1a. Objectives (from AD-416)
The overall objective is to refine and improve existing simulation models for corn and potato by developing new, and improving existing functional relationships between physiological processes and nutrients in the soil and plant tissues. Results of this research will be used to address existing knowledge gaps in the models. The modified models will be evaluated by testing simulated responses at plant component and whole plant levels. The specific sub-objectives are to improve simulation of the above- and below-ground potato and corn processes in the areas of: (1) water and nutrient stress effects on growth, development, morphology, and yield, and (2) response of root growth and activity (water and nutrient uptake) to soil nutrients (N, P and K), and water. The model and new components will be evaluated using experimental data. The potato and corn models, along with a rye cover crop model and existing models for soybean and corn, will then be used for assessment of the environmental consequences of agricultural management practices on carbon sequestration and nutrient balances. These practices include fertilizer applications, rotations, and cover crops.
1b. Approach (from AD-416)
Mechanistic models for soybean (GLYCIM), cotton (GOSSYM), corn (MAIZSIM), and potato (SPUDSIM) have been developed in previous projects by this group. The proposed research will broaden the capabilities of the corn and potato models by utilizing data from experiments carried out in unique, state-of-the-art sun-lit growth chambers and field plots. Short and long term experiments will be employed to test hypotheses and develop algorithms for plant processes to be used in the computer models. Data collected by collaborators will be used to test and evaluate the models. A simple Rye model will be developed to simulate a cover crop during the fall-winter season. A computer graphical user interface will be developed using components from the existing software program, GUICS, to allow a user to simulate long term crop rotations over multiple growing seasons. Advanced data management capabilities will be added to the interface to help with interpretation and management of input and output data. Tools for weather generation and estimation of soil hydraulic properties will be added to the interface to provide a wide range of environmental conditions for assessment. All crop models will be used within the new interface to assess the environmental and economic impacts of climate change on carbon sequestration, and nitrogen and water balances for relevant production systems.
3. Progress Report
This is a new project that started March of 2009. Some preliminary work on the effects of potassium deficiencies in potato and corn under elevated and ambient CO2 concentrations was carried out in 2008. This work was documented in the progress report for the bridging project (1275-61660-005-00D). Experiments to quantify nitrogen uptake and plant growth in corn were carried out in the summer of 2008. These data are being used to parameterize a root growth and nitrogen uptake routine in our corn model. A manuscript is also in preparation. Quantitative data on leaf expansion rate and leaf addition rate were collected from potato plants grown in growth chambers under elevated and ambient levels of CO2. The data are being used to develop an improved model of leaf expansion in potato. A preliminary model was developed and used to test the hypothesis that carbon allocation to leaves results in a bell shaped distribution of leaf areas along the main stem. A MySQL database was developed for the input data for the corn, potato, and soybean models, and the database populated with model input data from several scenarios for testing. An interface and queries were developed to facilitate retrieval of data from the database and creation of input files for the models. A new root growth model was developed for the corn and potato models. This new model simulates root growth as a diffusive process and is expected to be more adaptable for soil stresses than the current model. Root and above ground growth or rye as a cover crop has been compiled for two growing seasons. Measurements of water content and soil/air temperatures have also been collected in a field planted to rye for future testing of a rye model. The soybean model GLYCIM was tested for weather and soil conditions in Thailand and found to predict growth and yield of soybean planted at three different dates. Assessments of yield for a range of spring/early summer planting dates, three soils, and three varieties were carried out using the model. The results provided valuable information on optimal planting dates for soybean in Thailand.
1. Simulation of leaf growth in corn under water stress is improved by better representation of the functional relationship between leaf water potential and leaf growth. Growers need methods to estimate the effects of water stress on corn growth to evaluate different irrigation management practices and their impact on corn yields. An improved functional relationship to estimate leaf growth rate as a function of soil water status in the rooting zone was developed from measured data and incorporated into the corn simulation model MaizSim. These results will help scientists and growers interested in crop modeling and improved irrigation management to more precisely simulate the effect of water stress on corn development and growth.
2. The soybean simulation model GLYCIM was found to be a useful tool to assess the effects of planting date and variety on soybean yields in Thailand. Data from a field study in Thailand on soybean growth and yield was used to develop parameters for three Thai soybean varieties and validate the accuracy of the model in estimating yield under different planting dates. The model was run with four years of measured weather data, seven planting dates, three soil types and three soybean cultivars to produce simulated yield results for 504 cultivation and yield scenarios for two key soybean production areas in northern Thailand. The results of the simulation study revealed that delayed planting beyond May 31 decreased soybean yields at all locations. The results also indicated that there is a cultivar, soil type and planting date interaction on yields. These results confirmed the validity of the soybean model GLYCIM as a reliable on-farm decision support tool to optimize planting date and yields under tropical geographical locations. This information is useful for model users for on-farm decision support, scientists, and extension personnel involved in optimizing soybean yields.`
3. Stem Density Has Minimal Influence on Modeling Potato Growth. In order to develop potato crop models, data from controlled environment chambers, in which potato growth from each planted seed-piece is restricted to a single main-stem, is required. Experiments were conducted to quantify the error(s) involved in assuming single main-stem potato production is equivalent to multiple main-stem conditions. It was found that all potato plants produced the same amount of growth, and had similar developmental rates, independent of the number of stems. This result was primarily due to an ability of potato plants to continue to produce new leaves and branches throughout the growing season. The result confirms that potato models based on controlled environment work are suitable for use by crop consultants and scientists who use this type of data to help farmers more efficiently manage water and fertilizer usage.
Yang, Y., Timlin, D.J., Fleisher, D.H., Kim, S., Quebedeaux, B., Reddy, V. 2009. Simulating Leaf Area of Corn Plants at Contrasting Water Status. Agricultural and Forest Meteorology. 149:1161-1167.