Examining the Corn Seedling Emergence-Temperature Relationship for Recent Hybrids: Insights from Experimental Studies.

Corn seedling emergence is a critical factor affecting crop yields. Accurately predicting emergence is crucial for precise crop growth and development simulation in process-based crop models. While various experimental studies have investigated the relationship between corn seedling emergence and temperature, there remains a scarcity of studies focused on newer corn hybrids. In the present study, statistical models (linear and quadratic functional relationships) are developed based on the seedling emergence of ten current corn hybrids, considering soil and air temperatures as influencing factors. The data used for model development are obtained from controlled soil plant atmospheric research chamber experiments focused on corn seedling emergence at five different temperatures. Upon evaluating the developed models, the quadratic model relating the air temperature with time to emergence was found more accurate for all corn hybrids (coefficient of determination (R2): 0.97, root mean square error (RMSE): 0.42 day) followed by the quadratic model based on soil temperature (R2: 0.96, RMSE: 1.42 days), linear model based on air (R2: 0.94, RMSE: 0.53 day) and soil temperature (R2: 0.94, RMSE: 0.70 day). A growing degree day (GDD)-based model was also developed for the newer hybrids. When comparing the developed GDD-based model with the existing GDD models (based on old hybrids), it was observed that the GDD required for emergence was 16% higher than the GDD used in the current models. This showed that the existing GDD-based models need to be revisited when adopted for newer hybrids and adapted to corn crop simulation models. The developed seedling emergence model, integrated into a process-based corn crop simulation model, can benefit farmers and researchers in corn crop management. It can aid in optimizing planting schedules, supporting management decisions, and predicting corn crop growth, development, and it yields more accurately.

Developing functional relationships between waterlogging and cotton growth and physiology-towards waterlogging modeling.

Cotton crop is known to be poorly adapted to waterlogging, especially during the early growth stages. Developing functional relationships between crop growth and development parameters and the duration of waterlogging is essential to develop or improve existing cotton crop models for simulating the impact of waterlogging. However, there are only limited experimental studies conducted on cotton specifically aimed at developing the necessary functional relationships required for waterlogging modeling. Further research is needed to understand the effects of waterlogging on cotton crops and improve modeling capabilities in this area. The current study aimed to conduct waterlogging experiments and develop functional relationships between waterlogging and cotton growth and physiology. The experiments were conducted in pots, and the waterlogging was initiated by plugging the drain hole at the bottom of the pot using a wooden peg. In the experiments, eight waterlogging treatments, including the control treatment, were imposed at the vegetative growth stage (15 days after sowing). Control treatment had zero days of water-logged condition; other treatments had 2, 4, 6, 8, 10, 12, and 14 days of waterlogging. It took five days to reach zero oxygen levels and one to two days to return to control after the treatment. After a total treatment duration of 14 days (30 days after sowing), the growth, physiological, reproductive, and nutrient analysis was conducted. All physiological parameters decreased with the number of days of waterlogging. Flavonoid and anthocyanin index increased with increased duration of waterlogging. Photosynthesis and whole plant dry weight in continuously waterlogged conditions were 75% and 78% less compared to 0, and 2-day water-logged plants. Plant height, stem diameter, number of main stem leaves, leaf area, and leaf length also decreased with waterlogging duration. When waterlogging duration increased, leaf, stem, and root macronutrients decreased, while micronutrients showed mixed trends. Based on the experimental study, functional relationships (linear, quadratic, and exponential decay) and waterlogging stress response indices are developed between growth and development parameters and the duration of waterlogging. This can serve as a base for developing or improving process-based cotton models to simulate the impact of waterlogging.

Improving the cotton simulation model, GOSSYM, for soil, photosynthesis, and transpiration processes.

GOSSYM, a mechanistic, process-level cotton crop simulation model, has a two-dimensional (2D) gridded soil model called Rhizos that simulates the below-ground processes daily. Water movement is based on gradients of water content and not hydraulic heads. In GOSSYM, photosynthesis is calculated using a daily empirical light response function that requires calibration for response to elevated carbon dioxide (CO2). This report discusses improvements made to the GOSSYM model for soil, photosynthesis, and transpiration processes. GOSSYM's predictions of below-ground processes using Rhizos are improved by replacing it with 2DSOIL, a mechanistic 2D finite element soil process model. The photosynthesis and transpiration model in GOSSYM is replaced with a Farquhar biochemical model and Ball-Berry leaf energy balance model. The newly developed model (modified GOSSYM) is evaluated using field-scale and experimental data from SPAR (soil-plant-atmosphere-research) chambers. Modified GOSSYM better predicted net photosynthesis (root mean square error (RMSE) 25.5 versus 45.2 g CO2 m-2 day-1; index of agreement (IA) 0.89 versus 0.76) and transpiration (RMSE 3.3 versus 13.7 L m-2 day-1; IA 0.92 versus 0.14) and improved the yield prediction by 6.0%. Modified GOSSYM improved the simulation of soil, photosynthesis, and transpiration processes, thereby improving the predictive ability of cotton crop growth and development.

Projected long-term climate trends reveal the critical role of vapor pressure deficit for soybean yields in the US Midwest.

Extreme climate events including heat waves and droughts are projected to become more frequent under future climate change conditions. However, the mechanisms between soybean yields and climate factors, specifically involving variable rainfall and high heat episodes, are still unclear, particularly with respect to spatial trends in the United States (US) Midwest. A recently modified version of the model GLYCIM was used to evaluate rainfed soybean production across 12 states at a 10 km spatial resolution for three time periods (2011-2020, 2051-2060, 2091-2099) under Representative Concentration Pathway (RCP) scenarios 4.5 and 8.5. Results showed that except for the northernmost Midwest counties, most of the current rainfed cropping system in the Midwest would suffer a 24.6-47.4 % yield loss without considering the CO2 fertility effect. Incorporating the effect of elevated CO2 showed a smaller yield loss of 11.6-29.5 %. The increased frequency of extreme degree days (EDD) or accumulation of hourly temperatures above 30 °C associated with increased vapor pressure deficit (VPD) played a key role in contributing to water deficits and resultant crop losses under these future climate conditions. Although a relatively weak relationship between summer rainfall and crop yield was observed, decreased rainfall caused VPD to increase which induced crop water deficits. These findings suggest that it is crucial to consider VPD along with high temperature and low rainfall trends simultaneously for development of potential management or breeding-based adaptative strategies for soybean.

Importance of snowmelt on soil nitrate leaching to groundwater - A model study.

The movement of nitrate to surface water bodies during snow accumulation and melting has been extensively studied, but there are only limited studies on the influence of snow processes on nitrate leaching to groundwater. The present study investigated the impact of snow processes on nitrate leaching to groundwater based on a simulation modeling approach using HYDRUS-1D. HYDRUS-1D model has a temperature threshold-based snow model in addition to water, solute, and heat simulation components. The snow component in HYDRUS-1D was previously not applied to snow simulation studies since the method does not consider a detailed physical and process-based representation of snow accumulation and melting. In the present study, HYDRUS-1D was used to simulate snow accumulation and melting over 30 years for a location in Waverly, Lancaster County, Nebraska, USA. From the simulations, it was observed that the calibrated temperature threshold based snow module in HYDRUS-1D is effective in simulating snow accumulation and melting, as shown by the index of agreement and root mean squared error of 0.74 and 2.70 cm for calibration (15 years) and 0.88 and 2.70 cm for validation (15 years), respectively. The impact of snow melt on nitrate leaching was studied based on a study area with corn cultivation (Waverly, Nebraska, USA). A long-term (60 years) analysis was carried out for irrigated and non-irrigated agriculture with and without precipitation as snow. A higher nitrate leaching to groundwater was observed in the order of irrigated-with snow (54,038 kg/ha), irrigated-without snow (53,516 kg/ha), non-irrigated-with snow (7,431 kg/ha), and non-irrigated-without snow (7,090 kg/ha). This displays a 0.98% and 4.81% increase in nitrate leaching due to snow in irrigated and non-irrigated conditions, respectively. When extrapolated over the corn cultivated regions in Nebraska, this resulted in a difference of 1.2E+09 kg and 6.1E+08 kg of nitrate when considering snow in irrigated and non-irrigated areas over 60 years. This is the first study that has analyzed the long-term impact of snow on nitrate transport to groundwater based on a simulation modeling approach. The results show that snow accumulation and melting plays a vital role in the nitrate leaching into the groundwater and indicates the importance of considering snow components in similar studies.

Implementation of Solute Transport in the Vadose Zone into the "HYDRUS Package for MODFLOW".

The "HYDRUS package for MODFLOW" is an existing MODFLOW package that allows MODFLOW to simultaneously evaluate transient water flow in both unsaturated and saturated zones. The package is based on incorporating parts of the HYDRUS-1D model (to simulate unsaturated water flow in the vadose zone) into MODFLOW (to simulate saturated groundwater flow). The coupled model is effective in addressing spatially variable saturated-unsaturated hydrological processes at the regional scale. However, one of the major limitations of this coupled model is that it does not have the capability to simulate solute transport along with water flow and therefore, the model cannot be employed for evaluating groundwater contamination. In this work, a modified unsaturated flow and transport package (modified HYDRUS package for MODFLOW and MT3DMS) has been developed and linked to the three-dimensional (3D) groundwater flow model MODFLOW and the 3D groundwater solute transport model MT3DMS. The new package can simulate, in addition to water flow in the vadose zone, also solute transport involving many biogeochemical processes and reactions, including first-order degradation, volatilization, linear or nonlinear sorption, one-site kinetic sorption, two-site sorption, and two-kinetic sites sorption. Due to complex interactions at the groundwater table, certain modifications of the pressure head (compared to the original coupling) and solute concentration profiles were incorporated into the modified HYDRUS package. The performance of the newly developed model is evaluated using HYDRUS (2D/3D), and the results indicate that the new model is effective in simulating the movement of water and contaminants in the saturated-unsaturated flow domains.