TY - JOUR
T1 - Measuring postharvest loss inequality
T2 - Method and applications
AU - Miljkovic, Dragan
AU - Winter-Nelson, Alex
N1 - Funding Information:
APHLIS is the premier international effort to collect, analyze and disseminate data on postharvest losses of cereals in sub-Saharan Africa. The project is supported jointly by the European Commission and the Bill & Melinda Gates Foundation. APHLIS provides evidence-based data on postharvest loss at a large scale that would be prohibitively expensive to obtain by direct observation. It does this by combining loss data from academic research with contextual observations from local experts. In doing so APHLIS provides researchers, practitioners and policy makers a valuable overview of the current cost of postharvest loss across and within sub-Saharan African countries, allowing them to focus on crops and areas where interventions will have the most impact. Historically, APHLIS has focused on eight cereal crops – maize, sorghum, millet, wheat, barley, rice, teff and fonio. APHLIS provides consistent data for more than two dozen relatively homogeneous countries to create the basis for meaningful comparisons of the PHL and PHL inequality measures. Following APHLIS, in this paper maize PHL includes losses that take place during the harvest of food crops and all the steps of the value chain through the wholesale market, including on-farm handling, packing and storage, processing, distribution, transport, and wholesale marketing. In other words, PHL in this paper includes harvest up to but excluding retail markets. Pre-harvest losses (due to lost yield or loss of potential food) and consumer waste (at home and in food service) are not included ( Gustavsson and Cederberg, 2011 ; Timmermans et al., 2014 ; Xue et al., 2017 ). Including harvesting losses as part of “postharvest losses” is important because harvest losses are generally not included in production yield estimates and they represent measurable losses of unharvested crops and the losses that can occur during the harvest activities that are often bundled with postharvest functions (i.e. combine harvesting that links harvest and threshing functions).
Publisher Copyright:
© 2020 Elsevier Ltd
PY - 2021/1
Y1 - 2021/1
N2 - Sustainably meeting future food demand requires increases in food production and reductions in the amount of food that is lost and wasted. This paper examines inequality in postharvest losses to reveal patterns and opportunities for intervention. We present a measure that provides information on the distribution of postharvest losses in a single graph, the postharvest loss inequality curve, or an index number, the postharvest loss inequality index. Inequality measurement can help direct policy measures to units generating the greatest postharvest losses and thereby support more favorable policy outcomes and cost/benefit relationships. Concepts and methods introduced here are empirically analyzed based on the African Postharvest Losses Information System data for maize losses in Sub-Saharan Africa. Empirical results indicate the presence of a great deal of variability and inequality in postharvest losses as measured by the postharvest loss inequality index. In the data analyzed, the postharvest loss inequality index better captures anomalies in data distribution such as outliers, skewness and kurtosis than the more direct measure of postharvest losses as a share of total maize production.
AB - Sustainably meeting future food demand requires increases in food production and reductions in the amount of food that is lost and wasted. This paper examines inequality in postharvest losses to reveal patterns and opportunities for intervention. We present a measure that provides information on the distribution of postharvest losses in a single graph, the postharvest loss inequality curve, or an index number, the postharvest loss inequality index. Inequality measurement can help direct policy measures to units generating the greatest postharvest losses and thereby support more favorable policy outcomes and cost/benefit relationships. Concepts and methods introduced here are empirically analyzed based on the African Postharvest Losses Information System data for maize losses in Sub-Saharan Africa. Empirical results indicate the presence of a great deal of variability and inequality in postharvest losses as measured by the postharvest loss inequality index. In the data analyzed, the postharvest loss inequality index better captures anomalies in data distribution such as outliers, skewness and kurtosis than the more direct measure of postharvest losses as a share of total maize production.
KW - Food security
KW - Measuring postharvest loss inequality
KW - Postharvest loss inequality curve
KW - Postharvest loss inequality index
UR - http://www.scopus.com/inward/record.url?scp=85094126943&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85094126943&partnerID=8YFLogxK
U2 - 10.1016/j.agsy.2020.102984
DO - 10.1016/j.agsy.2020.102984
M3 - Article
AN - SCOPUS:85094126943
SN - 0308-521X
VL - 186
JO - Agricultural Administration
JF - Agricultural Administration
M1 - 102984
ER -