Salazar Brock (judgeformat46)
Throughout the 2020-2021 maize cropping season, a two-year experiment was undertaken. It simulated hail events at distinct maize developmental stages (8th leaf, flowering, milky, and dough) via a 0-40% defoliation gradient of damage intensities using purpose-built prototype machines. The study's outcomes showcased the accuracy of both sensors in estimating LAI within a damaged, non-standard canopy, needing only the extinction coefficient to be calibrated. k ( ) When performing parametric estimations. With 2020's data, the calibration procedure was carried out in this case. k ( ) In terms of values, Sentinel-2 shows 059 and the UAV sensor shows 037. In 2021, the validation process demonstrated the UAV sensor's superior accuracy, with the highest correlation (R) being observed. Among the metrics assessed, the root-mean-square error (RMSE) stood at 0.71, distinctly lower than the observed value of 0.86. The k ( ) The sensor-specific nature of the value explained the discrepancies in NDVI retrieval, stemming from the differing spatial operational scales of the two sensors. neuronal signaling inhibitors Across various defoliation severities in damaged maize canopies, NDVI proved a reliable tool for parametric estimation of leaf area index. Substantial agreement was achieved between the parametric method and the Sentinel-2 biophysical process, leading to a reduction in underestimations in the generated LAI, improving its accuracy. 101007/s11119-023-09993-9 hosts the supplementary materials for the online edition. Available at 101007/s11119-023-09993-9 is the supplementary material accompanying the online version. Agricultural field dimensions and configurations directly influence agricultural management strategies over time and location, impacting farm profitability, the richness of biodiversity in the fields, and environmental performance. The utilization of large, conventional agricultural machinery often proves less than efficient in the treatment of small, irregularly configured fields. The study posited that self-governing agricultural machinery would enable the cultivation of small, non-rectangular plots of land profitably, thus safeguarding agricultural biodiversity and other environmental advantages. This study, drawing upon the Hands Free Hectare (HFH) demonstration project, devised algorithms for quantifying field times (hours per hectare) and field efficiencies (%) in UK grain-oil-seed farms, acknowledging the effects of field size and configuration, and exploring four distinct equipment sets. The impact of field size and shape on the technical and economic output of all equipment sets was considerable. Autonomous machinery, however, proved adept at profitably managing small (1 hectare) rectangular and non-rectangular fields. Equipment of all sizes and types in small fields demanded more time, though the size and form of fields had minimal impact on HFH equipment sets. The HFH linear programming model's findings indicate a reduction in wheat production costs due to autonomous machinery, from 15 to 29 USD/ton on small rectangular fields and from 24 to 46 USD/ton on non-rectangular ones. This contrasts with the non-profitability of larger 112kW and 221kW human-operated equipment on small fields. Sensitivity analysis demonstrates that farms utilizing autonomous systems exhibited a remarkable ability to thrive in conditions marked by increasing wage rates and decreasing labor availability, a striking contrast to the difficulties encountered by farms employing conventional equipment. Small-field applications of autonomous machinery demonstrate technical and economic viability, potentially leading to enhanced biodiversity and improved environmental standards. The online version has supplementary materials available for download at 101007/s11119-023-10016-w. 101007/s11119-023-10016-w is the location for the supplementary material
