Gas exchange analysis is an important technique, as the reduction in yield may be related to the decreased photosynthetic activity, due to adverse climatic factors in the growing site. The hypothesis of this study was that contrasting soil water conditions result in different photosynthetic performance in soybean genotypes. Thus, our objective was to analyse the physiological capacity in soybean genotypes under field conditions with optimal soil moisture and under water deficit. The experimental design was random blocks with 10 genotypes (P1, P2, P3, P4, P5, P6, P7, P8, P9 and P10) and three replicates. Individual analysis of variance was performed in both environments (irrigated and rainfed), and a correlation network between the traits was generated. We measured the traits net photosynthesis, stomatal conductance, internal CO2 concentration, instant water‐use efficiency, instant carboxylation efficiency and grain yield. Stressed plants reduce stomatal conductance and transpiration, but increase the instant water‐use efficiency as a defence mechanism in sub‐optimal soil moisture conditions. The P6 genotype obtained better physiological capacity under irrigated conditions, while the P10 genotype showed the better performance under rainfed conditions, which makes it tolerant to water stress. Our findings can contribute to the genotype formation and parental choice steps in breeding programs aimed at obtaining both irrigation‐responsive and drought‐tolerant genotypes.

As sementes de graviola (Annona muricata) apresentam tegumento resistente e impermeável, além de substâncias inibidoras que dificultam a germinação das sementes. Assim, objetivou-se avaliar a contribuição da embebição em ácido giberélico para a superação da dormência e aumento da germinação das sementes de graviola. O delineamento experimental foi inteiramente casualizado, com cinco tratamentos e quatro repetições. As sementes foram escarificadas e em seguida embebidas por 24 horas a 25 ºC em soluções contendo 0; 50; 100; 150 e 200 mg L-1 (ppm) de ácido giberélico. Após este período de embebição as sementes foram submetidas ao teste de germinação, sendo avaliados a porcentagem de germinação, o índice de velocidade e o tempo médio de germinação. As concentrações de ácido giberélico apresentaram efeito sobre a germinação e índice de velocidade de germinação. Conclui-se que a embebição das sementes de graviola na concentração aproximada de 140 ppm de ácido giberélico auxilia na superação da dormência, aumentando a germinação e o índice de velocidade de germinação.

This study describes the use of X-ray fluorescence spectroscopy in Crotalaria ochroleuca seed technology. This work evaluated X-ray fluorescence techniques to estimate the physiological performance of different C. ochroleuca seed coat colours based on the concentration and distribution of Ca, P, K, and S in seed structures. The treatments consisted of seeds separated by coat colours (yellow, green, and red) and a control treatment (colour mix according to their natural occurrence in commercial lots), and was carried out in a completely randomized design, with four replications. The physiological performance was evaluated by analyzing the water content, germination, first germination count, germination speed index, electrical conductivity, seedling emergence, and seedling length and dry mass. X-ray fluorescence spectroscopy techniques were carried out with quantitative analyses (Ca, P, K, and S concentration in the seed coat and the whole seed) and qualitative analyses (macronutrient mapping). The EDXRF and μ-XRF techniques are efficient and promising to differentiate the physiological performance of C. ochroleuca seeds, based on the concentration and distribution of Ca, P, K, and S in different structures. Ca is predominant in the seed coat, and K, S, and P are found throughout the embryonic axis. Seeds of yellow and green coats have higher nutrients concentration and distribution in the embryonic axis, revealing high germinative capacity and physiological performance. Seeds of red coat have higher nutrients concentration in the seed coat and lower assimilation, showing less vigour, which interferes directly in the quality of commercial lots.

The current challenge of corn (Zea mays L.) crops is to reach high yield to supply the world's demand for food, especially under different fertilization regimes and using the latest technology. We hypothesized that wavelengths and vegetation indices have a linear relationship with agronomic variables in corn. Our objectives were to verify the formation of super-traits and to study the association between wavelengths and vegetation indices with agronomic traits in corn cultivated under high and low topdressing N. The experiments were carried out in two crop seasons using a randomized block design with three replicates in a factorial scheme (11 genotypes × 2 contrasting levels of N: low, 60 kg ha -1 and high, 180 kg ha -1 ). At full bloom, the spectral variables green (550 nm), red (660 nm), red-edge (735 nm), and near-infrared (790 nm) and the vegetation indices normalized difference vegetation index (NDVI), normalized difference red-edge index (NDRE), green normalized difference vegetation index (GNDVI), and soil-adjusted vegetation index (SAVI) were evaluated. Agronomic traits evaluated were leaf N, plant height, first pod height, stem diameter, cob length, rows per cob, grains per row, and grain yield. Results showed that SAVI, GNDVI, NDVI, and NDRE and red-edge and red were associated with agronomic traits in corn. The association between the agronomic traits evaluated here can be used to estimate the leaf N content and corn yield. INTRODUCTIONThe world demand for corn (Zea mays L.) consumption has increased, which has led to the need for increased production.To meet this growing demand, it is necessary to invest in crop

Using remote sensing combined with machine learning (ML) techniques is a promising approach to classify soybean cultivars. Therefore, the objectives of this study are (i) to verify which input dataset configuration (using only spectral bands, only vegetation indices, or both) is more accurate in the identification of soybean cultivars, and (ii) to verify which ML technique is more accurate in the identification of soybean cultivars. Information was extracted from five central irrigation pivots in the same region and with the same sowing date in the 2015/2016 crop year, in which each pivot was cultivated with a different cultivar, in which the cultivars used were: CV1—P98y12 RR, CV2—Desafio RR, CV3—M6410 IPRO, CV4—M7110 IPRO, and CV5—NA5909 RR. A cloud-free orbital image of the site was acquired from the Google Earth Engine platform. In addition to the spectral bands alone, a total of 13 vegetation indices were calculated. The models tested were: artificial neural networks (ANN), radial basis function network (RBF), decision tree algorithms J48 (DT) and reduced error pruning tree (REP), random forest (RF), and support vector machine (SVM). The five soybean cultivars were classified by the six-machine learning (ML) models in stratified randomized cross-validation with k-fold = 10 and 10 repetitions (100 runs for each model). After obtaining the r and MAE statistics, analysis of variance was performed considering a 6 × 3 factorial scheme (models versus inputs) with 10 repetitions (folds). The means were grouped by the Scott–Knott test at 5% probability. The spectral bands were the most accurate among the tested inputs in the identification of soybean cultivars. ANN was the most accurate model in identifying soybean cultivars.