A Functional Analysis of Pedotransfer Functions Developed for Sri Lankan soils: Applicability for Process-Based Crop Models

Abstract

As measurements are expensive and laborious, the estimation of soil hydraulic properties using pedotransfer functions (PTFs) has become popular worldwide. However, the estimation of soil hydraulic properties is not the final aim but an essential input value for other calculations and simulations, mostly in environmental and crop models. This modeling approach is a popular way to assess agricultural and environmental processes. However, it is rarely used in Sri Lanka because soil hydraulic data are rare. We evaluated the functionality of PTFs (developed to estimate field capacity (FC) and the permanent wilting point (PWP) of Sri Lankan soils) for process-based crop models. We used the Agricultural Production Systems sIMulator (APSIM) as the test model. Initially, we confirmed the importance of PWP (LL15) and FC (DUL) by assessing the sensitivity of the soil input parameters on the growth and yield of rice under rainfed conditions. We simulated the growth and yield of rice and the four selected outputs related to the APSIM soil module using the measured and estimated values of FC and PWP. These simulations were conducted for ten years in 16 locations of Sri Lanka, representing wet, intermediate, and dry zones. The simulated total aboveground dry matter and weight of the rough rice, using both input conditions (the measured and PTF-estimated soil hydraulic properties), showed good agreement, with no significant differences between each other. Outputs related to the soil module also showed good agreement, as no significant differences were found between the two input conditions (measured and PTF-estimated soil hydraulic properties). Although the DUL and LL15 are the most influential parameters for the selected outputs of APSIM-Oryza, the estimated FC and PWP values did not change the predictive ability of APSIM. In this way, the functionality of PTFs for APSIM crop modeling is confirmed.Agronomy 2020, 10, 285 2 of 18 world, especially in tropical regions [1]. Pedotransfer functions (PTFs) are predictive functions used to estimate difficult-to-measure soil parameters with easily measurable soil parameters [2]. Point-based PTFs are used to determine soil parameters at specific, conventional values of matric potential [3]. The moisture content at −10 and −33 kPa (representing the field capacity (FC)) and moisture content at -1500 kPa (representing the permanent wilting point (PWP)) are the most common reference values for the soil moisture characteristic curve (SMCC) used for point PTFs [4].Point PTFs were developed [5] for Sri Lankan soil and confirmed the statistical accuracy of these PTFs for Sri Lankan conditions. However, these PTFs cannot perfectly calculate soil hydraulic parameters. In other words, there are always some errors that could be over or underestimated according to soil type. These errors may be different according to the application of the PTFs. Hence, the final estimation of the model quality should be defined by the model's applicability [6] because the estimation of hydraulic properties is not the...

Basic Soil Data Requirements for Process-Based Crop Models as a Basis for Crop Diversification

“…However, they did not include AD, which showed the highest sensitivity in the current study. Without AD, the sensitivity of inputs of the current study were consistent with the finding of Gunarathna et al [44].…”

“…However, the increment of sigma squared value from 0.13 to 1.6 changed the linearity of the emulator to moderate nonlinearity [65]. The average sigma squared values for sensitivity analysis are within the acceptable range reported by Gunaratne et al [44] suggesting a strong linear relationship between the log-transformed response and predictors for all models. Therefore, the sensitivity of fitted models in Shapley's sensitivity analysis were accurate and able to capture the sensitivity of all tested inputs.…”

“…There is no threshold value to explain linearity or nonlinearity of developed models to study the sensitivity of inputs [44]. However, the increment of sigma squared value from 0.13 to 1.6 changed the linearity of the emulator to moderate nonlinearity [65].…”

“…Therefore, the sensitivity of fitted models in Shapley's sensitivity analysis were accurate and able to capture the sensitivity of all tested inputs. Using GEM-SA software for sensitivity analysis, Gunarathna et al [44] studied the sensitivity of soil input parameters into rice yield in Sri Lanka. Authors reported that the highest sensitivity for rice was for DUL followed by LL15.…”

“…The AgMERRA global gridded climate forcing dataset [41] (https://data.giss.nasa.gov/impacts/ agmipcf/agmerra/) which contains all the required weather parameters for APSIM was used in simulations. Out of the several reanalysis climate databases that utilise comprehensive methods and covariates, AgMERRA was identified as a potential dataset to use in process-based crop models where observed data are not available [41][42][43] and has been used for APSIM simulations in Sri Lanka [40,44]. The effective resolution of different parameters in AgMERRA are 0.25 • for rainfall, 0.5 • for temperature and 1.0 • for solar radiation.…”