Muhammad Tahir
3 May 2018
Collaborator: David Mulla
Introduction
Nutrient losses from Corn Belt states have been linked to hypoxia in the Gulf of Mexico. High nutrient losses from the Upper Midwest are partly due to high fertilizer application rates on corn, and mineralization of soil organic matter, precipitation and irrigation patterns that cause leaching and drainage. Goals to reduce nitrate-N loads include a 45% reduction for the Gulf of Mexico, Minnesota interim target is 20% by 2025. Ground water is increasingly at risk for nitrate contamination in Minnesota. Most vulnerable areas are the Central sand, Karst and tile drained regions of southern MN. Extensive monitoring of nitrate-N in nearly 900 wells by MDA and MPCA showed that 40% of groundwater wells in sand and gravel aquifers located in central Minnesota exceeding 10 mg/L, while 10% of wells in southeastern Minnesota exceeded the drinking water standard (Kroenig and Ferry, 2013). Optimizing N rates for agronomic and environmental considerations continues to receive much attention. However, selecting optimal N fertilizer rates is surprisingly difficult, due to quantity of potentially mineralizable N (Fernandez et al., 2017), soil types, precipitation pattern and other climatic factors.
Agro-hydrological simulation models can be useful tools for estimation of crop yield, nutrient uptake, and their leaching in soil, and can settle the nutrient management related issues over several seasons. Though, computer models can be of great value in evaluating the effectiveness of practices to predict yield and NO3-N to ground water, models are most accurate when calibrated and validated using site specific experimental data. Environmental Policy Integrated Climate (EPIC) model is a continuous time, field scale agricultural management and water quality model. EPIC functions on a daily time step. It can simulates >80 crops with using unique parameter values for each crop, still new crops can be added. EPIC has >200 input parameters, however few more sensitive can be optimized according to the climatic conditions, and management practices. Having wide range of input parameter, it can be configured for a wide range and variety of management practices. EPIC has capability to simulate management components including crop rotations, tillage, irrigation scheduling, drainage, furrow diking, liming, grazing, tree pruning, thinning, manure, and nutrient application rates and timing. EPIC can also do prediction under alternative scenario, such as auto-trigger at specific matric potential for irrigation scheduling, and at specific N contents in plant biomass to apply fertilizer.
We are focusing on the following BMPs: a) Fertilizer N practices (rate, timing, splits etc.) and forms of N (urea, slow release, etc.); b Water management practices (checkbook method, and other irrigation scheduling methods); and c) Vegetative management practices (cover crops, cropping systems etc.).
The objectives of our study were to test EPIC model suitability and accuracy to simulate yield, N uptake, and NO3- leaching in the central sands region of Minnesota, under C-C, C-Sb, and Sb-C rotation, and ultimately to model a range of alternative practices that can minimize leaching losses in the Central Sands Region of Minnesota.
Modeling Approach and Assumptions
EPIC model calibration process was made based on the identified sensitive input parameters pertaining to grain yield, nitrogen uptake, drainage and nitrogen leaching losses.
Calibration & Validation Use half of data for calibration (2011-12), half for validation (2013-14). Parameter were optimized on trial and error basis. Model calibration and validation procedures were conducted after sensitivity analysis. EPIC model parameter sensitivity analysis was made following the procedures of Hoffman et al., (1983), to calculate the sensitivity index (SI) associated with crop growth, nitrogen uptake and nitrogen leaching losses following the procedures of Hofman et al. (1983). This procedure considers the minimum and maximum output values resulting from varying the input over its entire range. The SI equation is:
Evaluation of the model efficiency was performed using Nash-Sutcliffe coefficient (NSE) and presented using graphical comparison of measured and simulated outputs.
Materials and Methods
EPIC simulation was performed on study conducted at Rosholt Research Farm in Pope County (95.17 o north, 45.72o west) during the years 2011 to 2014. The site has three blocks, i.e. Block I, II and III were under continuous corn (C-C), soybean-corn (Sb-C), and corn-soybean (C-Sb) rotations. Data of the years 2011 and 2012 were used for the model calibration, while the years 2013 and 2014 were the validation years. The model testing was focused on the following 8 treatments: T1, 0N; T2, 135 kg N ha-1 ; T3, 180 kg N ha-1 ; T4, 225 kg N ha-1; T5, 270 kg N ha-1 ; T6, 180 kg N as SuperU ha-1; T7, 180 kg N as ESN ha-1 ; and T8, 180 kg N as ESN/U ha-1.
Applied agronomic management practices such as tillage, planting density and dates, fertilizer application, irrigation practices, and harvesting dates over the years 2011-2014 were recorded during field experiments. Measured amounts of nitrogen uptake at different vegetative stages of corn (V8, V12 and R6) and the annual grain yield for the years from 2011-2014 were obtained from field experiments at the Rosholt Research Farm. Irrigation and precipitation data were collected for the years 2011-2014 (Table 1). All measured data from the experimental site were provided by Struffert et al. (2016).
References
Struffert, A.M., J. Rubin, F. Fernandez and J. Lamb. 2016. Nitrogen management for corn and groundwater quality in Upper Midwest irrigated sands. J. Environ. Qual. 45:1557-1564.