increasingly sold in mass market grocery stores, which represent the largest single distribution channel, accounting for 38 percent of organic food sales in 2006. Large natural food chains, along with small natural food chains or independent natural groceries and health food stores, represented about 44 percent of organic food sales. About 2 percent of organic food is sold through farmer’s markets.
Source: The Organic Trade Association (OTA) and Organic Trade Association’s 2007 Manufacturer Survey (Because USDA does not yet do comprehensive market studies of organic sales, as it does for conventional U.S. agriculture, OTA performs this research on the industry for its members and the public.)
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(1)Organic farming is the form of agriculture that relies on techniques such as crop rotation, green manure, compost and biological pest control to maintain soil productivity and control pests on a farm. Organic farming uses fertilizers and pesticides but excludes or strictly limits the use of manufactured(synthetic) fertilizers, pesticides (which include herbicides, insecticides and fungicides), plant growth regulators such as hormones, livestock antibiotics, food additives, and genetically modified organisms.
Organic agricultural methods are internationally regulated and legally enforced by many nations, based in large part on the standards set by the International Federation of Organic Agriculture Movements (IFOAM), an international umbrella organization for organic farming organizations established in 1972. IFOAM defines the overarching goal of organic farming as:
"Organic agriculture is a production system that sustains the health of soils, ecosystems and people. It relies on ecological processes, biodiversity and cycles adapted to local conditions, rather than the use of inputs with adverse effects. Organic agriculture combines tradition, innovation and science to benefit the shared environment and promote fair relationships and a good quality of life for all involved.."
—International Federation of Organic Agriculture Movements
(2)Since 1990, the market for organic products has grown from nothing, reaching $55 billion in 2009 according to Organic Monitor (www.organicmonitor.com). This demand has driven a similar increase in organically managed farmland. Approximately 37,000,000 hectares (91,000,000 acres) worldwide are now farmed organically, representing approximately 0.9 percent of total world farmland (2009) (see Willer/Kilcher 2011).
2.1 Soil management
2.2 Weed management
2.3 Controlling other organisms
2.4 Genetic modification
4.1 Geographic producer distribution
4.3 Productivity and profitability
4.3.2 Sustainability (African case)
4.4 Employment impact
5.2 Food quality and safety
5.3 Clothing quality and safety
5.4 Soil conservation
5.5 Climate change
5.6 Nutrient leaching
6 Sales and marketing
6.2 Farmers' markets
7 Capacity building
9 See also
12 Further reading
13 External links
Main article: History of organic farming
(3)Organic farming (of many particular kinds) was the original type of agriculture, and has been practiced for thousands of years. After the industrial revolution had introduced inorganic methods, some of which were not well developed and had serious side effects, an organic movement began in the 1940s as a reaction to agriculture's growing reliance on synthetic fertilizers. Artificial fertilizers had been created during the 18th century, initially with superphosphates and then ammonia-based fertilizers mass-produced using the Haber-Bosch process developed during World War I. These early fertilizers were cheap, powerful, and easy to transport in bulk. Similar advances occurred in chemical pesticides in the 1940s, leading to the decade being referred to as the 'pesticide era'.
Although organic farming is prehistoric in the widest sense, Sir Albert Howard is widely considered to be the "father of organic farming" in the sense that he was a key founder of the post-industrial-revolution organic movement. Further work was done by J.I. Rodale in the United States, Lady Eve Balfour in the United Kingdom, and many others across the world. The modern organic movement is a revival movement in the sense that it seeks to restore balance that was lost when technology grew rapidly in the 19th and 20th centuries.
Modern organic farming has made up only a fraction of total agricultural output from its beginning until today. Increasing environmental awareness in the general population has transformed the originally supply-driven movement to a demand-driven one. Premium prices and some government subsidies attracted farmers. In the developing world, many producers farm according to traditional methods which are comparable to organic farming but are not certified. In other cases, farmers in the developing world have converted for economic reasons.
Main article: Organic farming methods
Organic cultivation of mixed vegetables in Capay, California. Note the hedgerow in the background.
"An organic farm, properly speaking, is not one that uses certain methods and substances and avoids others; it is a farm whose structure is formed in imitation of the structure of a natural system that has the integrity, the independence and the benign dependence of an organism"
—Wendell Berry, "The Gift of Good Land"
 Soil management
(4)Plants need nitrogen, phosphorus, and potassium, as well as micronutrients and symbiotic relationships with fungi and other organisms to flourish, but getting enough nitrogen, and particularly synchronization so that plants get enough nitrogen at the right time (when plants need it most), is likely the greatest challenge for organic farmers. Crop rotation and green manure ("cover crops") help to provide nitrogen through legumes (more precisely, the Fabaceae family) which fix nitrogen from the atmosphere through symbiosis with rhizobial bacteria. Intercropping, which is sometimes used for insect and disease control, can also increase soil nutrients, but the competition between the legume and the crop can be problematic and wider spacing between crop rows is required. Crop residues can be ploughed back into the soil, and different plants leave different amounts of nitrogen, potentially aiding synchronization. Organic farmers also use animal manure, certain processed fertilizers such as seed meal and various mineral powders such as rock phosphate and greensand, a naturally occurring form of potash which provides potassium. Together these methods help to control erosion. In some cases pH may need to be amended. Natural pH amendments include lime and sulfur, but in the U.S. some compounds such as iron sulfate, aluminum sulfate, magnesium sulfate, and soluble boron products are allowed in organic farming.:43
Mixed farms with both livestock and crops can operate as ley farms, whereby the land gathers fertility through growing nitrogen-fixing forage grasses such as white clover or alfalfa and grows cash crops or cereals when fertility is established. Farms without livestock ("stockless") may find it more difficult to maintain fertility, and may rely more on external inputs such as imported manure as well as grain legumes and green manures, although grain legumes may fix limited nitrogen because they are harvested. Horticultural farms growing fruits and vegetables which operate in protected conditions are often even more reliant upon external inputs.
Biological research on soil and soil organisms has proven beneficial to organic farming. Varieties of bacteria and fungi break down chemicals, plant matter and animal waste into productive soil nutrients. In turn, they produce benefits of healthier yields and more productive soil for future crops. Fields with less or no manure display significantly lower yields, due to decreased soil microbe community, providing a healthier, more arable soil system.
 Weed management
Organic weed management promotes weed suppression, rather than weed elimination, by enhancing crop competition and phytotoxic effects on weeds. Organic farmers integrate cultural, biological, mechanical, physical and chemical tactics to manage weeds without synthetic herbicides.
Organic standards require rotation of annual crops, meaning that a single crop cannot be grown in the same location without a different, intervening crop. Organic crop rotations frequently include weed-suppressive cover crops and crops with dissimilar life cycles to discourage weeds associated with a particular crop. Organic farmers strive to increase soil organic matter content, which can support microorganisms that destroy common weed seeds.
Other cultural practices used to enhance crop competitiveness and reduce weed pressure include selection of competitive crop varieties, high-density planting, tight row spacing, and late planting into warm soil to encourage rapid crop germination.
Mechanical and physical weed control practices used on organic farms can be broadly grouped as:
Tillage - Turning the soil between crops to incorporate crop residues and soil amendments; remove existing weed growth and prepare a seedbed for planting;
Cultivation - Disturbing the soil after seeding;
Mowing and cutting - Removing top growth of weeds;
Flame weeding and thermal weeding - Using heat to kill weeds; and
Mulching - Blocking weed emergence with organic materials, plastic films, or landscape fabric.
Some naturally sourced chemicals are allowed for herbicidal use. These include certain formulations of acetic acid (concentrated vinegar), corn gluten meal, and essential oils. A few selective bioherbicides based on fungal pathogens have also been developed. At this time, however, organic herbicides and bioherbicides play a minor role in the organic weed control toolbox.
Weeds can be controlled by grazing. For example, geese have been used successfully to weed a range of organic crops including cotton, strawberries, tobacco, and corn, reviving the practice of keeping cotton patch geese, common in the southern U.S. before the 1950s. Similarly, some rice farmers introduce ducks and fish to wet paddy fields to eat both weeds and insects.
 Controlling other organisms
See also: Biological pest control
Organisms aside from weeds that cause problems on organic farms include arthropods (e.g., insects, mites), nematodes, fungi and bacteria. Organic farmers use a wide range of Integrated Pest Management practices to prevent pests and diseases. These include, but are not limited to, crop rotation and nutrient management; sanitation to remove pest habitat; provision of habitat for beneficial organisms; selection of pest-resistant crops and animals; crop protection using physical barriers, such as row covers; and crop diversification through companion planting or establishment of polycultures.
Organic farmers often depend on biological pest control, the use of beneficial organisms to reduce pest populations. Examples of beneficial insects include minute pirate bugs, big-eyed bugs, and to a lesser extent ladybugs (which tend to fly away), all of which eat a wide range of pests. Lacewings are also effective, but tend to fly away. Praying mantis tend to move more slowly and eat less heavily. Parasitoid wasps tend to be effective for their selected prey, but like all small insects can be less effective outdoors because the wind controls their movement. Predatory mites are effective for controlling other mites.:66-90
When these practices are insufficient to prevent or control pests an organic farmer may apply a pesticide. With some exceptions, naturally occurring pesticides are allowed for use on organic farms, and synthetic substances are prohibited. Pesticides with different modes of action should be rotated to minimize development of pesticide resistance.
Naturally derived insecticides allowed for use on organic farms use include Bacillus thuringiensis (a bacterial toxin), pyrethrum (a chrysanthemum extract), spinosad (a bacterial metabolite), neem (a tree extract) and rotenone (a legume root extract). These are sometimes called green pesticides because they are generally, but not necessarily, safer and more environmentally friendly than synthetic pesticides.:92[unreliable source?] Rotenone and pyrethrum are particularly controversial because they work by attacking the nervous system, like most conventional insecticides. Fewer than 10% of organic farmers use these pesticides regularly; one survey found that only 5.3% of vegetable growers in California use rotenone while 1.7% use pyrethrum (Lotter 2003:26).
Naturally derived fungicides allowed for use on organic farms include the bacteria Bacillus subtilis and Bacillus pumilus; and the fungus Trichoderma harzianum. These are mainly effective for diseases affecting roots. Agricultural Research Service scientists have found that caprylic acid, a naturally occurring fatty acid in milk and coconuts, as well as other natural plant extracts have antimicrobial characteristics that can help. Compost tea contains a mix of beneficial microbes, which may attack or out-compete certain plant pathogens, but variability among formulations and preparation methods may contribute to inconsistent results or even dangerous growth of toxic microbes in compost teas.
Some naturally derived pesticides are not allowed for use on organic farms. These include nicotine sulfate, arsenic, and strychnine.
Synthetic pesticides allowed for use on organic farms include insecticidal soaps and horticultural oils for insect management; and Bordeaux mixture, copper hydroxide and sodium bicarbonate for managing fungi.
 Genetic modification
Main article: Genetically modified organism
(5)A key characteristic of organic farming is the rejection of genetically engineered plants and animals. On October 19, 1998, participants at IFOAM's 12th Scientific Conference issued the Mar del Plata Declaration, where more than 600 delegates from over 60 countries voted unanimously to exclude the use of genetically modified organisms in food production and agriculture.
Although opposition to the use of any transgenic technologies in organic farming is strong, agricultural researchers Luis Herrera-Estrella and Ariel Alvarez-Morales continue to advocate integration of transgenic technologies into organic farming as the optimal means to sustainable agriculture, particularly in the developing world. Similarly, some organic farmers question the rationale behind the ban on the use of genetically engineered seed because they view this kind of biotechnology consistent with organic principles.
Although GMOs are excluded from organic farming, there is concern that the pollen from genetically modified crops is increasingly penetrating organic and heirloom seed stocks, making it difficult, if not impossible, to keep these genomes from entering the organic food supply. International trade restrictions limit the availability GMOs to certain countries.
The hazards that genetic modification could pose to the environment and/or individual health are hotly contested.
Main article: Organic certification
Standards regulate production methods and in some cases final output for organic agriculture. Standards may be voluntary or legislated. As early as the 1970s private associations certified organic producers. In the 1980s, governments began to produce organic production guidelines. In the 1990s, a trend toward legislated standards began, most notably with the 1991 EU-Eco-regulation developed for European Union, which set standards for 12 countries, and a 1993 UK program. The EU's program was followed by a Japanese program in 2001, and in 2002 the U.S. created the National Organic Program (NOP). As of 2007 over 60 countries regulate organic farming (IFOAM 2007:11). In 2005 IFOAM created the Principles of Organic Agriculture, an international guideline for certification criteria. Typically the agencies accredit certification groups rather than individual farms.
Organic production materials used in and foods are tested independently by the Organic Materials Review Institute.
Under USDA organic standards, manure must be subjected to proper thermophilic composting and allowed to reach a sterilizing temperature. If raw animal manure is used, 120 days must pass before the crop is harvested if the final product comes into direct contact with the soil. For products which do not come into direct contact with soil, 90 days must pass prior to harvest.
(6)The economics of organic farming, a subfield of agricultural economics, encompasses the entire process and effects of organic farming in terms of human society, including social costs, opportunity costs, unintended consequences, information asymmetries, and economies of scale. Although the scope of economics is broad, agricultural economics tends to focus on maximizing yields and efficiency at the farm level. Economics takes an anthropocentric approach to the value of the natural world: biodiversity, for example, is considered beneficial only to the extent that it is valued by people and increases profits. Some entities such as the European Union subsidize organic farming, in large part because these countries want to account for the externalities of reduced water use, reduced water contamination, reduced soil erosion, reduced carbon emissions, increased biodiversity, and assorted other benefits that result from organic farming.
Traditional organic farming is labor and knowledge-intensive whereas conventional farming is capital-intensive, requiring more energy and manufactured inputs.
Organic farmers in California have cited marketing as their greatest obstacle.
 Geographic producer distribution
The markets for organic products are strongest in North America and Europe, which as of 2001 are estimated to have $6 and $8 billion respectively of the $20 billion global market (Lotter 2003:6). As of 2007 Australasia has 39% of the total organic farmland, including Australia's 1,180,000 hectares (2,900,000 acres) but 97 percent of this land is sprawling rangeland (2007:35). US sales are 20x as much. (2003:7). Europe farms 23 percent of global organic farmland (6.9 million hectares), followed by Latin America with 19 percent (5.8 million hectares). Asia has 9.5 percent while North America has 7.2 percent. Africa has 3 percent.
Besides Australia, the countries with the most organic farmland are Argentina (3.1 million hectares), China (2.3 million hectares), and the United States (1.6 million hectares). Much of Argentina's organic farmland is pasture, like that of Australia (2007:42). Italy, Spain, Germany, Brazil (the world's largest agricultural exporter), Uruguay, and the UK follow the United States in the amount of organic land (2007:26).
Organic farmland by world region (2000-2008)
As of 2001, the estimated market value of certified organic products was estimated to be $20 billion. By 2002 this was $23 billion and by 2007 more than $46 billion.
In recent years both Europe (2007: 7.8 million hectares, European Union: 7.2 million hectares) and North America (2007: 2.2 million hectares) have experienced strong growth in organic farmland. In the EU it grew by 21% in the period 2005 to 2008. However, this growth has occurred under different conditions. While the European Union has shifted agricultural subsidies to organic farmers due to perceived environmental benefits, the United States has not, continuing to subsidize some but not all traditional commercial crops, such as corn and sugar. As a result of this policy difference, as of 2008 4.1% percent of European Union farmland was organically managed compared to the 0.6 percent in the U.S.
IFOAM's most recent edition of The World of Organic Agriculture: Statistics and Emerging Trends 2009 lists the countries which had the most hectares in 2007. The country with the most organic land is Australia with more than 12 million hectares, followed by Argentina, Brazil and the US. In total 32.2 million hectares were under organic management in 2007. For 1999 11 million hectares of organically managed land are reported.
As organic farming becomes a major commercial force in agriculture, it is likely to gain increasing impact on national agricultural policies and confront some of the scaling challenges faced by conventional agriculture.
 Productivity and profitability
Various studies find that versus conventional agriculture, organic crops yielded 91%, or 95-100%, along with 50% lower expenditure on fertilizer and energy, and 97% less pesticides, or 100% for corn and soybean, consuming less energy and zero pesticides.[clarification needed] The results were attributed to lower yields in average and good years but higher yields during drought years.
A 2007 study compiling research from 293 different comparisons into a single study to assess the overall efficiency of the two agricultural systems has concluded that
...organic methods could produce enough food on a global per capita basis to sustain the current human population, and potentially an even larger population, without increasing the agricultural land base. (from the abstract)
Converted organic farms have lower pre-harvest yields than their conventional counterparts in developed countries (92%) but higher than their low-intensity counterparts in developing countries (132%). This is due to relatively lower adoption of fertilizers and pesticides in the developing world compared to the intensive farming of the developed world.
Organic farms withstand severe weather conditions better than conventional farms, sometimes yielding 70-90% more than conventional farms during droughts. Organic farms are more profitable in the drier states of the United States, likely due to their superior drought performance. Organic farms survive hurricane damage much better, retaining 20 to 40% more topsoil and smaller economic losses at highly significant levels than their neighbors.
(7)Contrary to widespread belief, organic farming can build up soil organic matter better than conventional no-till farming, which suggests long-term yield benefits from organic farming. An 18-year study of organic methods on nutrient-depleted soil, concluded that conventional methods were superior for soil fertility and yield in a cold-temperate climate, arguing that much of the benefits from organic farming are derived from imported materials which could not be regarded as "self-sustaining".
The decreased cost of synthetic fertilizer and pesticide inputs, along with the higher prices that consumers pay for organic produce, contribute to increased profits. Organic farms have been consistently found to be as or more profitable than conventional farms. Without the price premium, profitability is mixed. Organic production was more profitable in Wisconsin, given price premiums.
 Sustainability (African case)
In 2008 the United Nations Environmental Programme (UNEP) and the United Nations Conference on Trade and Development (UNCTAD) stated that "organic agriculture can be more conducive to food security in Africa than most conventional production systems, and that it is more likely to be sustainable in the long-term" and that "yields had more than doubled where organic, or near-organic practices had been used" and that soil fertility and drought resistance improved.
 Employment impact
Organic methods often require more labor than traditional farming, therefore it provides rural jobs.
Main article: Motivations for organic agriculture
Agriculture imposes negative externalities (uncompensated costs) upon society through land and other resource use, biodiversity loss, erosion, pesticides, nutrient runoff, water usage, subsidy payments and assorted other problems. Positive externalities include self-reliance, entrepreneurship, respect for nature, and air quality. Organic methods reduce some of these costs. In 2000 uncompensated costs for 1996 reached 2,343 million British pounds or 208 pounds per hectare. In 2005 in the USA concluded that cropland costs the economy approximately 5 to 16 billion dollars ($30 to $96 per hectare), while livestock production costs 714 million dollars. Both studies recommended reducing externalities. The 2000 review included reported pesticide poisonings but did not include speculative chronic health effects of pesticides, and the 2004 review relied on a 1992 estimate of the total impact of pesticides.
It has been proposed that organic agriculture can reduce the level of some negative externalities from (conventional) agriculture. Whether the benefits are private or public depends upon the division of property rights.
A sign outside of an organic apple orchard in Pateros, Washington reminding orchardists not to spray pesticides on these trees.
(8)Most organic farms largely avoid pesticides as opposed to conventional farms. Some pesticides damage the environment or with direct exposure, human health. Children exposed to pesticides are of special concern. According to the National Academy of Sciences:
"A fundamental maxim of pediatric medicine is that children are not ‘little adults.’ Profound differences exist between children and adults. Infants and children are growing and developing. Their metabolic rates are more rapid than those of adults. There are differences in their ability to activate, detoxify, and excrete xenobiotic compounds. All these differences can affect the toxicity of pesticides in infants and children, and for these reasons the toxicity of pesticides is frequently different in children and adults.”
The five main pesticides used in organic farming are Bt (a bacterial toxin), pyrethrum, rotenone, copper and sulphur. Fewer than 10% of organic vegetable farmers acknowledge using these pesticides regularly; 5.3% of vegetable growers will admit rotenone use; while 1.7% admit pyrethrum use (Lotter 2003:26). Reduction and elimination of chemical pesticide use is technically challenging. Organic pesticides often complement other pest control strategies.
Ecological concerns primarily focus around pesticide use, as 16% of the world's pesticides are used in the production of cotton.
Runoff is one of the most damaging effects of pesticide use. The USDA Natural Resources Conservation Service tracks the environmental effects of water contamination and concluded, "the Nation's pesticide policies during the last twenty six years have succeeded in reducing overall environmental risk, in spite of slight increases in area planted and weight of pesticides applied. Nevertheless, there are still areas of the country where there is no evidence of progress, and areas where risk levels for protection of drinking water, fish, algae and crustaceans remain high".
 Food quality and safety
Main article: Organic food
Many studies have examined the relative quality and safety benefits of organic and conventional agricultural techniques. The results are diverse. Some find no significant differences. Others disagree. An example of the "no differences" school stated:
(9)No evidence of a difference in content of nutrients and other substances between organically and conventionally produced crops and livestock products was detected for the majority of nutrients assessed in this review suggesting that organically and conventionally produced crops and livestock products are broadly comparable in their nutrient content... There is no good evidence that increased dietary intake, of the nutrients identified in this review to be present in larger amounts in organically than in conventionally produced crops and livestock products, would be of benefit to individuals consuming a normal varied diet, and it is therefore unlikely that these differences in nutrient content are relevant to consumer health.
However, they also found that statistically significant differences between the composition of organic and conventional food were present for a few substances.
"Organic products stand out as having higher levels of secondary plant compounds and vitamin C". Organic kiwifruit had more antioxidants.
A review of potential health effects analysed eleven articles, concluding, "because of the limited and highly variable data available, and concerns over the reliability of some reported findings, there is currently no evidence of a health benefit from consuming organic compared to conventionally produced foodstuffs. It should be noted that this conclusion relates to the evidence base currently available on the nutrient content of foodstuffs, which contains limitations in the design and in the comparability of studies."
Individual studies have considered a variety of possible impacts, including pesticide residues. Pesticide residues present a second channel for health effects. Comments include, "Organic fruits and vegetables can be expected to contain fewer agrochemical residues than conventionally grown alternatives; yet, "the significance of this difference is questionable" and "It is intuitive to assume that children whose diets consist of organic food items would have a lower probability of neurologic health risks", and pesticide exposure brought an increased risk for ADHD in one study.
Nitrate concentrations may be less, but the health impact of nitrates is debated. Lack of data has limited research on the health effects of natural plant pesticides and bacterial pathogens. Consumption of organic milk was associated with a decrease in risk for eczema, although no comparable benefit was found for organic fruits, vegetables, or meat.
The higher cost of organic food (ranging from 45 to 200%) could inhibit consumption of the recommended 5 servings per day of vegetables and fruits, which improve health and reduce cancer regardless of their source.
 Clothing quality and safety
Main article: Organic clothing
Recently, organic clothing has become widely available. Although many consumers of organic clothing merely dislike synthetic chemicals, a significant portion of the organic clothing market comes from those suffering from Multiple Chemical Sensitivity, a chronic medical condition characterized by symptoms that the affected person says are adverse effects from exposure to low levels of chemicals.
 Soil conservation
Main article: Soil conservation
In Dirt: The Erosion of Civilizations, geomorphologist David Montgomery outlines a coming crisis from soil erosion. Agriculture relies on roughly one meter of topsoil, and that is being depleted ten times faster than it is being replaced. No-till farming, which some claim depends upon pesticides, is one way to minimize erosion. However, a recent study by the USDA's Agricultural Research Service has found that manure applications in tilled organic farming are better at building up the soil than no-till.
 Climate change
Organic agriculture emphasizes closed nutrient cycles, biodiversity, and effective soil management providing the capacity to mitigate and even reverse the effects of climate change. Organic agriculture can decrease fossil fuel emissions and, like any well managed agricultural system, sequesters carbon in the soil. The elimination of synthetic nitrogen in organic systems decreases fossil fuel consumption by 33 percent and carbon sequestration takes CO2 out of the atmosphere by putting it in the soil in the form of organic matter which is often lost in conventionally managed soils. Carbon sequestration occurs at especially high levels in organic no-till managed soil.
Agriculture has been undervalued and underestimated as a means to combat global climate change. Soil carbon data show that regenerative organic agricultural practices are among the most effective strategies for mitigating CO2emissions.
 Nutrient leaching
Excess nutrients in lakes, rivers, and groundwater can cause algal blooms, eutrophication, and subsequent dead zones. In addition, nitrates are harmful to aquatic organisms by themselves. The main contributor to this pollution is nitrate fertilizers whose use is expected to "double or almost triple by 2050". Organically fertilizing fields "significantly [reduces] harmful nitrate leaching" over conventionally fertilized fields: "annual nitrate leaching was 4.4-5.6 times higher in conventional plots than organic plots".
The large dead zone in the Gulf of Mexico is caused in large part by agricultural runoff: a combination of fertilizer and livestock manure. Over half of the nitrogen released into the Gulf comes from agriculture. This increases costs for fishermen, as they must travel far from the coast to find fish.
Nitrogen leaching into the Danube River was substantially lower among organic farms. The resulting externalities could be neutralized by charging 1 euro per kg of released nitrogen.
Agricultural runoff and algae blooms are strongly linked in California.
Main article: Organic farming and biodiversity
A wide range of organisms benefit from organic farming, but it is unclear whether organic methods confer greater benefits than conventional integrated agri-environmental programs. Nearly all non-crop, naturally occurring species observed in comparative farm land practice studies show a preference for organic farming both by abundance and diversity. An average of 30% more species inhabit organic farms. Birds, butterflies, soil microbes, beetles, earthworms, spiders, vegetation, and mammals are particularly affected. Lack of herbicides and pesticides improve biodiversity fitness and population density. Many weed species attract beneficial insects that improve soil qualities and forage on weed pests. Soil-bound organisms often benefit because of increased bacteria populations due to natural fertilizer such as manure, while experiencing reduced intake of herbicides and pesticides. Increased biodiversity, especially from beneficial soil microbes and mycorrhizae have been proposed as an explanation for the high yields experienced by some organic plots, especially in light of the differences seen in a 21-year comparison of organic and control fields.
Biodiversity from organic farming provides capital to humans. Species found in organic farms enhance sustainability by reducing human input (e.g., fertilizers, pesticides). Farmers that produce with organic methods reduce risk of poor yields by promoting biodiversity. Common game birds such as the ring-necked pheasant and the northern bobwhite often reside in agriculture landscapes, and benefit recreational hunters.
 Sales and marketing
Most sales are concentrated in developed nations. These products are what economists call credence goods in that they rely on uncertain certification. Interest in organic products dropped between 2006 and 2008, and 42% of Americans polled don't trust organic produce. 69% of Americans claim to occasionally buy organic products, down from 73% in 2005. One theory was that consumers were substituting "local" produce for "organic" produce.
In the United States, 75% of organic farms are smaller than 2.5 hectares. In California 2% of the farms account for over half of sales.(Lotter 2003:4) Small farms join together in cooperatives such as Organic Valley, Inc. to market their goods more effectively.
Most small cooperative distributors have merged or were acquired by large multinationals such as General Mills, Heinz, ConAgra, Kellogg, and others. In 1982 there were 28 consumer cooperative distributors, but as of 2007 only 3 remained. This consolidation has raised concerns among consumers and journalists of potential fraud and degradation in standards. Most sell their organic products through subsidiaries, under other labels.
Organic foods also can be a niche in developing nations. It would provide more money and a better opportunity to compete internationally with the huge distributors. Organic prices are much more stable than conventional foods, and the small farms can still compete and have similar prices with the much larger farms that usually take all of the profits.
 Farmers' markets
Price premiums are important for the profitability of small organic farmers. Farmers selling directly to consumers at farmers' markets have continued to achieve these higher returns. In the United States the number of farmers' markets tripled from 1,755 in 1994 to 5,274 in 2009.
 Capacity building
Organic agriculture can contribute to ecologically sustainable, socio-economic development, especially in poorer countries. The application of organic principles enables employment of local resources (e.g., local seed varieties, manure, etc.) and therefore cost-effectiveness. Local and international markets for organic products show tremendous growth prospects and offer creative producers and exporters excellent opportunities to improve their income and living conditions.
Organic agriculture is knowledge intensive. Globally, capacity building efforts are underway, including localized training material, to limited effect. As of 2007, the International Federation of Organic Agriculture Movements hosted more than 170 free manuals and 75 training opportunities online.
Norman Borlaug (father of the "Green Revolution" and a Nobel Peace Prize laureate), Prof A. Trewavas and other critics contested the notion that organic agricultural systems are more friendly to the environment and more sustainable than conventional farming systems. Borlaug asserts that organic farming practices can at most feed 4 billion people, after expanding cropland dramatically and destroying ecosystems in the process. The Danish Environmental Protection Agency estimated that phasing out all pesticides would result in an overall yield reduction of about 25%. Environmental and health effects were assumed but hard to assess.
In contrast, the UN Environmental Programme concluded that organic methods greatly increase yields in Africa. A review of over two hundred crop comparisons argued that organic farming could produce enough food to sustain the current human population and that the difference in yields between organic and non-organic methods were small, with non-organic methods yielding slightly more in developed areas and organic methods yielding slightly more in developing areas.
That analysis has been criticised by Alex Avery of the Hudson Institute, who contends that the review claimed many non-organic studies to be organic, misreported organic yields, made false comparisons between yields of organic and non-organic studies which were not comparable, counted high organic yields several times by citing different papers which referenced the same data, and gave equal weight to studies from sources which were not impartial.
The Center for Disease Control repudiated a claim by Avery's father, Dennis Avery (also at Hudson) that the risk of E. coli infection was eight times higher when eating organic food. (Avery had cited CDC as a source.) Avery had included problems stemming from non-organic unpasteurized juice in his calculations. Epidemiologists traced the 2011 E. coli O104:H4 outbreak - which caused over 3,900 cases and 52 deaths - to an organic farm in Bienenbüttel in Germany.
Urs Niggli, director of the FiBL Institute, contends that a global campaign against organic farming derives mostly from Alex Avery's book The truth about organic farming.
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Created By: Avery Horne
What is organic production?
USDA Definition and Regulations:
(1)The Organic Foods Production Act (OFPA), enacted under Title 21 of the 1990 Farm Bill, served to establish uniform national standards for the production and handling of foods labeled as “organic.” The Act authorized a new USDA National Organic Program (NOP) to set national standards for the production, handling, and processing of organically grown agricultural products. In addition, the Program oversees mandatory certification of organic production. The Act also established the National Organic Standards Board (NOSB) which advises the Secretary of Agriculture in setting the standards upon which the NOP is based. Producers who meet standards set by the NOP may label their products as “USDA Certified Organic.”
USDA National Organic Standards Board (NOSB) definition, April 1995
(2)“Organic agriculture is an ecological production management system that promotes and enhances biodiversity, biological cycles and soil biological activity. It is based on minimal use of off-farm inputs and on management practices that restore, maintain and enhance ecological harmony.
“‘Organic’ is a labeling term that denotes products produced under the authority of the Organic Foods Production Act. The principal guidelines for organic production are to use materials and practices that enhance the ecological balance of natural systems and that integrate the parts of the farming system into an ecological whole.
“Organic agriculture practices cannot ensure that products are completely free of residues; however, methods are used to minimize pollution from air, soil and water.
“Organic food handlers, processors and retailers adhere to standards that maintain the integrity of organic agricultural products. The primary goal of organic agriculture is to optimize the health and productivity of interdependent communities of soil life, plants, animals and people.”
CFR Regulatory Text, 7 CFR Part 205, Subpart A — Definitions. § 205.2 Terms defined
“Organic production. A production system that is managed in accordance with the Act and regulations in this part to respond to site-specific conditions by integrating cultural, biological, and mechanical practices that foster cycling of resources, promote ecological balance, and conserve biodiversity.” USDA National Organic Program. http://www.ams.usda.gov/nop/NOP/standards/DefineReg.html
USDA Consumer Brochure: Organic Food Standards and Labels: The Facts
(3)“What is organic food? Organic food is produced by farmers who emphasize the use of renewable resources and the conservation of soil and water to enhance environmental quality for future generations. Organic meat, poultry, eggs, and dairy products come from animals that are given no antibiotics or growth hormones. Organic food is produced without using most conventional pesticides; fertilizers made with synthetic ingredients or sewage sludge; bioengineering; or ionizing radiation. Before a product can be labeled ‘organic,’ a Government-approved certifier inspects the farm where the food is grown to make sure the farmer is following all the rules necessary to meet USDA organic standards. Companies that handle or process organic food before it gets to your local supermarket or restaurant must be certified, too.” Consumer
Brochure, USDA National Organic Program, http://www.ams.usda.gov/nop/Consumers/brochure.html
The final national organic standards rule was published in the Federal Register on December 21, 2000. The law was activated April 21, 2001. The rule, along with detailed fact sheets and other background information, is available on the National Organic Program's website,
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