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

Assessment of Groundwater Quality under Changing Climate in Ngorongoro Conservation Area, Tanzania

This research article was American Society of Civil Engineers in 2022Understanding the hydrochemical composition of water resources in the Ngorongoro Conservation Area (NCA, Dodoma, Tanzania) related to climate variability is essential for sustainable development. Thus, the current study used the HYDRUS-1D model to assess the groundwater quality change due to the leaching of hydrochemicals from surface water under the climate variability of the NCA. This study observed that the area’s surface water had varying hydrochemical contaminants, whereas the groundwater is currently most suitable for drinking and domestic purposes. However, it is predicted that two anions (Cl−1 and PO4−3) and two cations (Na+ and K+) are expected to exceed the permissible limits from 2036 to 2050, considering the anticipated climatic conditions. Changes in groundwater quality for cations and anions are significantly correlated to evapotranspiration and temperature, with Pearson’s coefficient of determinations r between 0.35 and 0.66. The findings of this study are necessary to benchmark better water resources management planning

Incorporation of carbon dioxide production and transport module into a Soil-Plant-Atmosphere continuum model

Carbon dioxide release from agricultural soils is influenced by multiple factors, including soil (soil properties, soil-microbial respiration, water content, temperature, soil diffusivity), plant (carbon assimilation, rhizosphere respiration), atmosphere (climate, atmospheric carbon dioxide), etc. Accurate estimation of the carbon dioxide (CO2) fluxes in the soil and soil respiration (CO2 flux between soil and atmosphere) requires a process-based modeling approach that accounts for the influence of all these factors. In this study, a module for CO2 production via root and microbial respiration and diffusion-based carbon dioxide transport is developed and integrated with MAIZSIM (a process-based maize crop growth model that accounts for detailed soil and atmospheric processes) based on a modularized architecture. The developed model simulates root respiration based on root mass, root age, soil water content, and temperature. Microbial respiration is based on the soil microbial processes by accounting for the carbon dynamics in the litter, humus, and organic fertilizer pools as moderated by the soil water content, temperature, microbial synthesis, humification, and decomposition of the carbon pools. Case studies presented include scenarios with different soil, climate, and carbon pools that simulated the soil respiration with an average index of agreement of 0.73 and root mean squared error of 11.4 kg carbon ha-1 between the measured and simulated soil respiration. The modular architecture used in the model development facilitates easy integration with other existing crop models and future modifications

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–atmosphereresearch) 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

Cotton Yield Prediction Using Random Forest

The cotton industry in the United States is committed to sustainable production practices that minimize water, land, and energy use while improving soil health and cotton output. Climate-smart agricultural technologies are being developed to boost yields while decreasing operating expenses. Crop yield prediction, on the other hand, is difficult because of the complex and nonlinear impacts of cultivar, soil type, management, pest and disease, climate, and weather patterns on crops. To solve this issue, we employ machine learning (ML) to forecast production while considering climate change, soil diversity, cultivar, and inorganic nitrogen levels. From the 1980s to the 1990s, field data were gathered across the southern cotton belt of the United States. To capture the most current effects of climate change over the previous six years, a second data source was produced using the process-based crop model, GOSSYM. We concentrated our efforts on three distinct areas inside each of the three southern states: Texas, Mississippi, and Georgia. To simplify the amount of computations, accumulated heat units (AHU) for each set of experimental data were employed as an analogy to use time-series weather data. The Random Forest Regressor yielded a 97.75% accuracy rate, with a root mean square error of 55.05 kg/ha and an R2 of around 0.98. These findings demonstrate how an ML technique may be developed and applied as a reliable and easy-to-use model to support the cotton climate-smart initiative.Comment: 6 pages, 2 figures, 3 table

Modeling vapor transfer in soil water and heat simulations: A modularized, partially-coupled approach

Coupled water and heat transfer models are widely used to analyze soil water content and temperature dynamics, evaluate agricultural management systems, and support crop growth modelling. In relatively dry soils, vapor transfer, rather than liquid water flux, becomes the main pathway for water redistribution. However, in some modularized soil simulators, e.g., 2DSOIL (Timlin et al., 1996), vapor transfer is not included, which may induce errors in soil water and heat modelling. Directly embedding vapor transfer into existing water and heat transfer modules may violate the modularized architecture of those simulators. Therefore, the objectives of this study are to design a vapor transfer model, evaluate its performance, and implement it as a separate module in a coupled soil water and heat simulator, e.g., 2DSOIL. The efficacy of the vapor transfer model is evaluated by comparing the simulated soil water content and temperature before and after including the new vapor transfer model, and the soil water content and temperature simulated with the standard Philip and de Vries (1957) model. By implementing vapor transfer as a separate module in 2DSOIL, modifications to existing water and heat transfer modules can be minimized and the modularized model architecture can be maintained. Numerical examples of 2DSOIL with the new vapor transfer model are presented to illustrate the effects of vapor flux on soil water and temperature redistributions. In conclusion, the new vapor transfer model provides an effective and easy-to-use method to account for the effects of vapor transfer on coupled soil water and heat simulations

Coupled heat and water transfer in heterogeneous and deformable soils: Numerical model using mixed finite element method

We present a generic model framework for coupled heat and water transfer (CHWT) in deformable (non-rigid) soils with spatial variations of soil properties. The model backbone is a mixed finite element method (FEM), which solves the Philip and de Vries (1957) CHWT model and achieves conservation of mass and energy on both local and global scales. Spatial variations occur in soil hydraulic and thermal properties due to transient water content and temperature distributions. Based on the mixed FEM scheme, a gradient measure and a clustering model (“k-means”) are proposed to trace the regions with large spatial variations of soil properties, and an adaptive mesh refinement technique is developed to improve the spatial resolution and simulation accuracy. Deformation perturbates local soil topography and alters transient soil water and temperature regimes in the deformed regions. A quasi-static deformation model is presented, and the deformation effects are incorporated into the mixed FEM scheme. When external load exists, soil deformation is simulated with an updated Lagrangian formulation, and the local water content and temperature variations due to soil volume changes are also updated in the CHWT model. Numerical examples, including thermally induced soil water transfer and water infiltration, illustrate the model ability to provide plausible CHWT results, especially the refined solutions near the wetting fronts and the water content and temperature distributions when the soil is deformable. In conclusion, the proposed model framework provides an effective pipeline to incorporate and process the spatial variations of soil properties and soil deformation in CHWT simulations.This article is published as Wang, Zhuangji, Dennis Timlin, Gang Liu, David Fleisher, Wenguang Sun, Sahila Beegum, Joshua Heitman et al. "Coupled heat and water transfer in heterogeneous and deformable soils: Numerical model using mixed finite element method." Journal of Hydrology 634 (2024): 131068. doi:10.1016/j.jhydrol.2024.131068. Works produced by employees of the U.S. Government as part of their official duties are not copyrighted within the U.S. The content of this document is not copyrighted