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- ItemNear Real-Time Biophysical Rice (Oryza sativa L.) Yield Estimation to Support Crop Insurance Implementation in India(Basel : MDPI, 2020) Arumugam, Ponraj; Chemura, Abel; Schauberger, Bernhard; Gornott, ChristophImmediate yield loss information is required to trigger crop insurance payouts, which are important to secure agricultural income stability for millions of smallholder farmers. Techniques for monitoring crop growth in real-time and at 5 km spatial resolution may also aid in designing price interventions or storage strategies for domestic production. In India, the current government-backed PMFBY (Pradhan Mantri Fasal Bima Yojana) insurance scheme is seeking such technologies to enable cost-efficient insurance premiums for Indian farmers. In this study, we used the Decision Support System for Agrotechnology Transfer (DSSAT) to estimate yield and yield anomalies at 5 km spatial resolution for Kharif rice (Oryza sativa L.) over India between 2001 and 2017. We calibrated the model using publicly available data: namely, gridded weather data, nutrient applications, sowing dates, crop mask, irrigation information, and genetic coefficients of staple varieties. The model performance over the model calibration years (2001–2015) was exceptionally good, with 13 of 15 years achieving more than 0.7 correlation coefficient (r), and more than half of the years with above 0.75 correlation with observed yields. Around 52% (67%) of the districts obtained a relative Root Mean Square Error (rRMSE) of less than 20% (25%) after calibration in the major rice-growing districts (>25% area under cultivation). An out-of-sample validation of the calibrated model in Kharif seasons 2016 and 2017 resulted in differences between state-wise observed and simulated yield anomalies from –16% to 20%. Overall, the good ability of the model in the simulations of rice yield indicates that the model is applicable in selected states of India, and its outputs are useful as a yield loss assessment index for the crop insurance scheme PMFBY.
- ItemFarm Production Diversity and Household Dietary Diversity: Panel Data Evidence From Rural Households in Tanzania(Lausanne : Frontiers Media, 2021-5-17) Habtemariam, Lemlem Teklegiorgis; Gornott, Christoph; Hoffmann, Harry; Sieber, StefanEvidence on whether diversifying farm production leads to improved household dietary diversity and nutrition remains inconclusive. Existing studies analyzing the link between production diversity and dietary diversity are mainly based on cross-sectional methods, which could be biased by omitted confounding factors. Using two waves of a panel household survey of 900 rural households in Tanzania, this paper examines the link between production diversity and dietary diversity, while minimizing potential confounding effects. We estimate four regression models with two different production diversity measures and two panel estimation methods—fixed effect (FE) and random effect (RE). In three out of the four models, production diversity is significantly and positively associated with the dietary diversity measure of the food consumption score. The production diversity indicator is represented by the total crop and livestock species count, as well as by counting only crop species. The total crop and livestock species count shows a significant positive association with dietary diversity across estimation methods while the positive association with crop species count is not significant in the FE method. Our results suggest that the selection of appropriate production diversity indicators tailored to the specific circumstances of the local agricultural system is likely one key factor in identifying a robust relationship between production diversity and dietary diversity.