Effects of Estimating Soil Hydraulic Properties and Root Growth Factor on Soil Water Balance and Crop Production

Abstract

Accurate simulation of plant growth depends not only on plant parameters, but also on soil parameters. Although there is uncertainty in measured soil parameters and root distributions, their eff ects on simulated plant growth have been much less studied. Th is study evaluates the simulated responses of six crops, wheat (Triticum aestivum L.), maize (Zea mays L.), barley (Hordeum vulgare L.), soybean (Glycine max L. Merr.), peanut (Arachis hypogaea L.), and chickpea (Cicer arietinum L.), under various water and N management to diff erent methods of estimating soil hydraulic properties and soil root growth factor (SRGF) in root zone water quality model (RZWQM2) that contains the decision support system for agrotechnology transfer (DSSAT) Version 4.0 plant growth models. Th e two methods of obtaining the soil water retention curve (SWRC) in RZWQM2 were based on (i) known soil water contents at both 33 and 1500 kPa suctions, or (ii) soil water content at 33 kPa only. Th e two methods of estimating saturated hydraulic conductivity (K sat ) were (i) soil texture class based average K sat or (ii) K sat calculated from eff ective porosity (diff erence between soil water contents at saturation and at 33 kPa). For the six crops, simulation results showed that the soil water balance was aff ected more by K sat than by SWRC, whereas the simulated crop growth was aff ected by both K sat and SWRC. Small variations in the SRGF did not aff ect soil and crop simulations, and SRGF could be estimated with a simple exponential equation.

Calibration, uncertainties and use of soybean crop simulation models for evaluating strategies to mitigate the effects of climate change in Southern Brazil

“…In DSSAT, for example, rooting is adjusted using a coefficient of root distribution in the soil profile (MA et al, 2009), information that can be found more easily for the study region than the parameters of maximum water uptake for each day per soil layer, which is used by APSIM (DARDANELLI et al, 1997). These variables related to soil and roots require a local calibration due to the interaction of crop and soil characteristics.…”

“…The procedure followed Hunt and Boote (1998) recommendation, starting calibration of phenological parameters and then growth parameters. The coefficients were calibrated using the generalized likelihood uncertainty method (GLUE) (MAKOWSKI et al, 2002;JONES et al, 2011) as well as based on visual adjustments to observed time-series. The visual adjustments was used due to GLUE to be a statistical method that considered only end-of-season measurements to estimate genetic coefficients, condition that can lead to incorrect results of in-season measurements.…”

“…Soil root growth factor determines the potential ability of roots to grow in respective soil layers, altering the shape of root distribution and the potential amount of water that can be extracted by the roots from the soil (MA et al, 2009). In this simulation, total root mass was not changed, rather, the adaptation to drought was based on reducing the root length density in the upper layers and improving it in deeper layers.…”

Vulnerabilities and Adapting Irrigated and Rainfed Cotton to Climate Change in the Lower Mississippi Delta Region

“…The modified model was used for simulations of yield responses to rainfall and irrigations under current and future climates in this study. v4.6 for simulation of specific crops [25,[43][44][45] (http://arsagsoftware.ars.usda.gov/). Studies verifying the capabilities of RZWQM2 for explaining and managing dryland and irrigated cropping systems in the Great Plains have been reported (for example, [43,[46][47][48]).…”

Simulation for response of crop yield to soil moisture and salinity with artificial neural network

“…Process-based crop growth models are often preferred to empirical ones, but these deterministic models need to identify many parameters (Sinclair and Seligman, 1996;Matthews, 2002a,b;Ziaei and Sepaskhah, 2003). Notably, deterministic calibration is very difficult in most cases (Ma et al, 2009). So an empirical model may offer an even more reliable method in investigating crop responses than poorly calibrated process models when the necessary data are available (Park et al, 2005).…”