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Monitoring of soybean genotypes is important because of intellectual property over seed technology, better management over seed genetics, and more efficient strategies for its agricultural production process. This paper aims at spectrally classifying soybean genotypes submitted to diverse water availability levels at different phenological stages using leaf-based hyperspectral reflectance. Leaf reflectance spectra were collected using a hyperspectral proximal sensor. Two experiments were conducted as field trials: one experiment was at Embrapa Soja in the 2016/2017, 2017/2018, and 2018/2019 cropping seasons, where ten soybean genotypes were grown under four water conditions; and another experiment was in the experimental farm of Unoeste University in the 2018/2019 cropping season, where nine soybean genotypes were evaluated. The spectral data collected was divided into nine spectral datasets, comprising single and multiple cropping seasons (from 2016 to 2019), and two contrasting crop-growing environments. Principal component analysis, applied as an indicator of the explained variance of the reflectance spectra among genotypes within each spectral dataset, explained over 94% of the spectral variance in the first three principal components. Linear discriminant analysis, used to obtain a model of classification of each reflectance spectra of soybean leaves into each soybean genotype, achieved accuracy between 61% and 100% in the calibration procedure and between 50% and 100% in the validation procedure. Misclassification was observed only between genotypes from the same genetic background. The results demonstrated the great potential of the spectral classification of soybean genotypes at leaf-scale, regardless of the phenological stages or water status to which plants were submitted.

Soybean is one of the most common grain crops worldwide, representing an important protein and oil source. Although genetic variability in the chemical composition of grains is seen in soybean, the mean levels of proteins have remained stagnant or, in some cases, have decreased over time, arousing concern in the agricultural industry. Furthermore, environmental conditions influence the chemical composition of grains. Thus, the present study evaluated the effect of water deficit (WD) induced at the vegetative period (vegetative stress (VS)) and reproductive period (reproductive stress (RS)) on the protein and oil contents of grains in different soybean genotypes. Yield and its components were evaluated to evaluate the interrelation of these traits. The experiment was completed over three crop seasons under field conditions in Londrina, Paraná (PR), Brazil. WD was induced using rainout shelters and then stress treatments with irrigated and non-irrigated conditions were compared. WD negatively affected yield and its components. All evaluated genotypes showed similar responses for oil and protein contents under different water conditions. Higher protein content and lower oil content were observed in grains under RS. Such a relationship was not equally established under VS. Additionally, negative relationships between protein and oil content and between protein content and yield were confirmed.

Soybean farming has faced several losses in productivity due to drought events in the last few decades. However, plants have molecular mechanisms to prevent and protect against water deficit injuries, and transcription factors play an important role in triggering different defense mechanisms. Understanding the expression patterns of transcription factors in response to water deficit and to environmental diurnal changes is very important for unveiling water deficit stress tolerance mechanisms. Here, we analyzed the expression patterns of ten APETALA2/Ethylene Responsive Element Binding-like (AP2/EREB-like) transcription factors in two soybean genotypes (BR16: drought-sensitive; and Embrapa 48: drought-tolerant). According to phylogenetic and domain analyses, these genes can be included in the DREB and ERF subfamilies. We also analyzed a GmDRIP -like gene that encodes a DREB negative regulator. We detected the up-regulation of 9 GmAP2/EREB -like genes and identified transcriptional differences that were dependent on the levels of the stress applied and the tissue type analyzed (the expression of the GmDREB1F -like gene, for example, was four times higher in roots than in leaves). The GmDRIP-like gene was not induced by water deficit in BR16 during the longest periods of stress, but was significantly induced in Embrapa 48; this suggests a possible genetic/molecular difference between the responses of these cultivars to water deficit stress. Additionally, RNAseq gene expression analysis over a 24-h time course indicates that the expression patterns of several GmDREB -like genes are subject to oscillation over the course of the day, indicating a possible circadian regulation.

The upcoming Landsat Next will provide more frequent land surface observations at higher spatial and spectral resolutions that will greatly benefit the agricultural sector. Early modeling of the upcoming Landsat Next products for soybean yield prediction is essential for long-term satellite monitoring strategies. In this context, this article evaluates the contribution of Landsat Next’s improved spectral resolution for soybean yield prediction under varying levels of water availability. Ground-based hyperspectral data collected over five cropping seasons at the Brazilian Agricultural Research Corporation were resampled to Landsat Next spectral resolution. The spectral dataset (n = 384) was divided into calibration and external validation datasets and investigated using three strategies for soybean yield prediction: (1) using the reflectance from each spectral band; (2) using existing and new vegetation indices developed based on three general equations: Normalized Difference Vegetation Index (NDVI-like), Band Ratio Vegetation Index (RVI-like), and Band Difference Vegetation Index (DVI-like), replacing the traditional spectral bands by all possible combinations between two bands for index calculation; and (3) using a partial least squares regression (PLSR) model composed of all Landsat Next spectral bands, in comparison to PLSR models using Landsat OLI and Sentienel-2 MSI bands. The results show the distribution of the new spectral bands over the most prominent changes in leaf reflectance due to water deficit, particularly in the visible and shortwave infrared spectrum. (1) Band 18 (centered at 1610 nm) had the highest correlation with yield (R2 = 0.34). (2) A new vegetation index, called Normalized Difference Shortwave Vegetation Index (NDSWVI), is proposed and calculated from bands 19 and 20 (centered at 2028 and 2108 nm). NDSWVI showed the best performance (R2 = 0.37) compared to traditional existing and new vegetation indices. (3) The PLSR model gave the best results (R2 = 0.65), outperforming the Landsat OLI and Sentinel-2 MSI sensors. The improved spectral resolution of Landsat Next is expected to contribute to improved crop monitoring, especially for soybean crops in Brazil, increasing the sustainability of the production systems and strengthening food security in Brazil and globally.

By the end of the decade, the world population is expected to increase by nearly one billion people, posing challenges to meeting global food demand. In this scenario, soybean production is projected to increase by 18% within this decade. Despite being the largest soybean producer, responsible for over 40% of soybeans produced worldwide, drought events often impair Brazilian production. The goals of the present research were to quantify soybean yield losses related to drought in Brazil from 1973 to 2023 at national, state, and municipal levels and to assess the spatial distribution of losses across the production areas. The hypothesis investigated is that year-to-year variations in soybean yield are closely related to water availability, considering that crop management practices are constant from year to year, while increments in soybean yield across time (more than five years) relate tightly to better crop management practices and breeding improvements. Thus, quantifying year-to-year yield losses might demonstrate the effects of water availability on soybean yield. Yield data from the 1976/1977 to 2022/2023 crop seasons from the 26 states and the Federal District came from the National Supply Company, while the Brazilian Institute of Geography and Statistics supplied yield data for the 1973/1974 to 2020/2021 crop seasons from 1998 municipalities with more than 14 crop seasons. Soybean drought yield losses were calculated for each cropping season individually at the municipal, state, and national levels, based on the deviation in the observed yield to the corresponding maximum yield in the five-year window, considering that crop management practices and genetics represent a regular increment in soybean yield, which means that production practices improved over time and deviations from year to year are mainly related to drought occurrence. Annual soybean yield loss (expressed in tons, USD, and percentage), frequency of yield loss, and severity of yield loss were calculated at national, state, and municipal levels for each cropping season. The Standardized Precipitation Index (SPI), acquired from the Brazilian Weather Forecast and Climate Studies Center at the National Space Research Institute, was used as a qualitative indicator to corroborate the assessed soybean yield losses related to drought. The results demonstrate yield losses in more than 50% of crop seasons at the national level, with a similar frequency across the five decades, albeit with lower severities in the last 30 years. The Central–West region was more stable than the South region, with yield losses of up to 74%. In five decades, yield losses related to drought events stand at 11.65%, corresponding to 280 million tons or USD 152 billion (considering the average soybean price in 2022 at the Chicago Board of Trade). At the municipal level, analogous behavior was observed across time and space. The outcomes from the present research might subsidize public and corporative policies related to agricultural zoning, farm loan programs, crop insurance contracts, and food security, contributing to higher agricultural, environmental, economic, and social sustainability.

Soybean grain yield has regularly been impaired by drought periods, and the future climatic scenarios for soybean production might drastically impact yields worldwide. In this context, the knowledge of soybean yield is extremely important to subsidize government and corporative decisions over technical issues. This paper aimed to predict grain yield in soybean crop grown under different levels of water availability using reflectance spectroscopy and partial least square regression (PLSR). Field experiments were undertaken at Embrapa Soja (Brazilian Agricultural Research Corporation) in the 2016/2017, 2017/2018 and 2018/2019 cropping seasons. The data collected were analyzed following a split plot model in a randomized complete block design, with four blocks. The following water conditions were distributed in the field plots: irrigated (IRR), non-irrigated (NIRR) and water deficit induced at the vegetative (WDV) and reproductive stages (WDR) using rainout shelters. Soybean genotypes with different responses to water deficit were distributed in the subplots. Soil moisture and weather data were monitored daily. A total of 7216 leaf reflectance (from 400 to 2500 nm, measured by the FieldSpec 3 Jr spectroradiometer) was collected at 24 days in the three cropping seasons. The PLSR (p ≤ 0.05) was performed to predict soybean grain yield by its leaf-based reflectance spectroscopy. The results demonstrated the highest accuracy in soybean grain yield prediction at the R5 phenological stage, corresponding to the period when grains are being formed (R2 ranging from 0.731 to 0.924 and the RMSE from 334 to 403 kg ha−1—7.77 to 11.33%). Analyzing the three cropping seasons into a single PLSR model at R5 stage, R2 equal to 0.775, 0.730 and 0.688 were obtained at the calibration, cross-validation and external validation stages, with RMSE lower than 634 kg ha−1 (13.34%). The PLSR demonstrated higher accuracy in plants submitted to water deficit both at the vegetative and reproductive periods in comparison to plants under natural rainfall or irrigation.

Drought is one of the most stressful environmental factor causing yield and economic losses in many soybean-producing regions. In the last decades, transcription factors (TFs) are being used to develop genetically modified plants more tolerant to abiotic stresses. Dehydration responsive element binding (DREB) and ABA-responsive element-binding (AREB) TFs were introduced in soybean showing improved drought tolerance, under controlled conditions. However, these results may not be representative of the way in which plants behave over the entire season in the real field situation. Thus, the objectives of this study were to analyze agronomical traits and physiological parameters of (1Ab58), (1Bb2193), and (1Ea2939) GM lines under irrigated (IRR) and non-irrigated (NIRR) conditions in a field experiment, over two crop seasons and quantify transgene and drought-responsive genes expression. Results from season 2013/2014 revealed that line 1Ea2939 showed higher intrinsic water use and leaf area index. Lines 1Ab58 and 1Bb2193 showed a similar behavior to wild-type plants in relation to chlorophyll content. Oil and protein contents were not affected in transgenic lines in NIRR conditions. Lodging, due to plentiful rain, impaired yield from the 1Ea2939 line in IRR conditions. qPCR results confirmed the expression of the inserted TFs and drought-responsive endogenous genes. No differences were identified in the field experiment performed in crop season 2014/2015, probably due to the optimum rainfall volume during the cycle. These field screenings showed promising results for drought tolerance. However, additional studies are needed in further crop seasons and other sites to better characterize how these plants may outperform the WT under field water deficit.

Although the information on the Normalized Difference Vegetation Index (NDVI) in plants under water deficit is often obtained from sensors attached to satellites, the increasing data acquisition with portable sensors has wide applicability in agricultural production because it is a fast, nondestructive method, and is less prone to interference problems. Thus, we carried out a set of experiments to investigate the influence of time, spatial plant arrangements, sampling size, height of the sensor and water regimes on NDVI readings in different soybean cultivars in greenhouse and field trials during the crop seasons 2011/12, 2012/13 and 2013/14. In experiments where plants were always evaluated under well-watered conditions, we observed that 9 a.m. was the most suitable time for NDVI readings regardless of the soybean cultivar, spatial arrangement or environment. Furthermore, there was no difference among NDVI readings in relation to the sampling size, regardless of the date or cultivar. We also observed that NDVI tended to decrease according to the higher height of the sensor in relation to the canopy top, with higher values tending to be at 0.8 m, but with no significant difference relative to 1.0 m—the height we adopted in our experiments. When different water regimes were induced under field conditions, NDVI readings measured at 9 a.m. by using a portable sensor were successful to differentiate soybean cultivars with contrasting responses to drought.

Soybean (Glycine max (L.) Merrill) is highly sensitive to water deficit, particularly during the vegetative phase, when morphological and metabolic plasticity support continued growth and photosynthetic efficiency. We applied eleven water regimes, from full irrigation (W100) to total water withholding (W0), to plants grown under controlled conditions. After 14 days, we quantified morphophysiological, biochemical, leaf optical, gas exchange, and chlorophyll a fluorescence traits. Drought induces significant reductions in leaf area, biomass, pigment pools, and photosynthetic rates (A, gs, ΦPSII) while increasing the levels of oxidative stress markers (electrolyte leakage, ROS) and proline accumulation. OJIP transients and JIP test metrics revealed reduced electron-transport efficiency and increased energy dissipation for many parameters under severe stress. Principal component analysis (PCA) clearly separated those treatments. PC1 captured growth and water status variation, whereas PC2 reflected photoprotective adjustments. These data show that progressive drought limits carbon assimilation via coordinated diffusive and biochemical constraints and that the accumulation of proline, phenolics, and lignin is associated with osmotic adjustment, antioxidant buffering, and cell wall reinforcement under stress. The combined use of hyperspectral sensors, gas exchange, chlorophyll fluorescence, and multivariate analyses for phenotyping offers a rapid, nondestructive diagnostic tool for assessing drought severity and the possibility of selecting drought-resistant genotypes and phenotypes in a changing stress environment.

Soybean (Glycine max) is one of the major crops worldwide and flooding stress affects the production and expansion of cultivated areas. Oxygen is essential for mitochondrial aerobic respiration to supply the energy demand of plant cells. Because oxygen diffusion in water is 10,000 times lower than in air, partial (hypoxic) or total (anoxic) oxygen deficiency is important component of flooding. Even when oxygen is externally available, oxygen deficiency frequently occurs in bulky, dense or metabolically active tissues such as phloem, meristems, seeds, and fruits. In this study, we analyzed conserved and divergent root transcriptional responses between flood-tolerant Embrapa 45 and flood-sensitive BR 4 soybean cultivars under hypoxic stress conditions with RNA-seq. To understand how soybean genes evolve and respond to hypoxia, stable and differentially expressed genes were characterized structurally and compositionally comparing its mechanistic relationship. Between cultivars, Embrapa 45 showed less up- and more down-regulated genes, and stronger induction of phosphoglucomutase (Glyma05g34790), unknown protein related to N-terminal protein myristoylation (Glyma06g03430), protein suppressor of phyA-105 (Glyma06g37080), and fibrillin (Glyma10g32620). RNA-seq and qRT-PCR analysis of non-symbiotic hemoglobin (Glyma11g12980) indicated divergence in gene structure between cultivars. Transcriptional changes for genes in amino acids and derivative metabolic process suggest involvement of amino acids metabolism in tRNA modifications, translation accuracy/efficiency, and endoplasmic reticulum stress in both cultivars under hypoxia. Gene groups differed in promoter TATA box, ABREs (ABA-responsive elements), and CRT/DREs (C-repeat/dehydration-responsive elements) frequency. Gene groups also differed in structure, composition, and codon usage, indicating biological significances. Additional data suggests that cis-acting ABRE elements can mediate gene expression independent of ABA in soybean roots under hypoxia.