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Agriculture is weather dependent, and changes in climate can have a drastic impact on our ability to feed, fuel, and clothe the world's population. Climate change is causing more frequent and unprecedented extreme weather events that are already negatively affecting agriculture. We need to assess the effects of extreme temperatures and rainfall on agriculture. Patterns of short‐term extreme weather events, such as elevated temperatures, flooding, and strong winds, are not predictable enough to design field experiments around. Process‐based crop and soil simulation models allow us to explore new management options and thus provide whole‐system‐based knowledge and management guides for different locations over variable climate conditions. By using crop simulation models, researchers can test different adaptation strategies and assess their effectiveness in reducing the impacts of climate change on agricultural production. In this paper, we discuss the development of crop models and how they have been used to assess the effects of a changing climate on agricultural productivity and propose methods for agriculture to adapt to those changes. We describe potential applications of crop models to assess regional issues such as irrigation demand, greenhouse gas emissions, and policy decisions. Better understanding of how weather and climate forecasts at various scales are provided and the reliability of these forecasts is important for using crop models as a planning tool. Different approaches for simulating long‐term climate change impacts on crop yield and seasonal yield forecasting are discussed. The use of ensemble models to better assess climate change impacts is also discussed. Core Ideas: Assessment of climate change impacts on agriculture requires a systems approach.Agriculture is weather dependent, and changes in climate can have a drastic impact on food production.Crop models can extend the results of physical experiments to better assess long‐term climate change effects on agriculture.Crop models are critical in assessing climate change impacts and developing adaptation and mitigation strategies. [ABSTRACT FROM AUTHOR]

Undergraduate hands‐on research can foster innovation and critical thinking among young scholars to delve into real‐world challenges. Specifically, exploring the critical nexus between water usage and agricultural yield, can foster academic growth and holds the key to addressing global food security in an era of increasing environmental constraints, where students can unlock insights crucial to enhancing crop yield and sustainability. Investigating the intricate relationship between water management and crop productivity through undergraduate research is exemplified in this article. Undergraduate students acquired hands‐on research experience by collecting, processing, and analyzing destructive (crop biomass samples) and non‐destructive (plant height, nodes, and leaf chlorophyll content) cropping system data on soybeans under irrigated and dryland production systems, where they worked closely with the farmer. Identifying the current research problem and study site selection, scientific decision‐making during the field study, developing critical thinking while ensuring research communication skills, and quality assurance and quality control through technology during data collection and analysis were learning outcomes. The research highlights the observed distinct performance between irrigated and non‐irrigated soybeans using non‐destructive plant health and growth indicators with plant biomass, following appropriate quality control and assurance steps. Statistically, irrigated soybeans outperformed non‐irrigated soybeans in terms of average plant height at maturity (irrigated: 97.0±1.7 cm vs. non‐irrigated: 37.4±0.6 cm; p<0.01) and number of nodes on the mainstem (irrigated: 19.5±1.2 vs. non‐irrigated: 12.6±0.8; p<0.01). Findings from this study can help ensure quality control and assurance in future cropping system projects. Through the agroecosystem study, we exhibit the importance and role of undergraduate research opportunities in developing the next generation of problem solvers. [ABSTRACT FROM AUTHOR]

Poultry litter on agricultural lands could introduce nitrogen (N), phosphorus (P), heavy metals in soil and ground water. Native vegetations were identified to assess efficacy for phytoremediation of nutrients and metals from soil and water. Objective was to measure capability of multi-year native species to remove metals, nutrients, and prevent Nitrate-N leaching below the rooting zone. Treatments were distributed in four replicates with/without fertilization. Suction lysimeters were installed at 30, 60, and 90-cm depths in 3 of 4 replicates. Species were identified, recorded, five specified cuttings sampled. Plant, soil, water samples were prepared and analyzed by spectroscopy. Nitrate-N extraction, nitrates in water samples were determined using flow injection analysis. Fertilized plots (NVM) had 39% more biomass yield than unfertilized plots (NVN). In plants, nutrient and metal concentrations varied significantly with 14% increase in Zn, 36% and 26% in K and Mg over NVN for first and second year. Uneven between NVM and NVN, topsoil had higher values for most nutrients and metals. Largest P and (NO3−)−N in plant and water were observed from NVM. Cultivation of native vegetation appears to be an effective approach for remediation of excess nitrates-N, P, heavy metals from surface and sub-surface zones of the soil. Native vegetation has been used for soil fertility, specific reasons like the removal of pesticides or agrochemicals, and other chemical related exposures. Studies on the use of native vegetation for phytoremediation on agricultural lands are uncommon. This research looked at the capability of native vegetation of different species as a viable tool for the remo+val of excess nutrients and heavy metals from agricultural lands. Results indicated native vegetation can take up significant amounts of excess nutrients from soils, proportional to their biomass accumulation. Native Vegetation was therefore found to be a nutrient sink, capable of removing excess nutrients/metals from the soil. [ABSTRACT FROM AUTHOR]

Interpreting time domain reflectometry (TDR) waveforms obtained in soils with non‐uniform water content is an open question. We design a new TDR waveform interpretation model based on convolutional neural networks (CNNs) that can reveal the spatial variations of soil relative permittivity and water content along a TDR sensor. The proposed model, namely TDR‐CNN, is constructed with three modules. First, the geometrical features of the TDR waveforms are extracted with a simplified version of VGG16 network. Second, the reflection positions in a TDR waveform are traced using a 1D version of the region proposal network. Finally, the soil relative permittivity values are estimated via a CNN regression network. The three modules are developed in Python using Google TensorFlow and Keras API, and then stacked together to formulate the TDR‐CNN architecture. Each module is trained separately, and data transfer among the modules can be facilitated automatically. TDR‐CNN is evaluated using simulated TDR waveforms with varying relative permittivity but under a relatively stable soil electrical conductivity, and the accuracy and stability of the TDR‐CNN are shown. TDR measurements from a water infiltration study provide an application for TDR‐CNN and a comparison between TDR‐CNN and an inverse model. The proposed TDR‐CNN model is simple to implement, and modules in TDR‐CNN can be updated or fine‐tuned individually with new data sets. In conclusion, TDR‐CNN presents a model architecture that can be used to interpret TDR waveforms obtained in soil with a heterogeneous water content distribution. Plain Language Summary: One popular way to measure water content in a whole soil profile is to insert an array of short TDR sensors at multiple, pre‐specified depths. Despite the efforts needed to install the sensors and the potential disturbance of nearby soils, it provides discretized measurements in space. Instead, vertically inserting long TDR sensors could provide measurement without the restriction of pre‐specific depths, but the spatial variation of soil water content along the TDR sensor rods needs to be reconstructed. That is an open and challenging question in TDR data interpretation studies. We developed a deep machine learning model that can compute the soil water content distributions along the sensor rods, and the model performance is evaluated using simulated TDR data with varying soil water content but under relatively stable electrical conductivity. The artificial neural network architecture in this study presents a model prototype, which is important for scientists and engineers in TDR data analysis, sensor design and related industries. Key Points: A time domain reflectometry (TDR) waveform interpretation model is developed using convolutional neural networksThe new model includes three steps: feature extraction, reflection position identification and relative permittivity regressionThe new model can predict variations of soil relative permittivity and water content along a TDR sensor [ABSTRACT FROM AUTHOR]

Crop models are frequently used to assess the impact of climate change responses. Evaluation of model performance against empirical data is crucial to establish confidence, particularly for rice (Oryza sativa L.), one of the world's important cereal crops. Data from soil-plant-atmosphere-research (SPAR) chambers and field plots were used to assess three versions of the ORYZA model to a range of climate conditions. The three versions were: V1–the original, V2–V1 plus a revised heat stress component, and V3–V2 plus a coupled leaf-level gas exchange algorithm. Comparison against SPAR datasets, which covered a range of temperatures at two CO2 levels, indicated successive improvement in yield predictions with the model version. Root Mean Square Error (RMSE) decreased by 520 and 647 kg ha−1 for V2 and V3, respectively, and Wilmott's index of agreement improved by 10 and 12% compared with V1 when averaged across 20 treatments and three cultivars. Similar improvements were observed from 17 field dataset simulations with two additional varieties. These results indicated the importance of improving heat sterility functions and carbon assimilation methodologies that incorporate direct responses to air temperature and CO2 concentration in rice models. Accounting for cultivar differences in thermal sensitivity is also an important consideration for climate assessments. [ABSTRACT FROM AUTHOR]

Water stress in plants depends on the soil water level and the evaporative demand. In this study, the physiological, biochemical, and molecular response of maize were examined under three evaporative demand conditions (low—1.00 kPa, medium—2.2 kPa, and high—4.00 kPa Vapor pressure deficit (VPD)) at three different soil water content (SWC); well-watered, 45%, and 35% SWC. Plants grown at 35% SWC under high VPD had significant (p < 0.01) lower leaf weight, leaf area, and leaf number than low VPD. Plants under low, medium, and high VPD with drought stress (45% and 35% SWC) showed a 30 to 60% reduction in their leaf area compared to well-watered plants. Gas exchange parameters including photosynthesis, stomatal conductance, and water use efficiency exhibited significant differences (p < 0.01) between treatments, with the highest reduction occuring at 35% SWC and high VPD. Both drought and VPD significantly (p < 0.01) increased C4 enzyme levels and some transcription factors with increased stress levels. Transcription factors primarily related to Abssisic Acid (ABA) synthesis were upregulated under drought, which might be related to high ABA levels. In summary, severe drought levels coupled with high VPD had shown a significant decrease in plant development by modifying enzymes, ABA, and transcription factors. [ABSTRACT FROM AUTHOR]

Synthesis and quantification of disciplinary knowledge at the whole system level, via the process models of agricultural systems, are critical to achieving improved and dynamic management and production systems that address the environmental concerns and global issues of the 21st century. Soil physicists have made significant contributions in this area in the past, and are uniquely capable of making the much-needed and exciting new contributions. Most of the exciting new research opportunities are trans-disciplinary, that is, lie on the interracial boundaries of soil physics and other disciplines, especially in quantifying interactions among soil physical processes, plant and atmospheric processes, and agricultural management practices. Some important knowledge-gap and catting-edge areas of such research are: (1) quantification and modeling the effects of various management practices (e.g., tillage, no-tillage, crop residues, and rooting patterns) on soil properties and soil--plant--atmosphere processes; (2) the dynamics of soil structure, especially soil cracks and biochannels, and their effects on surface runoff of water and mass, and preferential water and chemical transport to subsurface waters; (3) biophysics of changes in properties and processes at the soil--plant and plant--atmosphere interfaces; (4) modeling contributions of agricultural soils to climate change and effects of climate change on soil environment and agriculture; and (5) physical (cause-effect) quantification of spatial variability of soil properties and their outcomes, new methods of parameterizing a variable field for field-scale modeling, and new innovative methods of aggregating output results from plots to fields to larger scales. The current status of the various aspects of these research areas is reviewed briefly. The future challenges are identified that will require both experimental research and development of new concepts, theories, and models.

Water and thermal dynamics at soil surfaces are influenced by multiple ambient factors, for example, weather, soil, residue mulch, and surface runoff. A surface water and temperature model should address those ambient factors, and their interactions and derivatives. In this study, we developed a process‐based simulation model for surface water and heat transfer with two main ambient factors, residue mulch and surface runoff. Surface water content and temperature are simulated with a modified Philip and de Vries (1957) model, including precipitation interception and radiation attenuation in residue mulch. Surface runoff is modeled with the Saint‐Venant equation. Residue decomposition, as a derivative, is computed via a modified CERES‐N model. Interactions between surface runoff and residue mulch, and dynamic decreases in residue mulch thickness due to decomposition are also included. The model was modularized and deployed with a "layered module architecture" in MAIZSIM, such that the main ambient factors, interactions, and derivatives can be activated or deactivated based on scenarios or user settings. Illustrative examples include non‐decomposable residue mulch, surface runoff and mulch decomposition scenarios. Results demonstrate that residue mulch can conserve soil water and reduce temporal variations of surface temperature. Surface runoff and its effects on water infiltration and surface temperature, and nitrogen mineralization during decomposition are also illustrated. The simulated surface temperature, water content, and mulch decomposition results are similar to literature results from field experiments. This study demonstrates the model workability in simulating surface water and temperature dynamics, and the feasibility of synthesizing multiple factors via a modularized model architecture. Plain Language Summary: Knowledge of the impacts on residue mulch and surface runoff on surface soil is important for agricultural field management. We developed a new method to simulate the moisture and thermal regimes on soil surface with residue mulch and/or surface runoff using a computer program. The computer program can also present the surface runoff and residue decomposition. The program can estimate the nitrogen and carbon (organic matter) exchanges between residue mulch and surface soil. This information will be useful to scientists, agricultural managers and consultants. Key Points: A numerical model is developed to simulate soil surface water and temperature with residue mulch and runoffWater and thermal simulations are adaptive to mulch decomposition and shrinkageMulch residue decomposition and N mineralization rate are estimated by the model [ABSTRACT FROM AUTHOR]

Cereal rye (Secale cereale sp.) is a cover crop species known to improve soil and water quality. Late‐season biomass production is information growers need to maximize cover crop benefits and schedule field operations. Statistical learning, built upon statistical and computational algorithms that "learn" from data, may help to improve predictions of cover crop biomass as a function of initial soil inorganic nitrogen levels. Three models—Least Absolute Shrinkage and Selection Operator (LASSO), Ridge, and Random Forest (RF)—were trained and optimized on a 3‐yr allometric and remote sensing dataset of cereal rye responses to N fertilization in the mid‐Atlantic northern and southeastern United States. Shoot biomass (mean, 9,800 kg ha−1) was accurately predicted with a RF model (RMSE, 2,039 kg ha−1). Targeting shoot N content (mean, 107 kg ha−1), on the other hand, LASSO made accurate and more stable predictions (RMSE, 34 kg ha−1). Early‐season information (cover crop C/N ratio, tiller counts, and ground‐sensed normalized difference vegetation index) contributed to enhancing biomass and N content predictions. A final test on untrained data revealed that 92 and 73% of the predictions from either algorithm corresponded to ground‐truthed biomass and shoot N content observed under different N regimes. Modern data‐intensive approaches, such as statistical learning, show promise to characterize end‐season performance of a cover crop and may contribute to better farm decision‐making. [ABSTRACT FROM AUTHOR]

Crop model calibration refers to the process where values are obtained for a subset of model parameters that represent cultivar traits. Calibrated parameters, however, are just as likely to compensate for limitations in model structure as well as reflect true phenotypic characteristics. This confounding of genetics with production environment limits model accuracy particularly for climate assessments and can result in crop parameter values that are location dependent. We evaluated the calibration stability of the potato (Solanum tuberosum L.) model SPUDSIM for two cultivars grown in two sites in the United States sites using a location specific (R1) and cross‐location (R2) methodology. Differences between R1 coefficient values ranged from 4 to 17% between the two sites depending on cultivar. This likely reflected over‐coupling of canopy expansion rate within the model structure as well as an over‐sensitivity to temperature and photoperiod. Broader site differences were observed for growth coefficients, particularly when compared with the R2 method. Validation year end‐of‐season yields (17% error or less) and in‐season RMSE (<36.1 g plant−1) were similar for both methods. The R2 calibration overpredicted yields by 5% or less as compared with R1, with a maximum discrepancy of 0.35 Mg ha−1 in climate forecasted impacts. Consistent differences between R1 and R2 calibrated models in rates of yield decline per decade were not observed for cultivar, location, or climate scenario, and averaged 0.034 Mg ha−1 yr−1 for R1 and 0.038 for R2. Thus, calibration for these cultivars and locations was relatively stable in SPUDSIM for the temperate climate evaluated. [ABSTRACT FROM AUTHOR]

• The APEX model is used in a wide range of locations to evaluate conservation practices on farm land. • Of the several approximations to calculate Curve Number (CN) method for estimating runoff, the Variable Daily CN with Soil Moisture Index provided the best simulations of water content. • The diagnostic APEX performance in the Optimizing Production Inputs for Economic and Environmental Enhancement (OPE3) site suggested reasonably accurate Evapotranspiration (ET), soil water calculations, and corn yields. • APEX performance in the OPE3 site varied by season (pre-plant, growing and post-plant). The Agricultural Policy/Environmental Extender (APEX) is a complex mechanistic simulation model designed to evaluate agricultural management strategies and their effects on yields, water and soil quality, and resource use. It is one of the main models used in the Conservation Effects Assessment Project (CEAP) assessment program and has been employed to evaluate the effects of conservation practices in the Chesapeake Bay Watershed (CBW). Calibration of model parameters is an important process in order to apply a simulation model to a particular area. The APEX model is normally parameterized for each application using selected datasets from the region. However, the APEX application area is usually very large and all areas may not be well represented by the calibration. Thus, additional tests of APEX without site-specific calibration for crop yield and soil water content can provide information on the stability of the parameters related to these variables in the CBW. The objective of this study is to evaluate the performance of APEX for corn yields and water balance in a new location using parameters previously developed for the CBW. This study used observations from the OPE3 site (Optimizing Production Inputs for Economic and Environmental Enhancement) located at the US Department of Agriculture, Henry A. Wallace Agricultural Research Center, Beltsville, Maryland, USA. Data from the OPE3 site consists of water content at various layers, corn yields, and ET covering the period from 1998 to 2013. The results for total water in the soil profile (up to 100 cm deep) showed that the simulated water amounts were about 4.4 cm higher than the observed values, but the trends were very similar to the observed trends. The statistical error analysis of R-square (RSQ), Nash-Sutcliffe (E NS) efficiency coefficient and Root Mean Square Error (RMSE) are 0.59, 0.51 and 0.21 cm•day−1, respectively. The average simulated yields were 0.3 t•ha−1 (4%) lower than observed yields with an average absolute deviation between APEX and OPE3 of 1.3 t•ha1 where the average observed yields over the period was 5.18 t•ha−1, The year-to-year trends were very similar to the measured trends. The monthly evapotranspiration data also showed reasonable trends between observed data and model results with the model RSQ, E NS and RMSE being 0.56, 0.41 and 4.49 mm•month−1, respectively. These results suggest that APEX parameters for yield and water use simulations calibrated for the CBW can be considered applicable to other areas of the CBW where calibration data are not available. [ABSTRACT FROM AUTHOR]

In nature, crops such as soybean are concurrently exposed to temperature (T) stress and phosphorus (P) deficiency. However, there is a lack of reports regarding soybean response to T × P interaction. To fill in this knowledge-gap, soybean was grown at four daily mean T of 22, 26, 30, and 34°C (moderately low, optimum, moderately high, and high temperature, respectively) each under sufficient (0.5 mM) and deficient (0.08 mM) P nutrition for the entire season. Phosphorus deficiency exacerbated the low temperature stress, with further restrictions on growth and net photosynthesis. For P deficient soybean at above optimum temperature (OT) regimes, growth, and photosynthesis was maintained at levels close to those of P sufficient plants, despite a lower tissue P concentration. P deficiency consistently decreased plant tissue P concentration ≈55% across temperatures while increasing intrinsic P utilization efficiency of canopy photosynthesis up to 147%, indicating a better utilization of tissue P. Warmer than OTs delayed the time to anthesis by 8–14 days and pod development similarly across P levels. However, biomass partitioning to pods was greater under P deficiency. There were significant T × P interactions for traits such as plant growth rates, total leaf area, biomass partitioning, and dry matter production, which resulted a distinct T response of soybean growth between sufficient and deficient P nutrition. Under sufficient P level, both lower and higher than optimum T tended to decrease total dry matter production and canopy photosynthesis. However, under P-deficient condition, this decrease was primarily observed at the low T. Thus, warmer than optimum T of this study appeared to compensate for decreases in soybean canopy photosynthesis and dry matter accumulation resulting from P deficiency. However, warmer than OT appeared to adversely affect reproductive structures, such as pod development, across P fertilization. This occurred despite adaptations, especially the increased P utilization efficiency and biomass partitioning to pods, shown by soybean under P deficiency. [ABSTRACT FROM AUTHOR]