Silicon (Si) can increase phosphorus (P) use efficiency (PUE) by increasing P availability in the soil and altering P metabolism in the plant, thus resulting in improved yield under low soil P conditions. The objective was to evaluate the effect of P (10, 50, 100, and 200 mg dm⁻³) and Si (0, 50, and 200 mg dm⁻³) interrelationship on P and Si uptake by plants, forms of P in leaves and tubers, plant growth, and tuber yield of potato plants. Silicon supply increased organic and inorganic P in the leaves. At low soil P rates, Si also increased organic P in the tubers. However, alterations in the P metabolism of potato plants with Si supply did not promote significant changes in dry matter (DM) production and tuber yield. Phosphate fertilization increased growth and yield of plants. Silicon uptake by the plants were also increased by phosphate fertilization with soil Si supply.

The productive potential of new peanut cultivars has increased over the years in relation to old cultivars, especially when compared with ones with upright growth habit. Thus, the requirement for macronutrients for these new cultivars may also have increased, making the existing fertilizer recommendation tables obsolete, thus increasing the need for further studies measuring the real macronutrient requirements of these new peanut cultivars. Our study aimed to evaluate the growth patterns and the macronutrient absorption rate throughout the biological cycle of three modern runner peanut cultivars, as well as the potential for producing dry matter, pods, and kernels, and their respective macronutrient accumulations. The experimental design was a randomized complete block with split-plots and nine replications. The experimental plots consisted of three peanut cultivars (IAC Runner 886, IAC 505, and IAC OL3), and subplots consisted of nine plant samplings (14, 28, 42, 56, 70, 84, 105, 126, and 147 days after emergence (DAE)). Our results showed that modern peanut cultivars presented nutrient accumulation around 30 to 40 days earlier than older cultivars, as well as increasing the uptake by K and Ca. IAC 505 absorbed higher amounts of macronutrients and resulted in greater dry matter production compared with IAC OL3 and IAC Runner 886. Our study demonstrated that the most appropriate time for plants to find greater availability of nutrients in the soil is 70 to 84 DAE, in addition to highlighting the need for updates on nutritional recommendations for higher yields of modern peanut cultivars.

Using UAV and multispectral images has contributed to identifying field variability and improving crop management through different data modeling methods. However, knowledge on application of these tools to manage peanut maturity variability is still lacking. Therefore, the objective of this study was to compare and validate linear and multiple linear regression with models using artificial neural networks (ANN) for estimating peanut maturity under irrigated and rainfed conditions. The models were trained (80% dataset) and tested (20% dataset) using results from the 2018 and 2019 growing seasons from irrigated and rainfed fields. In each field, plant reflectance was collected weekly from 90 days after planting using a UAV-mounted multispectral camera. Images were used to develop vegetation indices (VIs). Peanut pods were collected on the same dates as the UAV flights for maturity assessment using the peanut maturity index (PMI). The precision and accuracy of the linear models to estimate PMI using VIs were, in general, greater in irrigated fields with R² > 0.40 than in rainfed areas, which had a maximum R² value of 0.21. Multiple linear regressions combining adjusted growing degree days (aGDD) and VIs resulted in decreased RMSE for both irrigated and rainfed conditions and increased R² in irrigated areas. However, these models did not perform successfully in the test process. On the other hand, ANN models that included VIs and aGDD showed accuracy of R² = 0.91 in irrigated areas, regardless of using Multilayer Perceptron (MLP; RMSE = 0.062) or Radial Basis Function (RBF; RMSE = 0.065), as well as low tendency (1:1 line). These results indicated that, regardless of the ANN architecture used to predict complex and non-linear variables, peanut maturity can be estimated accurately through models with multiple inputs using VIs and aGDD. Although the accuracy of the MLP or RBF models for irrigated and rainfed areas separately was high, the overall ANN models using both irrigated and rainfed areas can be used to predict peanut maturity with the same precision.