Calibration and Validation of the FAO AquaCrop Water Productivity Model for Perennial Ryegrass (Lolium perenne L.)

Abstract

Crop models that can accurately estimate yield and final biomass have been used for major herbaceous crops and to a lesser extent in forage systems. The AquaCrop version 7.0 contains new modules that have been introduced to simulate the growth and production of perennial herbaceous forage crops. Simulated forage yields as a function of water consumption provide valuable information that allows farmers to make decisions for adapting to both climate variability and change. The study aimed to calibrate and validate the AquaCrop model for perennial ryegrass (Lolium perenne L.) in the high tropics of Colombia (South America). The experiments were conducted during two consecutive seasons, in which perennial ryegrass meadows were subjected to two irrigation regimes: full irrigation and no irrigation. The model was evaluated using precision, accuracy, and simulation error indices. The overall performance of AquaCrop in simulating canopy cover, biomass and soil water content showed a good match between measured and simulated data. The calibration results indicated an acceptable measurement of simulated canopy cover (CC) (R2 = 0.95, d-index = 0.41, RMSE = 9.4%, NRMSE = 12.2%, and FE = −21.72). The model satisfactorily simulated cumulative dry mass (R2 = 0.95, d-index = 0.98, RMSE = 2. 63 t ha−1, NRMSE = 11.8%, and FE = 0.94). Though the biomass values obtained in the end-of-season cuts were underestimated by the model, soil water content was simulated with reasonable accuracy (R2 = 0.82, d-index = 0.84, RMSE = 6.10 mm, NRMSE = 4.80%, and FE = 0.32). During validation, CC simulations were good, except under water deficit conditions, where model performance was poor (R2 = 0.42, d-index = 0.01, RMSE = 40.60%, NRMSE = 40.90%, and FE = −25.71); biomass and soil water content simulations were reasonably good. The above results confirmed AquaCrop’s (v 7.0) suitability for simulating responses to water for perennial ryegrass. A single crop file was developed for managing a full season and can be confidently applied to direct future research to improve the understanding of the necessary processes and interactions for the development of perennial herbaceous forage crops.

Evaluation of AquaCrop’s Ability to Simulate Water Stress Based on 2-Year Case Study of Maize Crop

“…For the experimental results shown in Figure 2, we observe that AquaCrop performs well in simulating the total soil moisture content change process module during crop growth, and the trend of the simulated change curve is in line with that of the measured results. This result is similar to that of the studies by Wang [30], Augusto [31], and Amiri [32]. However, the overall measured results are generally slightly lower than the simulation results, of which there are two possible causes.…”

Assessment of Grassland Biomass Prediction Using AquaCrop Model: Integrating Sentinel-2 Data and Ground Measurements in Wielkopolska and Podlasie Regions, Poland

“…The objective of the study conducted by Terán-Chaves et al [37] in 2022 was to calibrate and validate the AquaCrop model for perennial ryegrass in the high tropics of Colombia (South America) over two consecutive seasons, spanning from 2008 to 2010. The experiments conducted during this period in perennial ryegrass meadows showed favorable statistical indicators, with R 2 = 0.95 and RMSE = 2.63 t•ha − 1 .…”

A mini-review on data science approaches in crop yield and disease detection

“…In crop modelling with MDMs, the trial-and-error procedure is the most used (Della Nave et al, 2022;Rai et al, 2022;Terań-Chaves et al, 2022;Alvar-Beltrań et al, 2023) where the authors use historical data or build new experiments to achieve their prediction goals. Statistical procedures can be employed in the input value selection phase to facilitate uncertainty quantification in predictions.…”