Soybean Phenology Research Articles

Spectral indices with different spatial resolutions in recognizing soybean phenology.

The aim of the present research was to evaluate the efficiency of different vegetation indices (VI) obtained from satellites with varying spatial resolutions in discriminating the phenological stages of soybean crops. The experiment was carried out in a soybean cultivation area irrigated by central pivot, in Balsas, MA, Brazil, where weekly assessments of phenology and leaf area index were carried out. Throughout the crop cycle, spectral data from the study area were collected from sensors, onboard the Sentinel-2 and Amazônia-1 satellites. The images obtained were processed to obtain the VI based on NIR (NDVI, NDWI and SAVI) and RGB (VARI, IV GREEN and GLI), for the different phenological stages of the crop. The efficiency in identifying phenological stages by VI was determined through discriminant analysis and the Algorithm Neural Network-ANN, where the best classifications presented an Apparent Error Rate (APER) equal to zero. The APER for the discriminant analysis varied between 53.4% and 70.4% while, for the ANN, it was between 47.4% and 73.9%, making it not possible to identify which of the two analysis techniques is more appropriate. The study results demonstrated that the difference in sensors spatial resolution is not a determining factor in the correct identification of soybean phenological stages. Although no VI, obtained from the Amazônia-1 and Sentinel-2 sensor systems, was 100% effective in identifying all phenological stages, specific indices can be used to identify some key phenological stages of soybean crops, such as: flowering (R1 and R2); pod development (R4); grain development (R5.1); and plant physiological maturity (R8). Therefore, VI obtained from orbital sensors are effective in identifying soybean phenological stages quickly and cheaply.

Temporal variability in soybean sowing and harvesting according to K-means and silhouette scores

Understanding the impact of seasonal variations on soybean productivity is crucial for optimizing agricultural practices. Given the influence of climatic factors such as temperature and rainfall on crop phenology, this study aims to analyze the effects of sowing and harvesting soybean cultivars in different seasons. This research investigates whether sowing soybeans in November and December leads to varying outcomes in terms of productivity and morphological characteristics, focusing on identifying the most stable cultivars across different climatic conditions. The study employed a comprehensive methodology, including data standardization, statistical tests (Levene, Shapiro-Wilk, ANOVA, Wilcoxon), and K-means clustering, to analyze 40 soybean cultivars across two seasons. Statistical preprocessing ensured data accuracy, while clustering helped identify cultivars with consistent responses to climatic changes. All computational analyses were performed using Python in the Google Colab environment. The findings revealed no significant difference in productivity between the two seasons, despite variations in temperature and rainfall. However, the moisture content of the grains (MTG) showed significant differences, influenced by higher rainfall in March and April and increased temperatures in December. K-means clustering highlighted SYN2282IPRO as the most stable cultivar and 77HO111I2X-GUAPORÉ as the most sensitive to climatic changes. The results emphasize the need for careful cultivar selection based on specific adaptability to seasonal variations. This study underscores the potential of computational tools like K-means clustering in agricultural optimization, offering a data-driven approach to selecting stable soybean cultivars. The adaptable methodology can be tailored to different geographical regions, soil types, and climate conditions, enhancing its relevance and applicability. These insights contribute to a better understanding of the complex interactions between climatic variables and soybean phenology, providing a foundation for improving agricultural practices in the face of climate change.

A Method for Estimating Soybean Sowing, Beginning Seed, and Harvesting Dates in Brazil Using NDVI-MODIS Data

Brazil, as a global player in soybean production, contributes about 35% to the world’s supply and over half of its agricultural exports. Therefore, reliable information about its development becomes imperative to those who follow the market. Thus, this study estimates three phenological stages of soybean crops (sowing, beginning seed, and harvesting dates), identifying spatial–temporal patterns of soybean phenology using phenological metric extraction techniques from Normalized Difference Vegetation Index (NDVI) time-series data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. Focused on the state of Paraná, this study validates the methodology using reference data from the Department of Rural Economics (DERAL). Subsequently, the model was applied to the major Brazilian soybean area cultivation. The results demonstrate strong agreement between the phenological estimates and reference data, showcasing the reliability of phenological metrics in capturing the stages of the soybean cycle. This study represents the first attempt, to the best of our knowledge, to correlate the vegetative peak of soybeans with the beginning seed stage at a large scale within Brazilian territory. Amidst the urgent need for the accurate estimation of agricultural crop phenological stages, particularly considering extreme weather events threatening global food security, this research emphasizes the continual importance of advancing techniques for soybean monitoring.

Oviposition patterns of primary lepidopteran defoliators in soybean and the impact on structured refuge recommendations.

Transgenic Bt technology in soybean, with plants expressing Cry1Ac, has been adopted as an insect pest management tool. It was first adopted in large areas of South America and Asia in 2013. The risk of resistance in target pests to this technology demands insect resistance management (IRM) programs. In Brazil, a structured refuge (area of non-Bt soybean) planted adjacent to the Bt soybean crop has been an important IRM recommendation, particularly for the primary lepidopteran defoliators Anticarsia gemmatalis (Lepidoptera: Erebidae) and Chrysodeixis includens (Lepidoptera: Noctuidae). The overall goal of this study was to validate IRM recommendations to Bt soybean. The objectives were to document the impact of soybean phenology, cultivar choice and non-Bt soybean defoliation on moth oviposition. In addition, a mark-release-recapture study estimated the dispersal capacity of these species. Five field experiments per species were performed for 3 years. Our results revealed an increase in A. gemmatalis and C. includens oviposition, respectively, on Bt plants as a consequence of the difference in plant growth stage at the time of oviposition. Defoliation of non-Bt plants significantly increased the oviposition preference of both moth species for Bt plants. The mark-release-recapture experiment indicated an average dispersal distance of ~300 m from the release point for A. gemmatalis, with maximum recapture at 1000 m. Overall, our findings emphasize the importance of planting synchronization of Bt soybean and the structured refuge. In addition, when operational aspects in large soybean areas challenge this recommendation, the priority should be for planting the refuge area first. This approach will minimize the impact of selective oviposition of A. gemmatalis and C. includens. © 2024 Society of Chemical Industry.

CRONOSOJA: a daily time-step hierarchical model predicting soybean development across maturity groups in the Southern Cone

Abstract Abstract. Accurate prediction of phenology is the most critical aspect for the development of models aimed at estimating seed yield, particularly in species that exhibit variable sensitivity to environmental factors throughout the cycle and among genotypes. With this purpose, we evaluated the phenology of 34 soybean varieties in field experiments located in Argentina, Uruguay and Paraguay. Experiments covered a broad range of maturity group (MG)s (2.2–6.8), sowing dates (SDs) (from spring to summer) and latitude range (24.9–35.6 °S), thus ensuring a wide range of thermo-photoperiodic conditions during the growing season. Based on the observed data, daily time-step models were developed and tested, first for each genotype, and then across MGs. We identified base temperatures specific for different developmental phases and an extra parameter for calculating the photoperiod effect after the R1 stage (flowering). Also, an optimum photoperiod length for each MG was found. Model selection showed that the determinants of phenology across MGs were mainly affecting the duration of vegetative and early reproductive phases. Even so, early phases of development were better predicted than later ones, particularly in locations with cool growing seasons, where the model tended to overestimate their duration. In summary, we have constructed a soybean phenology model that simulates phenology accurately across various geographic locations and sowing dates. The model’s process-based approach has resulted in root mean square errors ranging from 5.8 to 9.5 days for different developmental stages. The final model was made available at http://cronosoja.agro.uba.ar.

Genetic analysis of genotype-specific parameters in the DSSTA-CROPGRO-soybean phenology simulation model via a multi-GWAS method

ContextGenotype-specific parameters (GSP) are the fundamental elements for phenotypic prediction in the DSSAT-CROPGRO soybean phenology simulation model (DCS-PSM). Yet, it is unclear about the quantitative relationship between GSP and genomics, which limits the crop models used for breeding research. ObjectiveExploring the genetic composition of GSP related to four soybean phenological traits in DCS-PSM for providing a basis for whether GSP are genetic coefficient. Highlighting the breeding value of DCS-PSM from a genetic perspective and strengthening its genetic understanding, which could support a further crop model-assisted molecular breeding. MethodsWe conducted a multi-genome-wide association study (GWAS) method by intrgrating the single-locus with multi-locus GWAS models for GSP associated quantitative trait nucleotides (QTN) detection. And the heritability, functional annotation of QTN and genotype-environment interaction (GEI) effect were further analyzed. Results(i). All GSP had high broad-sense heritability, ranging form 88.20–98.80%. (ii). Multi-locus GWAS significantly increased the QTN detection efficiency, the GSP associated QTN detected by multi-GWAS method contributed 22.11–50.25% phenotypic variation explained (PVE) of GSP. Besides, the GSP associated QTN accounted for a high PVE of four phenological traits, ranging from 54.25% to 74.75%. (iii). Most QTN overlapped with the function-known genes which regulate soybean phenology through photoperiod-temperature pathways. (iv). GSP associated QTN could reveal the GEI effect and contributed 1.45–6.27% PVE of soybean phenology. ConclusionsThis study indicated that the genetic factor was the primary source of GSP’s variation, and the multi-GWAS method could be an alternative way for the genetic analysis of GSP. Phenology-related GSP had genetic potential and could be regarded as genetic coefficients and breeding targets.

Coupling the CSM-CROPGRO-Soybean crop model with the ECOSMOS Ecosystem Model – An evaluation with data from an AmeriFlux site

Process-based simulation models, such as land surface (LSM) and crop models, are useful tools for studying the impacts of the environment, management and genotype on agricultural production. LSM are capable of simulating crop development and growth, but not in as much detail as the processes embedded in crop models. Crop models, on the other hand, do not usually have the ability to solve the surface water, energy and carbon balances, which can help to assess the feedbacks between climate and agricultural systems. The goals of this study were first to implement the well-known Cropping System Model (CSM)-CROPGRO-Soybean model of the Decision Support System for Agrotechnology Transfer (DSSAT) into the Ecosystem Model Simulator (ECOSMOS) LSM and then to evaluate this coupling to simulate surface fluxes and crop performance of irrigated and rainfed soybean agroecosystems with comprehensive data from an AmeriFlux site. After model coupling, simulations were benchmarked against multiple flux (carbon dioxide, energy, and water), phenology, growth, and yield observations obtained from field scale experiments across 14 seasons from irrigated and rainfed fields near Mead, Nebraska. Calibration of the physiological parameters of ECOSMOS and of the CROPGRO-Soybean genetic coefficients was conducted. Common metrics were employed to assess model performance. Overall, the coupled model reproduced both the magnitude and seasonal patterns of the energy balance, carbon dioxide exchange, evapotranspiration and soil water balance. The crop model coupling into ECOSMOS preserves the known high skill of the CROPGRO-Soybean model in simulating soybean phenology and growth dynamics. The simulated yields were consistent with the observations at field level. Although the ECOSMOS-CROPGRO-Soybean model is sufficiently robust to simulate surface fluxes and crop performance of soybean agroecosystems at field scale for the environments of eastern Nebraska, further evaluation for a wide range of climatic and soil conditions and management practices is warranted.

Soybean phenological changes in response to climate warming in three northeastern provinces of China

ContextSoybean plays an important role in agricultural production in three northeastern provinces of China; furthermore, climate change, particularly temperature and rainfall changes, can significantly regulate soybean phenological periods. Research QuestionCurrent research on the spatial and temporal characteristics of soybean phenology and its relationship with climate change in the three northeastern provinces of China is still inadequate. MethodsIn this study, phenological and meteorological data from 26 agrometeorological stations in northeastern China were used to evaluate temporal and spatial changes and their correlations with soybean temperature, rainfall, phenological stage, and growth duration over the long term in three northeastern provinces. ResultsOn average, 22 of the 26 stations showed increases in temperature, and 15 stations showed increases in rainfall during the survey stage. All of soybean growth nodes were delayed, except R8, which advanced by 0.28 days, and all soybean growth durations were shortened except for during the V3-R1 period. In addition, temperature was negatively correlated with growth duration, while rainfall was positively correlated with growth duration. Further, the spatial and temporal distribution of temperature and rainfall showed that an increase in temperature led to an increase in thermal conditions and a significant northward and eastward expansion of the cumulative temperature zone, with the most significant increase in temperature occurring in most areas of Heilongjiang, followed by Liaoning. ConclusionsSoybean planting practices in the three eastern provinces should be adjusted to medium- and late-maturing varieties, which can adapt to climate change and mitigate its effects during the phenological period, due their tolerance to high heat. SignificanceThese findings fill a knowledge gap for understanding the relationship between soybean phenological changes and climate change in the three northeastern provinces.

Optimization of Soybean Phenological Models Using Historical Data of Breeding and Comparison of Optimization Methods

Optimization of Soybean Phenological Models Using Historical Data of Breeding and Comparison of Optimization Methods

A comparison of soybean maturity groups for phenology, seed yield, and seed quality components between eastern Ontario and southern Manitoba

The expansion of soybean [ Glycine max (L.) Merr.] production onto the Canadian Prairies has resulted in new environmental constraints that affect soybean phenology, seed yield, and seed quality. This study examined these factors for 10 soybean cultivars differing in maturity group (MG) rating from 000.9 to 1.3 in southern Manitoba (MB) and eastern Ontario (ON). Detailed climate and phenological data collected at both locations were used to explore the environmental factors and differences in measurements among MG and between locations. In MB, more time was spent in vegetative growth and less time developing flowers and seeds than in ON. The longer vegetative growth stage in MB resulted in more leaves produced on the main stem at flowering than in ON. The leaf appearance rate was consistent between locations and the rate of phenological development in the vegetative stage was greater in ON because of its warmer mean temperature and shorter photoperiod. In MB, seed yield was positively correlated with precipitation in all growth stages and had a strong correlation with precipitation during reproductive development. In ON, increasingly warmer temperatures during reproductive development had the greatest influence on seed yield, particularly in the seed development stage. This study is a baseline for soybean phenology, seed yield, and seed quality components for early MG and will aid in the optimization of soybean breeding and production in the Canadian Prairies.

Predator-Pest Dynamics of Arthropods Residing in Louisiana Soybean Agroecosystems.

Simple SummarySoybean agroecosystems in the Midsouth support an assorted insect community. Louisiana soybean is heavily managed by growers, and the subsequent effect on residing arthropod populations is not well understood. For sustainable management of soybean, evaluations need not only focus on problematic pests, but also the resident natural enemies. This study addresses the compositional and temporal abundance of prominent insects in Louisiana soybean, both pest and beneficial. Pest and natural enemy populations were monitored to delineate potential associations. We found that the pest community was dominated by those that fed on soybean through piercing-sucking mouthparts. Predator populations were comprised mainly of spiders and big-eyed bugs. Pest and predator populations were similarly more abundant later in the soybean reproductive period. A moderate positive linear relationship was observed between pest and predator communities indicating a temporal correlation. Here we present essential information about the pest and natural enemy communities residing in Louisiana.Over the past two decades, management practices within Louisiana soybean production have shifted. Successful application of an integrated pest management (IPM) strategy requires an understanding of how these changes have affected predator-pest dynamics. Surveys monitoring foliage-foraging arthropod populations in soybean took place across six locations within Louisiana over six years (2012–2014 and 2015–2018). Temporal associations of pest groups, defoliating and piercing-sucking, and predator groups relating to soybean phenology were observed. Additionally, soybean maturity groups (III, IV, and V) were also evaluated to delineate potential differences. Results indicated higher abundances of piercing-sucking pests compared to defoliating pests across both datasets (2012–2014 and 2015–2018). Pest groups were more abundant in later soybean reproductive periods, mainly attributed to Chrysodeixis includens and Piezodorus guildinii. Predator populations were mainly comprised of Araneae and Geocoridae throughout the survey periods. From 2015 to 2018, soybean growth had a significant effect on total predator abundance with more predators present at the pod-fill and soybean maturity stage. Correlations between total pest abundance and total predators exhibited a moderate positive linear relationship. Soybean maturity groups only influenced piercing-sucking pest abundance, with later maturing groups (IV and V) having higher numbers. Thus, control tools and tactics aimed at controlling late season pests should be modified to avoid reducing predator populations.

Janela ótima de semeadura da soja ajustada à variabilidade climática baseada em El Nino-Oscilação Sul utilizando-se modelagem agrometeorológica

ABSTRACT Determining the optimal sowing window (OSW) based on climate variability associated with El Nino-Southern Oscillation (ENSO) can provide valuable information for agricultural planning in the tropics. This study aimed to calibrate, evaluate and apply the CROPGRO-Soybean model for determining the OSW across the ENSO phases for soybean-producing areas in the Pará State, northern Brazil. First, the model was calibrated and evaluated using experimental data collected in the field, between 2006 and 2009. In this process, the model estimates showed a good agreement with the observed data for soybean phenology, growth and yield, demonstrating potential to simulate the crop yield in this part of the Amazon. After calibration, the model was used in the seasonal mode to simulate 18 planting dates, over 39 years and in three locations. The simulated yields were divided into three ENSO phases. The set of sowing dates that showed a high frequency (> 80 %) of yields above 3,500 kg ha−1 integrated the OSW for each location and ENSO phases. The OSW duration differed between locations and ENSO phases, varying more during La Nina than El Nino events. However, regardless of the location or ENSO phase, late sowing was more suitable, because, besides favoring a greater frequency of good climate conditions for the development, growth and high yields, it also favors a lower risk of rainfall during the harvest period.

Phenological Changes of Soybean in Response to Climate Conditions in Frigid Region in China over the Past Decades

Plant phenology becoming a focus of current research worldwide is a sensitive indicator of global climate change. To understand observed soybean phenology and explore its climatic determinants in frigid region (Northeast China and northeast in Inner Mongolia), we studied the phenological changes of soybean [Glycine max (L.) Merr.] for the frigid region during 1981–2017, then analyzed the contribution of major causal climate factors to phenology based on multiple stepwise regression. Altogether, the average temperature from sowing to maturity (WGP) was significant increasing, accumulated precipitation and sunshine hours were decreasing. More than 50% of observations showed delays in sowing, emergence and maturity stage and short durations of sowing to flowering (VGP), flowering to maturity (RGP) and sowing to maturity (WGP). The late sowing was getting the following phenological timing backward, but the flowering and maturity delaying trends were much less than that of sowing timing due to the warming accelerated growth of soybean. Detailed analysis indicated mean temperature and accumulated precipitation of the 1–3 months immediately preceding the mean emergence, flowering and maturity dates influenced the phenological timing in higher latitude areas (HLJ and FL), while in JL and LN, accumulated precipitation and sunshine hours(replacing mean temperature) were the climatic determinants. These results brought light the importance of research and policy to support strategies for adaptation to local condition under the climate change.

Long juvenility trait: A vehicle for commercial utilization of soybean (Glycine max) in lower latitudes

AbstractSoybean, a short‐day plant, has its origin in higher latitudes of China, and its commercial cultivation remained confined to areas >22°N till 1970s due to precocious flowering accompanied with reduced biomass in lower latitudes. This review summarizes the effect of short days on soybean phenology, discovery of a novel long juvenile (LJ) trait, its role in breaking the latitudinal boundary for soybean production, identification of genes and impact of LJ trait on world soybean production. E6, J and E9 major genes governing the trait have been characterized and cloned. LJ varieties are late in flowering and maturity and successful in long rainfall regime of Brazil. Availability of vast vacant fallows of Cerrado has been another factor for utilization of this trait in Brazil. Recent advances for developing short‐duration LJ varieties, for limited rainfall areas and its suitability to existing cropping systems by combining it with photoinsensitivity, have been reviewed. Based on the available data from lower latitudes of Brazil, the contribution of the LJ trait is estimated to 23.7% of the total world production.

Shortened key growth periods of soybean observed in China under climate change

Phenology is an important indicator of global climate change. Revealing the spatiotemporal characteristics of crop phenology is vital for ameliorating the adverse effects of climate change and guiding regional agricultural production. This study evaluated the spatiotemporal variability of soybean’s phenological stages and key growth periods, and assessed their sensitivity to key climatic factors, utilizing a long-term dataset (1992–2018) of soybean phenology and associated meteorological data collected at 51 stations across China. The results showed that (1) during the soybean growing seasons from 1992 to 2018, the average temperature (0.34 ± 0.09 ℃ decade−1) and cumulative precipitation (6.66 ± 0.93 mm decade−1) increased, but cumulative sunshine hours (− 33.98 ± 1.05 h decade−1) decreased. (2) On a national scale, dates of sowing, emergence, trifoliate, anthesis, and podding of soybean were delayed, while the maturity date showed an advancing trend. The vegetative growth period (− 0.52 ± 0.24 days decade−1) and whole growth period (− 1.32 ± 0.30 days decade−1) of soybean were shortened, but the reproductive growth period (0.05 ± 0.26 days decade−1) was slightly extended. Trends in soybean phenological stages and key growth periods diverged in regions. Soybean phenological stages were delayed in Huang-Huai-Hai soybean zone, whereas advanced in southern soybean zone. Moreover, the key growth periods were greatly shortened in northern soybean zone. (3) In general, the sensitivity of soybean key growth periods to temperature was negative, whereas those to precipitation and sunshine hours differed among regions. In particular, most phenological stages were negatively sensitive to sunshine hours. Our results will provide scientific support for decision-making in agricultural production practices.

Associating Site Characteristics With Distributions of Pestiferous and Predaceous Arthropods in Soybean.

Although site-specific pest management has the potential to decrease control costs and environmental impact associated with traditional pest management tactics, the success of these programs relies on the accurate characterization of arthropod distributions within a crop. Because potential correlation of insect counts with remotely sensed field attribute data could help to decrease the costs associated with and need for fine-scale spatial sampling, we chose to determine which within-field variables would be informative of soybean arthropod counts in an attempt to move toward site-specific pest management in this crop. Two soybean fields were grid-sampled for pestiferous and predaceous arthropods, plant productivity estimates, and abiotic variable characterization in 2017-2018. Negative binomial, zero-inflated models were used to estimate presence and counts of soybean arthropod taxa based on normalized difference vegetation index (NDVI), soybean plant height, soil electrical conductivity (ECa), elevation, and calendar week. Among all variables, calendar week was the most reliable predictor of arthropod counts, as it was a significant predictor for a majority of all taxa. Additionally, counts for a majority of pestiferous taxa were significantly associated with distance from the field edge, elevation, soybean plant height, and NDVI. Although site-specific pest management has the potential for reduced management inputs and increased profitability over conventional management (i.e., whole-field) practices, management zones must first be clearly defined based on the within-field variability for the variables of interest. If site-specific pest management practices are to be applied in soybean, calendar week (and associated soybean phenology), soybean plant height (and associated elevation), and NDVI may be useful for describing the distributions of pests, such as kudzu bug, Megacopta cribraria (Hemiptera: Plataspidae) (Fabricius), green cloverworm, Hypena scabra (Lepidoptera: Erebidae) (Fabricius), velvetbean caterpillar, Anticarsia gemmatalis (Lepidoptera: Erebidae) (Hübner), and soybean looper, Chrysodeixis includens (Lepidoptera: Noctuidae) (Walker).

Intercontinental prediction of soybean phenology via hybrid ensemble of knowledge-based and data-driven models

AbstractABSTRACTThe timing of crop development has significant impacts on management decisions and subsequent yield formation. A large intercontinental dataset recording the timing of soybean developmental stages was used to establish ensembling approaches that leverage both knowledge-based, human-defined models of soybean phenology and data-driven, machine-learned models to achieve accurate and interpretable predictions. We demonstrate that the knowledge-based models can improve machine learning by generating expert-engineered features. The collection of knowledge-based and data-driven models was combined via super learning to both improve prediction and identify the most performant models. Stacking the predictions of the component models resulted in a mean absolute error of 4.41 and 5.27 days to flowering (R1) and physiological maturity (R7), providing an improvement relative to the benchmark knowledge-based model error of 6.94 and 15.53 days, respectively, in cross-validation. The hybrid intercontinental model applies to a much wider range of management and temperature conditions than previous mechanistic models, enabling improved decision support as alternative cropping systems arise, farm sizes increase and changes in the global climate continue to accelerate.

High-Resolution Soybean Yield Mapping Across the US Midwest Using Subfield Harvester Data

Cloud computing and freely available, high-resolution satellite data have enabled recent progress in crop yield mapping at fine scales. However, extensive validation data at a matching resolution remain uncommon or infeasible due to data availability. This has limited the ability to evaluate different yield estimation models and improve understanding of key features useful for yield estimation in both data-rich and data-poor contexts. Here, we assess machine learning models’ capacity for soybean yield prediction using a unique ground-truth dataset of high-resolution (5 m) yield maps generated from combine harvester yield monitor data for over a million field-year observations across the Midwestern United States from 2008 to 2018. First, we compare random forest (RF) implementations, testing a range of feature engineering approaches using Sentinel-2 and Landsat spectral data for 20- and 30-m scale yield prediction. We find that Sentinel-2-based models can explain up to 45% of out-of-sample yield variability from 2017 to 2018 (r2 = 0.45), while Landsat models explain up to 43% across the longer 2008–2018 period. Using discrete Fourier transforms, or harmonic regressions, to capture soybean phenology improved the Landsat-based model considerably. Second, we compare RF models trained using this ground-truth data to models trained on available county-level statistics. We find that county-level models rely more heavily on just a few predictors, namely August weather covariates (vapor pressure deficit, rainfall, temperature) and July and August near-infrared observations. As a result, county-scale models perform relatively poorly on field-scale validation (r2 = 0.32), especially for high-yielding fields, but perform similarly to field-scale models when evaluated at the county scale (r2 = 0.82). Finally, we test whether our findings on variable importance can inform a simple, generalizable framework for regions or time periods beyond ground data availability. To do so, we test improvements to a Scalable Crop Yield Mapper (SCYM) approach that uses crop simulations to train statistical models for yield estimation. Based on findings from our RF models, we employ harmonic regressions to estimate peak vegetation index (VI) and a VI observation 30 days later, with August rainfall as the sole weather covariate in our new SCYM model. Modifications improved SCYM’s explained variance (r2 = 0.27 at the 30 m scale) and provide a new, parsimonious model.

Relationship between stink bug populations and soybean (Glycine max L.) phenology

The populations of phytophagous stink bugs may vary with cultivar and crop management. The aim of this study was to know the population dynamics of stink bugs Euschistus heros, Nezara viridula, and Piezodorus guildinii at different soybean phenological stages. Samples of each stink bug species were collected from three soybean (Syn1059, P97R01, and M7110IPRO) cultivars conducted in two seasons. For three cultivars and two seasons, E. heros was the most abundant, followed by P. guildinii and N. viridula. The soybean phenology influenced stink bug population dynamics, mainly in the reproductive stages. For soybean cultivars, Syn1059 and M7110IPRO were high colonized by stink bugs, while colonization in P97R01 was low suggesting susceptible differences between cultivars. The number of collected insects was positively related between reproductive soybean phonological stages, suggesting that stink bugs population can compromise crop production.

Biochar addition alleviate the negative effects of drought and salinity stress on soybean productivity and water use efficiency

BackgroundEnvironmental stress is a crucial factor restricting plant growth as well as crop productivity, thus influencing the agricultural sustainability. Biochar addition is proposed as an effective management to improve crop performance. However, there were few studies focused on the effect of biochar addition on crop growth and productivity under interactive effect of abiotic stress (e.g., drought and salinity). This study was conducted with a pot experiment to investigate the interaction effects of drought and salinity stress on soybean yield, leaf gaseous exchange and water use efficiency (WUE) under biochar addition.ResultsDrought and salinity stress significantly depressed soybean phenology (e.g. flowering time) and all the leaf gas exchange parameters, but had inconsistent effects on soybean root growth and WUE at leaf and yield levels. Salinity stress significantly decreased photosynthetic rate, stomatal conductance, intercellular CO2 concentration and transpiration rate by 20.7, 26.3, 10.5 and 27.2%, respectively. Lower biomass production and grain yield were probably due to the restrained photosynthesis under drought and salinity stress. Biochar addition significantly enhanced soybean grain yield by 3.1–14.8%. Drought stress and biochar addition significantly increased WUE-yield by 27.5 and 15.6%, respectively, while salinity stress significantly decreased WUE-yield by 24.2%. Drought and salinity stress showed some negative interactions on soybean productivity and leaf gaseous exchange. But biochar addition alleviate the negative effects on soybean productivity and water use efficiency under drought and salinity stress.ConclusionsThe results of the present study indicated that drought and salinity stress could significantly depress soybean growth and productivity. There exist interactive effects of drought and salinity stress on soybean productivity and water use efficiency, while we could employ biochar to alleviate the negative effects. We should consider the interactive effects of different abiotic restriction factors on crop growth thus for sustainable agriculture in the future.