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Analysis of adoption of conservation agriculture practices in southern Africa: mixed-methods approach

Adane Tufa Hambulo Ngoma Paswel Marenya Christian Thierfelder (2023, [Artículo])

In southern Africa, conservation agriculture (CA) has been promoted to address low agricultural productivity, food insecurity, and land degradation. However, despite significant experimental evidence on the agronomic and economic benefits of CA and large scale investments by the donor community and national governments, adoption rates among smallholders remain below expectation. The main objective of this research project was thus to investigate why previous efforts and investments to scale CA technologies and practices in southern Africa have not led to widespread adoption. The paper applies a multivariate probit model and other methods to survey data from 4,373 households and 278 focus groups to identify the drivers and barriers of CA adoption in Malawi, Zambia, and Zimbabwe. The results show that declining soil fertility is a major constraint to maize production in Zambia and Malawi, and drought/heat is more pronounced in Zimbabwe. We also find gaps between (a) awareness and adoption, (b) training and adoption, and (c) demonstration and adoption rates of CA practices in all three countries. The gaps are much bigger between awareness and adoption and much smaller between hosting demonstration and adoption, suggesting that much of the awareness of CA practices has not translated to greater adoption. Training and demonstrations are better conduits to enhance adoption than mere awareness creation. Therefore, demonstrating the applications and benefits of CA practices is critical for promoting CA practices in all countries. Besides, greater adoption of CA practices requires enhancing farmers’ access to inputs, addressing drudgery associated with CA implementation, enhancing farmers’ technical know-how, and enacting and enforcing community bylaws regarding livestock grazing and wildfires. The paper concludes by discussing the implications for policy and investments in CA promotion.

Adoption Focus Group Discussion CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA CONSERVATION AGRICULTURE CLIMATE CHANGE

Automated in-season rice crop mapping using Sentinel time-series data and Google Earth Engine: A case study in climate-risk prone Bangladesh

Mustafa Kamal Timothy Joseph Krupnik (2024, [Artículo])

High-resolution mapping of rice fields is crucial for understanding and managing rice cultivation in countries like Bangladesh, particularly in the face of climate change. Rice is a vital crop, cultivated in small scale farms that contributes significantly to the economy and food security in Bangladesh. Accurate mapping can facilitate improved rice production, the development of sustainable agricultural management policies, and formulation of strategies for adapting to climatic risks. To address the need for timely and accurate rice mapping, we developed a framework specifically designed for the diverse environmental conditions in Bangladesh. We utilized Sentinel-1 and Sentinel-2 time-series data to identify transplantation and peak seasons and employed the multi-Otsu automatic thresholding approach to map rice during the peak season (April–May). We also compared the performance of a random forest (RF) classifier with the multi-Otsu approach using two different data combinations: D1, which utilizes data from the transplantation and peak seasons (D1 RF) and D2, which utilizes data from the transplantation to the harvest seasons (D2 RF). Our results demonstrated that the multi-Otsu approach achieved an overall classification accuracy (OCA) ranging from 61.18% to 94.43% across all crop zones. The D2 RF showed the highest mean OCA (92.15%) among the fourteen crop zones, followed by D1 RF (89.47%) and multi-Otsu (85.27%). Although the multi-Otsu approach had relatively lower OCA, it proved effective in accurately mapping rice areas prior to harvest, eliminating the need for training samples that can be challenging to obtain during the growing season. In-season rice area maps generated through this framework are crucial for timely decision-making regarding adaptive management in response to climatic stresses and forecasting area-wide productivity. The scalability of our framework across space and time makes it particularly suitable for addressing field data scarcity challenges in countries like Bangladesh and offers the potential for future operationalization.

Synthetic Aperture Radar Random Forest Boro Rice In-Season Maps CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA SAR (RADAR) RICE FLOODING CLIMATE CHANGE