Estimating smallholder crop yields robustly and timely is crucial for improving agronomic practices, determining yield gaps, guiding investment, and policymaking to ensure food security. However, there is poor estimation of yield for most smallholders due to lack of technology, and field scale data, particularly in Egypt. Automated machine learning (AutoML) can be used to automate the machine learning workflow, including automatic training and optimization of multiple models within a user-specified time frame, but it has less attention so far. Here, we combined extensive field survey yield across wheat cultivated area in Egypt with diverse dataset of remote sensing, soil, and weather to predict field-level wheat yield using 22 Ml models in AutoML. The models showed robust accuracies for yield predictions, recording Willmott degree of agreement, (d > 0.80) with higher accuracy when super learner (stacked ensemble) was used (R² = 0.51, d = 0.82). The trained AutoML was deployed to predict yield using remote sensing (RS) vegetative indices (VIs), demonstrating a good correlation with actual yield (R² = 0.7). This is very important since it is considered a low-cost tool and could be used to explore early yield predictions. Since climate change has negative impacts on agricultural production and food security with some uncertainties, AutoML was deployed to predict wheat yield under recent climate scenarios from the Coupled Model Intercomparison Project Phase 6 (CMIP6). These scenarios included single downscaled General Circulation Model (GCM) as CanESM5 and two shared socioeconomic pathways (SSPs) as SSP2-4.5and SSP5-8.5during the mid-term period (2050). The stacked ensemble model displayed declines in yield of 21% and 5% under SSP5-8.5 and SSP2-4.5 respectively during mid-century, with higher uncertainty under the highest emission scenario (SSP5-8.5). The developed approach could be used as a rapid, accurate and low-cost method to predict yield for stakeholder farms all over the world where ground data is scarce.

Conservation agriculture (CA) practices are becoming more important in Mediterranean rainfed areas due to their potential to minimize climatic risk, reduce soil erosion, and improve soil quality and water availability. Due to minimum soil disturbance and crop residue retention, the soil environment for crop growth and development can differ between CA and conventional tillage (CT) practice. However, breeding targets for improving yield performance in CA system remain poorly explored. The objective of this study was to assess the performance of elite genotypes of barley, chickpea, lentil, and wheat grown under CA, a promising alternative agricultural practice in the Mediterranean rainfed conditions. A three-year field study, with contrasting rainfall pattern, was conducted in the International Center for Agriculture Research in the Dry Areas’s research field in Morocco to evaluate the tillage × genotype interaction and its consequence for yield performance of barley, chickpea, lentil, and wheat. Thirteen elite genotypes for each crop were planted under both CA and CT systems. Wheat and chickpea produced significantly higher grain yield (+62% for wheat and +43% for chickpea) under CA than in CT, while lentil and barley performed equally under both systems. Significant effect of tillage × genotype was more frequent for chickpea and wheat than for barley and lentil. Increased yield under CA, mainly in dry year, was associated with higher harvest index (HI). For each crop species yield was mainly influenced by rainfall amount and distribution (75–88% yield variation), and tillage × genotype was of little importance. The overall results suggest that a specific breeding program for CA in lentil, chickpea, wheat, and barley may not be efficient. Few tillage × genotype interaction, especially in dry years, indicated that breeding target on increasing HI, tolerance to drought (high yield in dry years), and potential yield (high yield in wet year) can help to improve yield performance of chickpea, lentil, and wheat genotypes in CA system. Varieties with wider adaptability considering drought tolerance, higher yield with stability, and adoption of CA practices are important in the context of the Mediterranean rainfed environment. Integrating trade-off analysis between yield potential and stability in a rainfall gradient in both CT and CA in the national certification scheme of varieties may be more efficient than developing breeding programs for each type of tillage system.

• Mechanization provided more predictable profit and return on investment of inputs. • Applying inputs by hand disassociated fertilizer from end of season yield. • Chest-mounted spreader can be stop-gap between hand distributed inputs and zero-till. • Labor efficiency was roughly doubled by using chest-mounted spreader. • Chest-mounted spreader increased within and between-field uniformity of yield.

Drylands, encompassing 41% of global land and supporting over 2 billion people, face significant challenges, including water scarcity, extreme temperatures, and soil degradation. Dryland spans vast areas of Middle East and North Africa (MENA) and Sub‐Sahara Africa (SSA) regions and poses a threat to food security and resilience. This study examines the potential of neglected and underutilized species (NUS) to improve dryland food and nutrition security, focusing on their agronomic performance, water productivity, economic viability, and nutritional benefits. Using long‐term data from FAOSTAT, USDA Food Data Central, and peer‐reviewed literature, we analyzed trends in the cultivation, yield, and nutritional contributions of 26 NUS across 22 countries in the MENA region comparing them with major staples—rice, wheat, and maize. Between 1961 and 2022, NUS crop areas in MENA fluctuated, decreasing by 7.0% since 2018 to 21.17 Mha. Despite this, NUS demonstrated superior water productivity—up to 30% higher than major cereals. For instance, sorghum and cowpea achieved 2.5 kg/m³ compared to maize (0.83 kg/m³) and wheat (0.91 kg/m³) and exhibited strong heat tolerance, withstanding temperatures of up to 42°C and 38°C, respectively. Despite a negative trade balance, NUS significantly contributed to dietary calories, surpassing wheat. A field experiment in Merchouch, Morocco, confirmed that NUS offered a higher economic value per unit than wheat, and outperformed conventional crops across key indicators. Integrating NUS into dryland farming systems can enhance food security, sustainability, and resilience to climate change. Advancing NUS requires breeding programs, tailored good agricultural practices, value addition and market linkage, supportive policies, and farmer education. Collaborative efforts among international organizations, governments, and civil society are crucial to mainstreaming NUS in agrifood systems and contributing to the diversity, sustainability, and resilience of dryland farming systems in MENA and SSA regions.