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While there is growing awareness of the issue of food losses at the political level, official post-harvest loss data for informing policymaking and reporting on SDG Indicator 12.3.1. (a) Food Loss Index is scarce. Representative sample-based surveys are necessary to obtain information on on-farm losses at the country level, but due to the issue's complexity, a loss module covering several key questions is needed. One main strategy proposed by the 50x2030 Initiative for optimizing data collection is sub-sampling for some of the survey modules. This paper examines whether modelling approaches can be combined with sub-sampling to improve harvest and post-harvest loss estimates and allow for further sample and cost reduction. The paper first presents the loss models generated on four selected surveys conducted in Malawi, Zimbabwe, and Nigeria, which were built using the Classification and Regression Tree (CART) method. The performance of each model is assessed for different sizes of sub-samples to improve the sample-based estimates, either by model-based estimates or by model-based imputation. The research concludes that the model-based estimates improve the loss estimates of the sub-samples due to post-stratification implied in the CART method, whereby they can constitute a cost-effective complement to sub-sampling strategies, while model-based imputations should only be used on a reduced number of missing observations. The models perform best when the survey invests in obtaining more detailed on-farm loss data and considers some key variables identified as relevant for on-farm loss models. Sub-sampling allows for investment in more detailed questionnaires and some considerations are derived for its design. [ABSTRACT FROM AUTHOR]

• Survey question recall length is associated with measurement error in agricultural survey data. • Measurement error affects key agricultural output, input, and productivity variables. • Recall length effects are sizeable at 2–5 percent of the value of the variable per additional month. • The recall effect affects the reliability of key metrics, including SDG indicators. • Investment in better measurement and conscientious survey design are critical. This paper assesses the relationship between the length of recall and nonrandom error in agricultural survey data. Using data from LSMS-ISA surveys in Malawi and Tanzania, we show that key input and output variables are systematically related to the length of the recall period, indicating the presence of nonrandom measurement error. With longer recall periods, farmers report higher quantities of harvest, labor and fertilizer inputs. Farmers list fewer plots as the recall period increases. We argue that it is plausible that farmers over-estimate plot-level outcomes – harvest, labor and fertilizer inputs – while it is also plausible that they forget some of their more marginal plots as their memory decays due to longer recall periods. We find evidence of measurement error related to the length of recall also in common measures of agricultural productivity. The size of the recall effect typically varies between 2 and 5 percent per additional month of recall length, making its impact on the reliability of key agricultural indicators, including SDG indicator 2.3, economically significant. With data reliability affecting policy effectiveness, improving agricultural survey data quality remains an important concern, especially in light of the ambitious 2030 agenda reflected in the Sustainable Development Goals. Mainstreaming objective measures where possible and reducing the risk of recall error through shorter recall periods appear to be promising avenues to improve the quality of key variables in agricultural surveys. [ABSTRACT FROM AUTHOR]