Your search found 2 records
1 Berazneva, J.; McBride, L.; Sheahan, M.; Guerena, D.. 2018. Empirical assessment of subjective and objective soil fertility metrics in East Africa: implications for researchers and policy makers. World Development, 105:367-382. [doi: https://doi.org/10.1016/j.worlddev.2017.12.009]
Soil fertility ; Agricultural productivity ; Soil analysis ; Soil pH ; Soil types ; Soil quality ; Cation exchange capacity ; Natural resources management ; Researchers ; Policy making ; Farmers attitudes ; Crop yield ; Maize / East Africa / Kenya / Tanzania
(Location: IWMI HQ Call no: e-copy only Record No: H048769)
(1.09 MB)
Bringing together emerging lessons from biophysical and social sciences as well as newly available data, we take stock of what can be learned about the relationship among subjective (reported) and objective (measured) soil fertility and farmer input use in east Africa. We identify the correlates of Kenyan and Tanzanian maize farmers’ reported perceptions of soil fertility and assess the extent to which these subjective assessments reflect measured soil chemistry. Our results offer evidence that farmers base their perceptions of soil quality and soil type on crop yields. We also find that, in Kenya, farmers’ reported soil type is a reasonable predictor of several objective soil fertility indicators while farmer-reported soil quality is not. In addition, in exploring the extent to which publicly available soil data are adequate to capture local soil chemistry realities, we find that the time-consuming exercise of collecting detailed objective measures of soil content is justified when biophysical analysis is warranted, because farmers’ perceptions are not sufficiently strong proxies of these measures to be a reliable substitute and because currently available high-resolution geo-spatial data do not sufficiently capture local variation. In the estimation of agricultural production or profit functions, where the focus is on averages and in areas with low variability in soil properties, the addition of soil information does not considerably change the estimation results. However, having objective (measured) plot-level soil information improves the overall fit of the model and the estimation of marginal physical products of inputs. Our findings are of interest to researchers who design, field, or use data from agricultural surveys, as well as policy makers who design and implement agricultural interventions and policies.
2 Campolo, J.; Guerena, D.; Maharjan, S.; Lobell, D. B. 2021. Evaluation of soil-dependent crop yield outcomes in Nepal using ground and satellite-based approaches. Field Crops Research, 260:107987. [doi: https://doi.org/10.1016/j.fcr.2020.107987]
Crop yield ; Soil deficiencies ; Estimation ; Satellite imagery ; Remote sensing ; Soil maps ; Wheat ; Farmland ; Fertilizers ; Soil quality ; Soil properties ; Smallholders ; Weather data ; Models / Nepal / Terai Region
(Location: IWMI HQ Call no: e-copy only Record No: H050190)
(5.14 MB)
Smallholder farmers face many constraints to achieving food security. Optimal soil management is often limited by a lack of accessible and accurate soil characterization, and an associated lack of soil-specific management practice recommendations. Crop yields depend on both soil quality and soil-mediated fertilizer responses. Existing research on soil-fertilizer interactions is primarily based on farm trials and/or survey data, which are resource intensive and typically restricted to local scales. High-resolution (~10-meter) remote sensing data and digital soil maps provide a low cost, scalable alternative. Here, we deploy methods based on the Sentinel satellite constellation to estimate soil and fertilizer impacts on irrigated wheat grain yields in Nepal and to inform precision soil and nutrient management recommendations. We first combine field data with Sentinel-1 and Sentinel-2 imagery to delineate wheat cropping areas for 2016–2019 with 92 % accuracy. We then estimate wheat yields at 10-meter resolution using Sentinel-2 and weather covariates based on yield models parameterized from two different methods: 1) APSIM crop model simulations and 2) ground cropcuts from 147 fields. Using a large dataset of soil samples collected by the Nepal Agricultural Research Council, we examine the linear and non-linear effects of soil properties on wheat yields. Finally, the soil maps were combined with a survey of field-level crop management data and our yield estimates to test the interaction of soil quality with fertilizer effectiveness. Our ground-calibrated satellite model predicted yields with good accuracy (R2 = 0.55), while the uncalibrated simulation-based approach had weaker but significant prediction accuracy (R2 = 0.24). We find statistically significant gains in yield of 0.9–2.4 % are possible by increasing soil organic matter and zinc from highly deficient values to optimal values of 2.2 % organic matter (OM) and 0.67 ppm zinc (Zn). Using digital soil maps of Nepal produced by the International Maize and Wheat Improvement Center (CIMMYT), we map croplands deficient in zinc (Zn < 0.66 ppm) and organic matter (OM < 2.2 %) and find that 72 % of croplands in the Nepal Terai are experiencing less than optimal levels of these nutrients. We examine the effectiveness of nitrogen and zinc fertilizers, applied in amounts ranging from 0 to 150 kg ha-1 and 0 to 15 kg ha-1 respectively, in different soil quality regimes as determined by a soil quality index informed by standard relationships between crop yields and soil properties. Yields were significantly more responsive to zinc fertilizer inputs in soils with a higher than average soil quality but responded similarly to nitrogen inputs across different soils. Effects of soil and fertilizers on the simulation-based yield estimate were generally similar but less significant than effects on ground-calibrated yields. Overall, nitrogen and zinc increased ground-calibrated yields by 0.8 and 10.4 kg ha-1 per kg of nutrient input, respectively. This research demonstrates the potential of satellite data, crop simulation, and machine learning to examine the influence of soils on yields and guide precision fertilizer use in smallholder regions.
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