Prediction of Brachiaria decumbens forage biomass using structural characteristics

Jefte A. de A. Conrado; Marcos N. Lopes; Magno José Duarte Cândido; Vitor H.M. Macedo; Valdson J. da Silva; Vitória G. Damasceno

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Prediction of Brachiaria decumbens forage biomass using structural characteristics

Jefte A. de A. Conrado ^[1] ; Marcos N. Lopes ^[4] ; Magno J.D. Cândido ^[1] ; Vitor H.M. Macedo ^[2] ; Valdson J. da Silva ^[3] ; Vitória G. Damasceno ^[1]
1. [1] Universidade Federal do Ceará
  
  Universidade Federal do Ceará
  
  Brasil
2. [2] Universidade Federal Rural da Amazônia
  
  Universidade Federal Rural da Amazônia
  
  Brasil
3. [3] Universidade Federal Rural de Pernambuco
  
  Universidade Federal Rural de Pernambuco
  
  Brasil
4. [4] Instituto Federal do Piauí
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Localización: Agricultura técnica = Chilean Journal of Agricultural Research, ISSN-e 0718-5839, ISSN 0365-2807, Vol. 81, Nº. 3, 2021, págs. 467-478
Idioma: inglés
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Resumen
- Tools that generate models with good biomass predictive capacity are essential to maintain the sustainability of production systems. The objective was to analyze the relationship between forage biomass and structural variables and generate models to predict total forage biomass (TFB) and green leaf blade biomass (GLB). Irrigated pastures of Brachiaria decumbens Stapf ‘Basilisk’ were kept under rotational stocking with sheep (Ovis aries L.) The TFB, GLB, leaf area index (LAI), height (cm), and normalized difference vegetation index (NDVI) were evaluated. The experimental design was completely randomized with four replicates: ten and five cycles of defoliation management, respectively, were used to generate and validate the stages of the models. The best goodness of fit was obtained by nonlinear models for both TFB and GLB, which can be confirmed by high Spearman’s correlations and significance (P < 0.0001). The path analysis showed low collinearity (42.60) between NDVI, LAI, and height; the high determination coefficient (R²) with values of 0.8421 and 0.7767 demonstrated their associations with TFB and GLB, respectively. Among the studied models to predict TFB and GLB, only exponentials using NDVI and power using LAI and height showed the best fit. In the validation stage, the models related to height exhibited the highest performance with 0.9531 (TFB) and 0.9638 (GLB) d-index, -2.3 (TFB) and -7.20 (GLB) bias, and 0.8532 (TFB) and 0.8932 (GLB) R². Only nonlinear models using height (cm) to predict TFB and GLB had the best practical application potential, thus ensuring efficiency in data collection.