Description
The climatic variability of semiarid regions is the main source of uncertainties associated with forage and animal production, indicating a need for tools that accurately estimate forage production in order to construct a forage budgeting plan for livestock. This study simulated the biomass of Leucaena (Leucaena leucocephala) using the PHYGROW model in four locations located in Brazilian Semiarid. The work was carried out based on field data collected from 2019 to 2021. After sowing in 2018, leucaena was harvested when it reached 200 cm and to a target residual height of 70 cm. The biomass (fresh matter) was weighed, sampled and dried to obtain the total forage biomass (BFT) of each sample. The BFT was also estimated using the PHYGROW model, with field data being used to parameterize, calibrate and validate the model. The model performance, in turn, was evaluated based on the mean forecast error (BIAS %), root mean square error (RMSE) and Willmott index. Afterwards, a BFT time series was downloaded for each location, with the highest biomass simulated for each year being evaluated in the @Risk regarding their probability distribution. Thereafter, probability calculations of biomass production were performed, based on different levels of warranty in SigmaPlot software (11.0). The model underestimated the BFT collected in two locations and overestimated BFT in the others. The Weibull function was the best one to describe the data. Regarding biomass production under a 95% natural warranty, it was observed that leucaena showed low variation among locations (2240 ± 752 kg of DM ha-1 year-1 ). The PHYGROW model accurately predicted the leucaena BFT which, in turn, demonstrated significant adaptation potential to the various soil and climate conditions of Brazilian Semiarid. The use of probability analysis can contribute to forage planning, thus reducing the uncertainties related to climate variability, especially in rainfed production systems of dry areas.
Included in
Agricultural Science Commons, Agronomy and Crop Sciences Commons, Plant Biology Commons, Plant Pathology Commons, Soil Science Commons, Weed Science Commons
Modelling Leucaena Biomass Under Rainfed Production Systems of Semiarid Regions
The climatic variability of semiarid regions is the main source of uncertainties associated with forage and animal production, indicating a need for tools that accurately estimate forage production in order to construct a forage budgeting plan for livestock. This study simulated the biomass of Leucaena (Leucaena leucocephala) using the PHYGROW model in four locations located in Brazilian Semiarid. The work was carried out based on field data collected from 2019 to 2021. After sowing in 2018, leucaena was harvested when it reached 200 cm and to a target residual height of 70 cm. The biomass (fresh matter) was weighed, sampled and dried to obtain the total forage biomass (BFT) of each sample. The BFT was also estimated using the PHYGROW model, with field data being used to parameterize, calibrate and validate the model. The model performance, in turn, was evaluated based on the mean forecast error (BIAS %), root mean square error (RMSE) and Willmott index. Afterwards, a BFT time series was downloaded for each location, with the highest biomass simulated for each year being evaluated in the @Risk regarding their probability distribution. Thereafter, probability calculations of biomass production were performed, based on different levels of warranty in SigmaPlot software (11.0). The model underestimated the BFT collected in two locations and overestimated BFT in the others. The Weibull function was the best one to describe the data. Regarding biomass production under a 95% natural warranty, it was observed that leucaena showed low variation among locations (2240 ± 752 kg of DM ha-1 year-1 ). The PHYGROW model accurately predicted the leucaena BFT which, in turn, demonstrated significant adaptation potential to the various soil and climate conditions of Brazilian Semiarid. The use of probability analysis can contribute to forage planning, thus reducing the uncertainties related to climate variability, especially in rainfed production systems of dry areas.
