Simulation of Crop Productivity for Guinea Grass (Megathyrsus maximus) Using AquaCrop under Different Water Regimes

Abstract

The perennial herbaceous forage crops’ (PHFC) biomass as bioindustry feedstock or source of nutrients for ruminants is very important from their final utilization point of view. In 2022, the AquaCrop-FAO version 7.0 model has been opened for PHFC. In this context, this study aimed to test for the first time the ability of the AquaCrop-FAO model to simulate canopy cover (CC), total available soil water (TAW), and biomass (B) of Guinea grass (Megathyrsus maximus cv. Agrosavia sabanera) under different water regimes at the Colombian dry Caribbean, South America. The water regimes included L1—irrigation based on 80% field capacity (FC), L2—irrigation based on 60% FC, L3—irrigation based on 50% FC, L4—irrigation based on 40% FC, L5—irrigation based on 20% FC, and L6—rainfed. The AquaCrop model uses the normalized water productivity—WP* (g m−2)—to estimate the attainable rate of crop growth under water limitation. The WP* for Guinea grass was 35.9 ± 0.42 g m−2 with a high coefficient of determination (R2 = 0.94). The model calibration results indicated the simulated CC was good (R2 = 0.84, RMSE = 17.4%, NRMSE = 23.2%, EF = 0.63 and d = 0.91). In addition, cumulative biomass simulations were very good (R2 = 1.0, RMSE = 5.13 t ha−1, NRMSE = 8.0%, EF = 0.93 and d = 0.98), and TAW was good (R2 = 0.85, RMSE = 5.4 mm, NRMSE = 7.0%, EF = 0.56 and d= 0.91). During validation, the CC simulations were moderately good for all water regimes (0.78 < R2 < 0.97; 12.0% < RMSE < 17.5%; 15.9% < NRMSE < 28.0%; 0.47 < EF < 0.87; 0.82 < d < 0.97), the cumulative biomass was very good (0.99 < R2 < 1.0; 0.77 t ha−1 < RMSE < 3.15 t ha−1; 2.5% < NRMSE < 21.9%; 0.92 < EF < 0.99; 0.97 < d < 1.0), and TAW was acceptable (0.70 < R2 < 0.90; 5.8 mm < RMSE < 21.7 mm, 7.6% < NRMSE < 36.7%; 0.15 < EF < 0.58 and 0.79 < d < 0.9). The results of this study provide an important basis for future research, such as estimating biomass production of high-producing grasses in tropical environments, yield effects under scenarios of climate variability, and change based on the presented parameterization and considering a wide range of environments and grazing variations.

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

“…Terán-Chaves et al ( 2023), in their study [11], focused on assessing the capability of the AquaCrop-FAO model to predict the biomass (B) of Guinea grass (Megathyrsus maximus cv. Agrosavia sabanera) for the first time in the dry Caribbean region of Colombia, South America.…”

“…Therefore, recent studies have begun to investigate within-field relationships using satellitederived vegetation indices, paving the way for integrating grassland monitoring into precision agriculture practices [7][8][9][10]. Several established models have been successfully modified for grass application, such as ALMANAC, CROPGRO-PFM Perennial Forage of DSSAT, the AgPasture model integrated within the APSIM package, and the AquaCrop model [11]. The main reason for choosing the AquaCrop model for predicting grasslands lies in the critical role of soil and climate data in biomass estimation.…”