Use of cover crops in an integrated agricultural system can reduce demand of inorganic phosphorus (P) fertilizers, where the subsequent crops can take up P accumulated in cover crops biomass after the decomposition. In this research we hypothesized that some cover crops can take up P from less labile fractions and recycle it back to the soil through plant residues resulting in better P use efficiency of the system; cover crops are capable of P uptake from subsurface layers which leads to the accumulation of this P on the surface after the decomposition of their residues; and cover crop species respond differently to distinct inorganic P sources. To examine these hypotheses, a field experiment was conducted over nine successive years in South Brazil. The experimental treatments were established as a split-plot randomized block design, in a 3 × 6 factorial scheme, considering three P treatments [no-P, single superphosphate (SSP), and rock phosphate (RP)] as main plot and six cover crop treatments (common vetch, white lupin, fodder radish, ryegrass, black oat, and a fallow) as subplots. Soil samples were collected from the depths of 0–5, 5–10, and 10–15 cm and analyzed by Hedley P fractionation. In P-unfertilized cropping system, the amounts of labile and mod-labile P fractions were not modified by cover crops related to fallow. When inorganic P fertilizers were applied, the amount of labile and mod-labile P pools under fallow were higher than under cover crops in 5–10 cm depth. Black oat and common vetch cycled more labile P under SSP and RP, respectively. Common vetch and ryegrass resulted in the highest accumulation of organic P on the surface under SSP and RP, respectively. Black oat was capable to change P extracted by 1.0 M HCl to more labile forms. Fodder radish showed the highest P uptake in comparison with other cover crops. The higher P balance efficiency of the system was achieved under SSP in comparison with RP application but it seems that cover crops are more effective at improving the efficiency under RP compared to SSP.

The spatial and temporal variability of crop parameters are fundamental in precision agriculture. Remote sensing of crop canopy can provide important indications on the growth variability and help understand the complex factors influencing crop yield. Plant biomass is considered an important parameter for crop management and yield estimation, especially for grassland and cover crops. A recent approach introduced to model crop biomass consists in the use of RGB (red, green, blue) stereo images acquired from unmanned aerial vehicles (UAV) coupled with photogrammetric softwares to predict biomass through plant height (PHT) information. In this study, we generated prediction models for fresh (FBM) and dry biomass (DBM) of black oat crop based on multi-temporal UAV RGB imaging. Flight missions were carried during the growing season to obtain crop surface models (CSMs), with an additional flight before sowing to generate a digital terrain model (DTM). During each mission, 30 plots with a size of 0.25 m² were distributed across the field to carry ground measurements of PHT and biomass. Furthermore, estimation models were established based on PHT derived from CSMs and field measurements, which were later used to build prediction maps of FBM and DBM. The study demonstrates that UAV RGB imaging can precisely estimate canopy height (R² = 0.68–0.92, RMSE = 0.019–0.037 m) during the growing period. FBM and DBM models using PHT derived from UAV imaging yielded R² values between 0.69 and 0.94 when analyzing each mission individually, with best results during the flowering stage (R² = 0.92–0.94). Robust models using datasets from different growth stages were built and tested using cross-validation, resulting in R² values of 0.52 for FBM and 0.84 for DBM. Prediction maps of FBM and DBM yield were obtained using calibrated models applied to CSMs, resulting in a feasible way to illustrate the spatial and temporal variability of biomass. Altogether the results of the study demonstrate that UAV RGB imaging can be a useful tool to predict and explore the spatial and temporal variability of black oat biomass, with potential use in precision farming.