Moisture is the single most important factor that affects soil reflectance spectra, particularly for field applications. Interest in using soil VNIR spectral libraries, which are commonly based on dry ground soils, to predict soils in the intact field-moist condition (in situ VNIR) is growing. External parameter orthogonalization (EPO) has been proposed as a useful method that links dry ground VNIR models to field moist scans. The goal of this study is to test EPO on a wider set of soil properties and four different modeling techniques, namely, Partial Least Squares Regression (PLS), Artificial Neural Network (ANN), Random Forest (RF), and Support Vector Machine (SVM). We selected and scanned 352 archived soil samples from Nebraska, USA, among which 185 samples were used to develop dry ground models and the remaining 167 samples were rewetted to eight different moisture levels for EPO development and testing. Two methods to determine optimum number of EPO components, model-coupled cross validation (Model-Coupled-CV) and Wilk's Λ were also compared. The results showed that EPO minimized the variability of soil spectra induced by moisture. Results suggest a preference for the Wilk's Λ method over Model-Coupled-CV for determining the number of EPO components g, as it produced smoother transformed spectra and more parsimonious models. Among the eight soil properties tested, EPO caused significant improvements for soil Organic Carbon (OC), Inorganic Carbon (IC), and Total Carbon (TC) prediction, marginal improvement for sand and clay, and no improvement for pH, Mehlich-3 Phosphorus, and Cation Exchange Capacity. The failed EPO for the latter three properties is attributable to the poor initial dry-ground models that EPO was built upon. For OC, IC, and TC, EPO coupled effectively with all four modeling methods, with ANN and SVM outperforming the other two slightly. This adds flexibility to the implementation of EPO in predicting field moist soils. As there are increasing demands of spatially-explicit soil data in many disciplines, EPO would be an important essential part for the future in situ VNIR based proximal soil sensing technology

Core Ideas: A mid‐infrared spectral library containing 20,000+ samples was reported. Twelve soil physical and chemical properties were predicted with MIR spectra. ANN models performed better than PLSR for most soil properties. Horizon and taxonomic order as auxiliary variables improved prediction for PLSR. MIR library has the potential as an alternative to laboratory‐based analysis for OC and IC. Mid‐infrared (MIR) reflectance spectroscopy is commonly studied as a rapid and nondestructive method for predictive soil analysis under laboratory conditions. The first objective of this paper is to report an MIR spectral library based on 20,000+ soil samples collected from the United States. The second objective is to assess, using partial least squares regression (PLSR) and artificial neural networks (ANN), the performance of the library to predict 12 physical and chemical soil properties: organic carbon (OC), inorganic carbon (IC), total carbon (TC), total nitrogen (TN), clay, silt, sand, Mehlich‐3 extractable phosphorus (P), NH ₄ OAc extractable potassium (K), cation exchange capacity (CEC), total sulfur (TS), and pH. The third objective is to investigate whether the use of auxiliary variables of master horizon (HZ), taxonomic order (TAXON), and land use land cover (LULC) would improve MIR model performance. The results showed that OC, IC, TC, TN and TS were predicted most satisfactorily with R ² > 0.95 and RPD (ratio of performance to deviation) > 5.5. Soil CEC, pH, clay, silt, and sand were also predicted satisfactorily with R ² > 0.75 and RPD > 2.0. P and K were predicted poorly, with R ² < 0.4 and RPD < 1.4. The ANN models generally outperformed PLSR models, except for clay, silt and sand. Using auxiliary variables (HZ, TAXON, and LULC) to develop stratified models generally improved model performance. The HZ‐specific models showed the greatest improvements. Using an MIR spectral library for routine soil analysis would positively impact many modern applications where high spatial resolution, quantitative soil data are demanded.

Drought and heat stresses are the major abiotic stress factors detrimental to maize (Zea mays L.) production. Much attention has been directed toward plant responses to heat or drought stress. However, maize reproductive stage responses to combined heat and drought remain less explored. Therefore, this study aimed to quantify the impact of optimum daytime (30°C, control) and warmer daytime temperatures (35°C, heat stress) on pollen germination, morpho‐physiology, and yield potential using two maize genotypes (“Mo17” and “B73”) under contrasting soil moisture content, that is, 100% and 40% irrigation during flowering. Pollen germination of both genotypes decreased under combined stresses (42%), followed by heat stress (30%) and drought stress (19%). Stomatal conductance and transpiration were comparable between control and heat stress but significantly decreased under combined stresses (83% and 72%) and drought stress (52% and 47%) compared with the control. Genotype “Mo17” reduced its green leaf area to minimize the water loss, which appears to be one of the adaptive strategies of “Mo17” under stress conditions. The leaf reflectance of both genotypes varied across treatments. Vegetation indices associated with pigments (chlorophyll index of green, chlorophyll index of red edge, and carotenoid index) and plant health (normalized difference red‐edge index) were found to be highly sensitive to drought and combined stressors than heat stress. Combined drought and heat stresses caused a significant reduction in yield and yield components in both Mo17 (49%) and B73 (86%) genotypes. The harvest index of genotype “B73” was extremely low, indicating poor partitioning efficiency. At least when it comes to “B73,” the cause of yield reduction appears to be the result of reduced sink number rather than the pollen and source size. To the best of our awareness, this is the first study that showed how the leaf‐level spectra, yield, and quality parameters respond to the short duration of independent and combined stresses during flowering in inbred maize. Further studies are required to validate the responses of potential traits involving diverse maize genotypes under field conditions. This study suggests the need to develop maize with improved tolerance to combined stresses to sustain production under increasing temperatures and low rainfall conditions.

Core Ideas: We modeled ∼20,000 samples in RaCA from conterminous US to predict OC TC with VisNIR. We developed local models by four variables: RaCA Region, Land Use, Horizon, and Texture. We compared local models with the global models and PLSR versus ANN. ANN models outperform PLSR in global models, but local models improve substantially in PLSR. Overall, local PLSR models developed from Horizon and Texture are most favorable Visible and near infrared reflectance spectroscopy (VisNIR) has been used across a number of spatial scales to predict soil organic carbon (OC) content. The Rapid Carbon Assessment Project (RaCA) is a nationwide project that collected 144,000+ soil samples from across the conterminous United States for C stock mapping using VisNIR. The objective of this study was to calibrate and validate the VisNIR soil OC and total C (TC) models with ∼20,000 samples from RaCA. Models were developed with either partial least squares regression (PLSR) or the Artificial Neural Network (ANN) model. Four auxiliary variables—RaCA Region, Land Use Land Cover, Master Horizon, and Textural Class—were tested to stratify the dataset and to develop local models. The results showed that OC and TC models calibrated with ANN ( R ² > 0.94; RPD > 4.0) outperformed those of PLSR ( R ² = 0.83; RPD = 2.4). For PLSR, local models developed with all four auxiliary variables exhibited improvement in prediction accuracy, and the improvement was only marginal for ANN models. Master Horizon and Textural Class appeared to be more effective in stratifying samples into homogeneous groups because they gave an overall lower root mean squared error of prediction (RMSE _P ) for the validation samples. For the majority of the local Textural Class models, the RMSE _P of OC prediction ranged from 0.5 to 1.5%. To maximize the applicability of the RaCA spectral library on external soil samples, PLSR local models developed from Master Horizon or Textural Class appeared to be more favorable.

Drought and excess ambient temperature intensify abiotic and biotic stresses on agriculture, threatening food security and economic stability. The development of climate-resilient crops is crucial for sustainable, efficient farming. This review highlights the role of multi-omics encompassing genomics, transcriptomics, proteomics, metabolomics, and epigenomics in identifying genetic pathways for stress resilience. Advanced phenomics, using drones and hyperspectral imaging, can accelerate breeding programs by enabling high-throughput trait monitoring. Artificial intelligence (AI) and machine learning (ML) enhance these efforts by analyzing large-scale omics and phenotypic data, predicting stress tolerance traits, and optimizing breeding strategies. Additionally, plant-associated microbiomes contribute to stress tolerance and soil health through bioinoculants and synthetic microbial communities. Beyond agriculture, these advancements have broad societal, economic, and educational impacts. Climate-resilient crops can enhance food security, reduce hunger, and support vulnerable regions. AI-driven tools and precision agriculture empower farmers, improving livelihoods and equitable technology access. Educating teachers, students, and future generations fosters awareness and equips them to address climate challenges. Economically, these innovations reduce financial risks, stabilize markets, and promote long-term agricultural sustainability. These cutting-edge approaches can transform agriculture by integrating AI, multi-omics, and advanced phenotyping, ensuring a resilient and sustainable global food system amid climate change.

Global crop production is facing the challenge of a high projected demand, while the yields of major crops are not increasing at sufficient speeds. Crop breeding is an important way to boost crop productivity, however its improvement rate is partially hindered by the long crop generation cycles. If end-season crop traits such as yield can be predicted through early-season phenotypic measurements, crop selection can potentially be made before a full crop generation cycle finishes. This study explored the possibility of predicting soybean end-season traits through the color and texture features of early-season canopy images. Six thousand three hundred and eighty-three images were captured at V4/V5 growth stage over 6039 soybean plots growing at four locations. One hundred and forty color features and 315 gray-level co-occurrence matrix-based texture features were derived from each image. Another two variables were also introduced to account for location and timing differences between the images. Five regression and five classification techniques were explored. Best results were obtained using all 457 predictor variables, with Cubist as the regression technique and Random Forests as the classification technique. Yield (RMSE = 9.82, R² = 0.68), Maturity (RMSE = 3.70, R² = 0.76) and Seed Size (RMSE = 1.63, R² = 0.53) were identified as potential soybean traits that might be early predictable.