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Anil, A.S., Sharma, V.K., Jimenez-Ballesta, R., Parihar, C.M., Datta, S.P., Barman, M., Chobhe, K.A., Kumawat, C., Patra, A. & Jatav, S.S. 2022. Land. 11 (9). Article number 1488 https://doi.org/10.3390/land11091488
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Kaduwal, S., Karki, T.B., Neupane, R., Bhattarai, R.K., Chaulagain, B., Ghimire, P., Gyawaly, P. & Das, S.K. 2022. Agronomy Journal of Nepal. 6 (1) 119-131. https://doi.org/10.3126/ajn.v6i1.47953
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This study estimated the the spatial-temporal variations of maize yields from 13-year on-farm trials from 4 countries in Southern Africa. Agronomic data from long-term CA trials is used with gridded biophysical and socioeconomic variables. Comparisons were made between CA and CT practices with above and below average precipitation. The variable importance analysis showed that the altitude, precipitation, temperature, and soil physical and nutrients conditions variables explained most of the variation in maize grain yield. Maps were generated to identify the locations where CA had a yield advantage over CP during seasons with below and above-average precipitation. The paper concludes that multi-source remotely sensed data, coupled with advanced and efficient machine learning algorithms provides accurate, cost-effective, and timely platforms for predicting the optimal locations for upscaling sustainable agricultural technologies.