This study aims to calibrate and validate the generic crop model (CROPGRO-Soybean) and estimate the soybean yield, considering simulations with different sowing times for the current period (1990–2013) and future climate scenario (2014–2030). The database used came from observed data, nine climate models of CORDEX (Coordinated Regional climate Downscaling Experiment)-Africa framework and MERRA (Modern Era Retrospective-Analysis for Research and Applications) reanalysis. The calibration and validation data for the model were acquired in field experiments, carried out in the 2009/2010 and 2010/2011 growing seasons in the experimental area of the International Institute of Tropical Agriculture (IITA) in Angónia, Mozambique. The yield of two soybean cultivars: Tgx 1740-2F and Tgx 1908-8F was evaluated in the experiments and modeled for two distinct CO₂ concentrations. Our model simulation results indicate that the fertilization effect leads to yield gains for both cultivars, ranging from 11.4% (Tgx 1908-8F) to 15% (Tgx 1740-2Fm) when compared to the performance of those cultivars under current CO₂ atmospheric concentration. Moreover, our results show that MERRA, the RegCM4 (Regional Climatic Model version 4) and CNRM-CM5 (Centre National de Recherches Météorologiques – Climatic Model version 5) models provided more accurate estimates of yield, while others models underestimate yield as compared to observations, a fact that was demonstrated to be related to the model’s capability of reproducing the precipitation and the surface radiation amount.

Leaf area estimation is a very important indicator in studies related to plant anatomy, morphology and physiology, and in many cases, it is a fundamental criterion to understand plant response to input conditions. Although there are leaf area prediction models have been produced for some plant species, a leaf area estimation model has not yet been developed for the zucchini. The objective of this paper was to determine the leaf area based on destructive and non-destructive methods for zucchini. The accuracy of measurement methods was evaluated and compared to determinations of the leaf area by the scanning integration method (LICOR equipment LI 3100C), considered as standard procedure. Non-destructive methods consisted of digital photography and measurement of leaf dimensions (width and length) based on ImageJ software. The destructive methods used were a) leaf area integrator LI-3100C, b) determination of leaf mass and c) weighing of leaf discs punched from the leaves. According to statistical parameters that evaluate the performance of the analyzed methods: determination coefficient (R2), Pearson (r) correlation coefficient, Willmott agreement index (d) and Camargo and Sentelhas performance index (c) the parameters presented values higher than 0.8820, classifying the methods as very good, whereas the modeling efficiency index (NSE) and the percentage of bias (PBIAS) also classified the methods as very good (0.87≤NSE≤0.99; -4.80≤PBIAS≤1.40), except the ImageJ method without correction (NSE=0.77; PBIAS = -22.70).

Water resources management aims to solve problems arising from intensive use of water. The proper management of this resource is based on understanding water availability, often using information from hydrometric stations; flow data is the most important information. The availability of information on river flows is often insufficient for all regions of interest. A technique called hydrological regionalization can be an alternative for obtaining information on streamflow. The objective of this study was to review the main regionalization techniques used, their advantages and limitations, as well as perspectives for the future. Traditional and widely used methods for forecasting hydrological variable, such as spatial proximity and multiple linear regression, were addressed, as well as new technologies, such as the geostatistical approach, techniques using volume balance in watersheds based on remote sensing products, and machine learning techniques. These techniques allow working with several physical characteristics of basins, generally ensuring better performances than the multiple linear regression. Further advancements in this area of knowledge are expected shortly, as the great potential of machine learning has been explored only to a small extent for hydrological regionalization purposes.

The practice of using different photo-selective nets, associated with the technique of fertigation, represents a new agrotechnological concept that aims to promote specific physiological responses for growth and production, which are regulated by light. The aims of the study were to evaluate the effect of photo-selective nets and potassium concentrations in the fertigation solution on the horticultural performance of Costus lasius. The study was conducted at the Universidade de São Paulo, ESALQ, Piracicaba, SP, Brazil. Three experiments were carried out separately to evaluate five potassium concentrations in the fertigation solution (40, 80, 120, 160 and 200 mg L-1) under three colours of photo-selective nets (red, blue and black) in a randomized block design, with five repetitions. The variables plant height and numbers of leaves, stems and flower buds were evaluated. The data were submitted to the joint analysis of experiments. The association between the K+ concentration of 200 mg L-1 and the black photo-selective net results in higher stem and inflorescence production.