Repositorio Nacional

This literature delves into 82 research articles, published between 2016 and 2022, to develop a deep understanding of how women manage their lives and livelihoods within their agrifood systems when these systems are being affected, sometimes devastatingly, by climate change. The Findings show that four core gender norms affect the ability of women to achieve economic resilience in the face of climate change operate in agrifood production systems. Each of these gender norms speaks to male privilege: (i) Men are primary decision-makers, (ii) Men are breadwinners, (iii) Men control assets, and (iv) Men are food system actors. These gender norms are widely held and challenge women’s abilities to become economically resilient. These norms are made more powerful still because they fuse with each other and act on multiple levels, and they serve to support other norms which limit women’s scope to act. It is particularly noteworthy that many institutional actors, ranging from community decision-makers to development partners, tend to reinforce rather than challenge gender norms because they do not critically review their own assumptions.

However, the four gender norms cited are not hegemonic. First, there is limited and intriguing evidence that intersectional identities can influence women’s resilience in significant ways. Second, gender norms governing women’s roles and power in agrifood systems are changing in response to climate change and other forces, with implications for how women respond to future climate shocks. Third, paying attention to local realities is important – behaviours do not necessarily substantiate local norms. Fourth, women experience strong support from other women in savings groups, religious organisations, reciprocal labour, and others. Fifth, critical moments, such as climate disasters, offer potentially pivotal moments of change which could permit women unusually high levels of agency to overcome restrictive gender norms without being negatively sanctioned. The article concludes with recommendations for further research.

Economic Resilience Intersectional Identities Women Groups Support CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA ECONOMICS RESILIENCE CLIMATE CHANGE GENDER NORMS AGRIFOOD SYSTEMS WOMEN

Unsupervised segmentation and clustering time series approach to Southern Africa rainfall regime changes

Lovemore Chipindu Walter Mupangwa Isaiah Nyagumbo Mainassara Zaman-Allah (2023, [Artículo])

Autoregressive Integrated Moving Average Facebook Prophet Hidden Markov Model Regression Regression with Hidden Logistic Process CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA COASTAL AREAS SEMIARID ZONES SUBHUMID ZONES RAINFALL CLIMATE CHANGE

Mining alleles for tar spot complex resistance from CIMMYT's maize Germplasm Bank

Martha Willcox Juan Burgueño Daniel Jeffers Zakaria Kehel Rosemary Shrestha Kelly Swarts Edward Buckler Sarah Hearne Charles Chen (2022, [Artículo])

Maize Landraces Maize Genetic Resources Allelic Diversity Rare Alleles Phenotypic Characterization Tropical Maize Phyllachora maydis CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA MAIZE LANDRACES GENETIC RESOURCES ALLELES FOLIAR DISEASES 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