The estimation of the reference evapotranspiration is an important factor for hydrological studies, design and management of irrigation systems, among others. The Penman Monteith equation presents high precision and accuracy in the estimation of this variable. However, its use becomes limited due to the large number of required meteorological data. In this context, the Hargreaves-Samani equation could be used as alternative, although, for a better performance a local calibration is required. Thus, the aim was to compare the calibration process of the Hargreaves-Samani equation by linear regression, by adjustment of the coefficients (A and B) and exponent (C) of the equation and by combinations of the two previous alternatives. Daily data from 6 weather stations, located in the state of Minas Gerais, from the period 1997 to 2016 were used. The calibration of the Hargreaves-Samani equation was performed in five ways: calibration by linear regression, adjustment of parameter “A”, adjustment of parameters “A” and “C”, adjustment of parameters “A”, “B” and “C” and adjustment of parameters “A”, “B” and “C” followed by calibration by linear regression. The performances of the models were evaluated based on the statistical indicators mean absolute error, mean bias error, Willmott’s index of agreement, correlation coefficient and performance index. All the studied methodologies promoted better estimations of reference evapotranspiration. The simultaneous adjustment of the empirical parameters “A”, “B” and “C” was the best alternative for calibration of the Hargreaves-Samani equation.

Estimation of reference evapotranspiration (ETo) is very relevant for water resource management. The Penman-Monteith (PM) equation was proposed by the Food and Agriculture Organization (FAO) as the standard method for estimation of ETo. However, this method requires various weather data, such as air temperature, wind speed, solar radiation and relative humidity, which are often unavailable. Thus, the objective of this study was to compare the performance of multivariate adaptive regression splines (MARS) and alternative equations, in their original and calibrated forms, to estimate daily ETo with limited weather data. Daily data from 2002 to 2016 from 8 Brazilian weather stations were used. ETo was estimated using empirical equations, PM equation with missing data and MARS. Four data availability scenarios were evaluated as follows: temperature only, temperature and solar radiation, temperature and relative humidity, and temperature and wind speed. The MARS models demonstrated superior performance in all scenarios. The models that used solar radiation showed the best performance, followed by those that used relative humidity and, finally, wind speed. The models based only on air temperature had the worst performance.

The objective of this study was to characterize banana tree endophytic bacteria at genus and species level and to determine the metabolic reactions associated with the nitrogen transformations. The identification at genus and species levels was performed using the partial sequencing of the rDNA 16S region. The assimbyotic nitrogen fixation, the reduction of nitrate and the production of urease were in vitro evaluated. The DNA of the bacterial isolates was also amplified to verify the presence of the nifH, nirK and nirS regions. Biochemical tests were performed in a complete randomized design; the treatments consisted of 39 bacterial isolates with three replications. Sequence analysis enabled the identification of four genera: Bacillus, Rhizobium, Klebsiella and Enterobacter. The Bacillus genus occurred more frequently, nine species were identified. By evaluating the results of biochemical tests, it was observed that three isolates showed multiple abilities: growth in NFb medium, nitrate reduction and production of urease. The isolates belong to the genus Bacillus and of the species subtilis, thuringienses and amyloliquefaciens. Approximately 12.5% of the isolates amplified the region corresponding to the nifH gene, 7.5% amplified gene nirK and 3.9% amplified the nirS gene. Endophytic bacteria evaluated in the present study showed in vitro activity for urease, nitrate reductase enzymes, however, relevant nitrogenase activity was not observed.

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Methodologies for imposing stress and reproducible results are a bottleneck for breeding programmes, and this is due to the lack of consensus between the existing methodologies. The aim of the present study was to propose and validate a new methodology for imposing water deficit in soybean that allows the identification of water deficit-tolerant genotypes, at different harvest times and phenological stages.The methodology was based on the construction of a water retention curve in the soil to determine the water stresses that indicate the field capacity and the permanent wilt point and, thus, define the water regime in the conditions of control and stress. Seven trials were carried out to validate the methodology. In trials 1, 2, 3, 4, 5 and 6, the water deficit was imposed in the reproductive phase and the components of production were evaluated. In addition to these variables, leaf water potential was evaluated in trial 6. In trial 7, the plants were subjected to water deficit in the vegetative phase and the morphological traits were evaluated. The efficiency of the methodology was confirmed by the distinction between the conditions of control and stress, affirmed by the statistical differences in most of the traits evaluated in the reproductive and vegetative phases.

The objective of this study was to evaluate the performance of four machine learning models, as well as multitask learning, to predict soybean root variables from simpler variables, under two water availability conditions. In order to do so, 100 soybean cultivars were conducted in a greenhouse under a control condition and a stress condition. Aerial part and root variables were evaluated. The machine learning models used to predict complex root variables were artificial neural network (ANN), random forest (RF), extreme gradient boosting (EGBoost) and support vector machine (SVM). A linear model was used for comparison purposes. Multitask learning was employed for ANN and RF. In addition, feature importance was defined using RF and XGBoost algorithms. All the machine learning models performed better than the linear model. In general, SVM had the greatest potential for the prediction of most of the root variables, with better values of RMSE, MAE and R2. Dry weight of the aerial part and root volume exhibited the greatest importance in the predictions. The models developed using multitask learning performed similarly to the ones conventionally developed. Finally, it is concluded that the machine learning models evaluated can be used to predict root variables of soybean from easily measurable variables, such as dry weight of the aerial part and root volume.

-The aim of this study was to quantify the influence of some environmental and genotypic variables on genotype by environment (GE) interactions in soybean. Mean yield data from eighteen test genotypes in eleven experiments in Goiás State, Brazil, were used and analyzed by AMMI method. To identify environmental and genotypic variables related to the GE interaction, simple linear correlations were estimated between the means of these variables and the scores of the first AMMI principal component of the interactions. Successive simple linear regression analyses were also carried stepwise, in order to relate the GE interaction of each genotype to the observed environmental factors. The environmental factors that influenced the GE interactions most were altitude, maximum temperature, end-of-cycle disease complex, total rainfall and soil fertility.The genotypic variables days to maturity and reaction to end-of-cycle disease complex were most associated with GE interactions.

ABSTRACT:The reference evapotranspiration (ET o ) is an important component for determining the water requirements of the crops. In order to estimate this variable accurately, the Food and Agriculture Organization (FAO) proposed the Penman-Monteith equation, however, this demands a large number of meteorological data, which restricts its use. In this context, this study compares the performance of the Penman-Monteith equation using only measured air temperature (PMT) and the Hargreaves-Samani (HS) equation with the performance of the multivariate adaptive regression splines (MARS) technique for the daily ET o estimation with only air temperature data. For the study, daily meteorological data from 2002 to 2016 were used. The data were collected from weather stations located in Florianópolis-SC, Manaus-AM and Petrolina-PE, being these selected in order to capture different climatic conditions. MARS models were developed for each weather station and the PMT e HS equations were locally calibrated. The performances of the original and calibrated equations and MARS models were evaluated based on the statistical indices root mean square error, mean absolute error, mean bias error and coefficient of determination. The ET o estimated by the Penman-Monteith method with full data was used as reference for the development of the MARS models, calibration of the equations and for the performance evaluation of the models under study. The calibration of the HS and PMT equations promoted better performances in relation to the original equations, improving the methods accuracy. The MARS technique presented good performance, outperforming the original and calibrated PMT and HS equations, with lower error values and higher coefficient of determination, and can be considered as an alternative to empirical methods.