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- Genetic mapping and validation of QTL for whitefly resistance in cassava (Manihot esculenta Crantz)Author(s): Bohorquez-Chaux, Adriana; Becerra Lopez-Lavalle, Luis Augusto; Barrera‑Enriquez, Vianey Barrera; Gómez‑Jiménez, María Isabel; Sanchez‑Sarria, Camilo E.; Delgado, Luis Fernando; Zhang, Xiaofei; Gimode, Winnie (Springer nature link, 2025-06-24)Date: 2025-06-24Type: Journal ArticleStatus: Open accessWhitefly species pose a major threat to cassava production in tropical regions causing direct plant damage and transmitting viruses that lead to devastating cassava diseases. Aleurotrachelus socialis whitefly is one of the pests that affect cassava in South America. Developing resistant cassava varieties is the most sustainable control strategy for managing whiteflies. This study aimed to map the quantitative trait loci (QTL) associated with resistance to A. socialis and develop molecular markers to facilitate marker-assisted selection. An F2 cassava population (N = 183) was generated by selfing a highly resistant F1 derived from a cross between ECU72 (resistant) and COL2246 (susceptible) landraces. Phenotyping was performed using an efficient glasshouse screening method and high throughput image analysis of infested leaves (Nymphstar). We identified QTL on chromosomes 1, 2, 5, 6, 8, 9, and 14, with a stable and highly significant QTL on chromosome 8 (MeF2WFly8.1), explaining 35.44% of the phenotypic variation. To enable efficient selection, high-throughput KASP markers were developed and validated across diverse genetic backgrounds. Three SNPs displayed the highest association with whitefly resistance, with Chr08_6483145 as the most effective marker for selection in diverse backgrounds. These markers are provided for improving the efficiency of whitefly resistance breeding in the global cassava community.
- Potential impacts of carbon pricing on vegetable cold chainsAuthor(s): Alegbeleye, Oluwadara; Kassie, Girma; Ndour, Adama; Adamseged, Muluken Elias; Athukorala, Aruni (Elsevier (12 months), 2025-12-01)Date: 2025-09-24Type: Journal ArticleStatus: Open accessThe urgent need to address climate change has prompted growing interest in carbon pricing mechanisms as tools for reducing emissions in food systems. This review explores how carbon pricing may affect vegetable cold chains, which rely on energy-intensive, temperature-controlled networks essential for preserving produce quality and limiting food loss. While carbon pricing can serve as an incentive for adopting energy-efficient technologies, renewable energy, and sustainable logistics practices, its implementation can also trigger adverse consequences. These include increased operational costs, potential disruptions to supply chains, food affordability challenges, and public health concerns, particularly for vulnerable populations. Drawing on global evidence, this paper discusses both the enabling conditions for carbon pricing (when applied to vegetable cold chains or relevant stages within them) to deliver environmental benefits and the risks of socio-economic trade-offs, including potential impacts on labour, equity, and food security. Mitigation strategies, such as revenue recycling, targeted subsidies, and hybrid policy designs, are also discussed. Overall, the paper emphasizes the need for carefully designed carbon pricing mechanisms tailored to the structure of vegetable cold chains to ensure a just and effective transition to low-carbon food systems.
- Impacts of different land and water management interventions on runoff and sediment yield under climate change: a case study from the Bale Eco-Region, EthiopiaAuthor(s): Gashaw, Temesgen; Mekuria, Wolde; Ray, Ram; Tefera, Gebrekidan Worku; R. van Oel, Pieter; Chukalla, Abebe D.; Adgo, Enyew; Abeyou, Abeyou; Taddele Dile, Yihun; Seid, Abdulkarim; Bantider, Amare; Berhanu, Daniel; Legese, Gudina; Ayele, Girma; Haileslassie, Amare (Taylor and Francis Group, 2025-10-03)Date: 2025-10-03Type: Journal ArticleStatus: Timeless limited accessThis study evaluated the impacts of land and water management (LWM) intervention scenarios on runoff and sediment yield (SY) in Ethiopia's Bale Eco-Region (BER) under changing climate conditions. The scenarios analyzed include implementing conservation measures in non-cultivated lands (Scenario 1), cultivated lands (Scenario 2), and a combination of both (Scenario 3). These were compared against a Reference Scenario (i.e., no LWM interventions). The revised Soil and Water Assessment Tool (SWAT+) model was applied for baseline (1992–2020), and mid-century (2041–2070) and late-century (2071–2100) periods under SSP2-4.5 and SSP5-8.5 climate projections. Model calibration (2001–2009) and validation (1995–2000) for streamflow and sediment parameters confirmed SWAT+ suitability for the BER. Results indicate that, under the Reference Scenario, runoff and SY are projected rise by 12.8–53.7% and 16–60.6%, respectively, in future periods. Scenario 3, followed by Scenario 1, provided the greatest reductions in average areal runoff and SY. This study shows that conserving non-cultivated lands through LWM practices significantly reduces runoff and sediment yield. To effectively mitigate these impacts under changing climates, conservation should prioritize both cultivated and non-cultivated lands. The presented framework offers a transferable approach for evaluating LWM strategies in regions with comparable environmental conditions.
- Family farming through the lens of work organization. Illustrations from AfricaAuthor(s): Dedieu, Benoît; Hostiou, Nathalie; Kuzo, Jacques; Mercandalli, Sara; Frija, Aymen; Alary, Veronique; Haule, Yohana; Raharimalala, Sitrakiniana; Belières, Jean-François; Dembele, Catherine; Cesaro, Jean-daniel; BA, BABA; Girard, Pierre (EDP Sciences, 2025-11-14)Date: 2025-11-14Type: Journal ArticleStatus: Open accessThe paper’s objective is to generate knowledge about work organization patterns in family farms across Africa. A thorough understanding of farming systems is essential (since behind every practice, there is one or several workers), as well as a step toward clarifying the diversity of workers’ task profiles and working conditions across the continent. The study uses a multisite approach, covering five African countries and 14 farming systems, from Tunisia to Madagascar, and is based on the Quaework methodology (which tracks the annual duration of routine and seasonal work, by category of worker—farm manager, family workers, permanent and temporary workers, and by gender). In total, 438 farms were surveyed. Three work organization patterns were identified: one based on the farmer’s investment in either managing the system or performing most operational tasks; another relying on the contributions of family members notably to routine work; and the third involving the mobilization of wage workers, particularly temporary workers, for seasonal tasks. Most farming systems combine multiple work organization patterns. The contribution of women to farm work is also discussed, sometimes more significant and other times less so, depending on the farming system. We examine the workers’ task profiles and related working conditions, providing more detail on what the work involves and who the workers are behind “family” farming.
- Enhanced agricultural land use/land cover classification in the Nile Delta using Sentinel-1 and Sentinel-2 data and machine learningAuthor(s): Maze, Mona; Attaher, Samar; Taqi, Mohamed O.; Elsawy, Rania; Gad El-Moula, Manal M.H.; Hashem, Fadl A.; Moussa, Ahmed S. (Elsevier (12 months), 2025-11-01)Date: 2025-08-21Type: Journal ArticleStatus: Timeless limited accessAccurate and timely Land Use and Land Cover (LULC) classification is crucial for effective agricultural planning and decision-making, particularly in regions like the Nile Delta, Egypt, where LULC is rapidly changing. This study addresses the challenge of classifying small, fragmented agricultural fields and road networks by leveraging the synergistic potential of Sentinel-1 and Sentinel-2 data, combined with Machine Learning (ML) and Deep Learning (DL) techniques. Unlike previous studies that often rely on Sentinel-2 or image-based DL, this research introduces a novel approach: a pixel-based ML classification using both Sentinel-1 and Sentinel-2 data. This strategy allowed to effectively capture the spectral and textural information crucial for distinguishing small features, which are often missed by traditional methods. Using distinct temporal datasets and validated ground truth annotations, we trained and tested several ML and DL models, including XGB, Support Vector Classifier, KNearest Neighbor, Decision Tree, Random Forest, and LSTM. XGB achieved the highest overall accuracy (94.4 %), whereas Random Forest produced the most accurate map with independent data (91.4 % Overall Accuracy). Integrating Sentinel-1 with Sentinel-2 data improved classification accuracy by 1–7 % compared to using Sentinel-2 alone. Notably, the pixel-based ML approach yielded reliable predictions for small road areas and agricultural fields, which are often challenging to map accurately. This research demonstrates the effectiveness of integrating multi-sensor data with advanced ML/DL for improved LULC classification, particularly for small feature mapping, thus providing critical information for enhanced agricultural planning and decision-making in the Nile Delta.