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Alfalfa forage is highly important forage for cattle feeding worldwide. This crop is capable to achieve high biomass yields during many years, due to its persistence to frequent cuttings. The great implications for animal performance require of early pre-harvest estimations for applying practices in real-time. In addition, meteorological conditions and photoperiod have a strong influence in its growth and productivity. The remote sensing techniques have allowed the estimation of crop yields through no-invasive and rapid evaluations. Thus, the main objective of this study is to develop mathematical models for predicting biomass yield of 64 fields of alfalfa located in Navarre (Spain) at three phenological stages (mid vegetative, mid-veg; late vegetative, late-veg and early bloom). For that, during two years (2020 and 2021), number of cuttings throughout the years, meteorological data and statistical values (mean and median) of vegetation indices (VIs) calculated from Sentinel-2 satellite images were considered as predictors; then, multilinear regressions (MLR) were developed to estimate biomass yield and finally the most suitable model was applied in validation set. The models generated achieved an R2 of 32%, 57% and 70%, along to a nRMSE of 0.27, 0.17 and 0.13 for mid-veg, late-veg and early bloom, respectively. The number of cuttings and meteorological variables were identified as important predictors into the models, because the seasonal changes of photoperiod and temperature can modify the alfalfa growth. In addition, indices related to biomass, soil cover and water content were the better selected predictors for mid-veg, late-veg and early bloom, respectively. Prediction models for biomass yield developed in the current work could be relevant for agricultures and decision makers for to optimize the managements practices in field with considerable time before the cutting period.

Wheat grain quality characteristics have experienced increasing attention as a central factor affecting wheat end-use products quality and human health. Nonetheless, in the last decades a reduction in grain quality has been observed. Therefore, it is central to develop efficient quality-related phenotyping tools. In this sense, one of the most relevant wheat features related to grain quality traits is grain nitrogen content, which is directly linked to grain protein content and monitorable with remote sensing approaches. Moreover, the relation between nitrogen fertilization and grain nitrogen content (protein) plays a central role in the sustainability of agriculture. Both aiming to develop efficient phenotyping tools using remote sensing instruments and to advance towards a field-level efficient and sustainable monitoring of grain nitrogen status, this paper studies the efficacy of various sensors, multispectral and visible red–greenblue (RGB), at different scales, ground and unmanned aerial vehicle (UAV), and phenological stages (anthesis and grain filling) to estimate grain nitrogen content. Linear models were calculated using vegetation indices at each sensing level, sensor type and phenological stage. Furthermore, this study explores the up-scalability of the best performing model to satellite level Sentinel-2 equivalent data. We found that models built at the phenological stage of anthesis with UAV-level multispectral cameras using red-edge bands outperformed grain nitrogen content estimation (R2 = 0.42, RMSE = 0.18%) in comparison with those models built with RGB imagery at ground and aerial level, as well as with those built with widely used ground-level multispectral sensors. We also demonstrated the possibility to use UAV-built multispectral linear models at the satellite scale to determine grain nitrogen content effectively (R2 = 0.40, RMSE = 0.29%) at actual wheat fields.

Reliable methods for estimating wheat grain yield before harvest could help improve farm management and, if applied on a regional level, also help identify spatial factors that influence yield. Regional grain yield can be estimated using conventional methods, but the typical process is complex and labor-intensive. Here we describe the development of a streamlined approach using publicly accessible agricultural data, field-level yield, and remote sensing data from Sentinel-2 satellite to estimate regional wheat grain yield. We validated our method on wheat croplands in Navarre in northern Spain, which features heterogeneous topography and rainfall. First, this study developed stepwise multilinear equations to estimate grain yield based on various vegetation indices, which were measured at various phenological stages in order to determine the optimal timings. Second, the most suitable model was used to estimate grain yield in wheat parcels mapped from Sentinel-2 satellite images. We used a supervised pixel-based random forest classification and the estimates were compared to government-published post-harvest yield statistics. When tested, the model achieved an R2 of 0.83 in predicting grain yield at field level. The wheat parcels were mapped with an accuracy close to 86% for both overall accuracy and compared to official statistics. Third, the validated model was used to explore potential relationships of the mapped per-parcel grain yield estimation with topographic features and rainfall by using geographically weighted regressions. Topographic features and rainfall together accounted for an average for 11 to 20% of the observed spatial variation in grain yield in Navarre. These results highlight the ability of our method for estimating wheat grain yield before harvest and determining spatial factors that influence yield at the regional scale.

Although consumers appreciate the genetic diversity among tomato landraces, traditional varieties have been displaced from commercial agriculture. Their cultivation through organic farming in natural parks can contribute to their resurgence. With this aim, we developed a participatory plant breeding (PPB) program in Collserola Natural Park (Barcelona, Spain) to promote the conservation of the Mando landrace and to obtain new varieties adapted to local organic farms, Postprint (published version)