2013 Annual Report
1a.Objectives (from AD-416):
The objectives of this agreement are:.
1)to collect data sets for cotton, soybean, corn, and potato on the impact of reduced rates of major nutrients on the growth and developmental rates of various plant organs;.
2)to develop algorithms on the response variables and integrate with models;.
3)to develop and test a diffusive root growth routine and integrate with process based two-dimensional soil process model, 2DSOIL to use with MAIZSIM and SPUDSIM models; and.
4)to work on further improvements in MAIZSIM and SPUDSIM models to account for differences in root growth, water and nitrogen uptake parameters between corn and potato crops in order to improve crop model simulations.
1b.Approach (from AD-416):
The Agricultural Research Service (ARS) continues to improve various subroutines by incorporating new algorithms for crop models, SPUDSIM, MAIZSIM, GLYCIM and GOSSYM. In order to accurately simulate these crops under a range of environmental and management conditions, soils, and varietal differences, the models require additional modules and improved algorithms. The collaborator, working closely with ARS scientists, will develop new modules and algorithms and integrate with Crop Systems and Global Change Laboratory crop models. These models will be validated against field data collected under a range of conditions using several cultivars.
Two studies were conducted to evaluate cotton and soybean responses to phosphorous nutrition under current and projected levels of carbon dioxide (400 and 800ppm). Both crops responded positively to increased CO2 concentration, especially under optimum phosphorous nutrition, however, failed to alleviate the negative effects of phosphorous deficiency on various physiological processes. Data were collected on crop growth, development, fruiting, and dry matter distribution during the season. Using the data from these studies, quantifiable relationships will be developed for various growth processes, including photosynthesis and respiration with plant tissue nutrient status of cotton and soybean crops. These relationships can be used to improve the cotton simulation model, GOSSYM and also the soybean simulation model, GLYCIM.