Yellow index, a primary commercial quality trait in durum wheat, shows strong environmental variation, but its climate correlates remain unresolved at the phenological scale. We analysed 871 plots, of which 740 yielded a yellow index, from 35 cultivars × 7 stations × 7 harvest years (2017–2023) in Morocco’s multi-environment trial network. Climate variables from ERA5-Land and CHIRPS were aligned to seven growing degree day (GDD)-defined phenological phases and tested against environment-level yellow index. Of 49 phase × variable combinations, 16 passed false discovery rate correction (q < 0.05). Cumulative maturation precipitation was the dominant environmental correlate (r = −0.706, p < 0.0001; a decline of 0.101 b* per millimetre of maturation rainfall), and remained so after controlling for grain-filling temperature and vapour pressure deficit (partial r = −0.682). On the 23 environments measured natively as b*, without ppm conversion, the association was stronger (r = −0.820; 0.134 b* per millimetre). A moisture-timing asymmetry emerged: vegetative-phase rainfall correlated positively with pigmentation (r = +0.508 to +0.591) while maturation rainfall correlated negatively (r = −0.706). A three-feature precipitation model was cross-validated within the network (leave-one-station-out R² = 0.73, 95% CI 0.41–0.84, falling to 0.66 when feature selection was repeated inside each fold; leave-one-year-out R² = 0.56 with station intercepts estimated from training years only). With only six stations, prediction at genuinely new sites remains to be established. Calendar-based phenology failed spatially (R² = 0.21). Finlay–Wilkinson slopes differed significantly among the 32 registered cultivars (F(31, 641) = 1.80, p = 0.0055) and ranged from 0.51 to 1.40; ‘Nachit’ had the lowest slope among well-sampled cultivars (0.74, 95% CI 0.50–0.98), though no individual cultivar remains significant once the 32 comparisons are corrected for. National and imported releases showed no evidence of a difference (p = 0.69), suggesting that pigment stability may need to be considered explicitly in selection. Network-mean yellow index declined by 0.56 b* yr⁻¹ across the seven harvest years (p = 0.009), a series too short to establish a long-term trend. Together, the rainfall–pigmentation gradient, the cross-validated model and the cultivar rankings provide a basis for anticipating pigmentation within this Moroccan network.
Accurate estimation of nitrogen (N) concentration in alfalfa (Medicago sativa L.) supports forage quality assessment and harvest scheduling. Empirical remote sensing models often overfit because of confounding effects of three-dimensional canopy structure. Incorporating structural parameters can stabilize biochemical inversions, but concurrent in situ morphological measurements are difficult to acquire at scale. We developed the Morphology-Aware Latent Embedding Network (MALENet), a hybrid framework integrating physiological and morphological mechanisms with hyperspectral data. The Decision Support System for Agrotechnology Transfer (DSSAT), coupled with the PROSAIL radiative transfer model, generated a structurally rich synthetic hyperspectral prior. A convolutional neural network (CNN) with a Squeeze-and-Excitation (SE) attention mechanism, was pre-trained on these data to distill ecophysiological knowledge into a dense latent representation. This mechanistically derived embedding was then fused with empirical hyperspectral measurements collected under diverse field conditions. Evaluation using multi-year field quadrats in the Hexi Corridor showed improved predictive performance (R² = 0.71, RMSE = 0.30%) relative to conventional machine learning and deep learning models (LASSO-ANN: R² = 0.61; CNN: R² = 0.66). Feature attribution indicated that MALENet used near-infrared multiple scattering (58.3% attribution) as a structural proxy to calibrate nitrogen concentration retrieval against the confounding effects of canopy architecture. Despite constraints imposed by sample size and PROSAIL's 1D canopy assumption, these findings demonstrate the potential of physically guided deep learning to use structural information for agricultural trait retrieval in precision forage management.
Bt maize is a genetically modified crop engineered to express insecticidal proteins derived from Bacillus thuringiensis. This technology has suppressed populations of Sesamia nonagrioides, the primary pest of maize in the Mediterranean basin, by delivering a lethal toxin dose to feeding larvae. Recent anomalies in the Girona province involving the detection of larvae within Bt fields have raised concerns regarding potential field-evolved resistance. To address this, we analyzed a dataset (2013–2025) combined with spatial and climatic monitoring during recent years (2021–2023). Results indicate that Bt technology maintains functional field efficacy, as no viable pupae were recovered from Bt plants. Climatic analyses revealed significant associations between warmer autumn conditions and increased larval pressure, consistent with an extension of the pest’s seasonal activity window. Spatial analysis of the 2022 survey further identified localized areas of elevated infestation, where damage observed in Bt stands was spatially associated with high pest pressure in nearby conventional maize. This pattern is consistent with a density-dependent spillover mechanism. These findings suggest that the recent increase in damage may reflect the interaction between environmental conditions, landscape-level pest pressure, and the limits of field-level protection rather than loss of Bt efficacy per se. The durability of transgenic crops under climate change depends on plant genetics and environmental factors. This study supports the value of incorporating landscape-scale surveillance into resistance-management strategies.
Biosurfactants are amphiphilic compounds synthesized by microorganisms such as Pseudomonas and Bacillus with considerable potential for supporting sustainable agriculture due to their multifaceted roles in plant-microbe-soil interactions. This review emphasizes current knowledge on biosurfactant physicochemical properties, including surface tension reduction, micelle formation, and metal chelation, which enhance nutrient bioavailability, soil structure, and pollutant degradation. Biosurfactants modulate rhizosphere dynamics by facilitating quorum sensing, root colonization, and the induction of systemic resistance. Their antimicrobial properties contribute as a biocontrol agent against soil-borne pathogens such as Fusarium and Phytophthora. In addition, biosurfactants alleviate abiotic stresses, such as salinity and heavy metal toxicity thereby supporting climate-resilient farming. Recent advancements in high-throughput analytical techniques and omics approaches have provide deep knowledge about molecular mechanisms through which biosurfactant influence microbial communication, plant-microbe interactions, and abiotic stress tolerance. Despite their enormous potential, the large-scale application of biosurfactants is restricted by various challenges such as soil heterogeneity, high production costs, lack of standardized protocols and assessment methodologies. This review highlights emerging conceptual frameworks that integrate system biology with precision agriculture to connect mechanistic understanding with field applications. Future research should be focus on the development of synthetic microbial consortia, advance delivery systems, and participatory field studies to optimize biosurfactant efficacy. Overall, biosurfactants can be represent as promising tool for sustainable agroecological expansion as they are capable of reducing dependency on chemical inputs and enhancing soil health in diverse farming systems.
