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Estimation of spatial and temporal variability of pasture growth and digestibility in grazing rotations coupling unmanned aerial vehicle (UAV) with crop simulation models

机译：用作物模拟模型估算放牧旋转中牧草轮作中牧草生长和消化率的时空变异。

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Systematic monitoring of pasture quantity and quality is important to match the herd forage demand (pasture removal by grazing or harvest) to the supply of forage with adequate nutritive value. The aim of this research was to monitor, assess and manage changes in pasture growth, morphology and digestibility by integrating information from an Unmanned Aerial Vehicle (UAV) and two process-based models. The first model, Systems Approach to Land Use Sustainability (SALUS), is a process-based crop growth model used to predict pasture regrowth based on soil, climate, and management data. The second model, Morphogenetic and Digestibility of Pasture (MDP), uses paddock-scale values of herbage mass as input to predict leaf morphogenesis and forage nutritive value. Two field experiments were carried out on tall fescue- and ryegrass-based pastures under rotational grazing with lactating dairy cattle. The first experiment was conducted at plot scale and was used to calibrate the UAV and to test models. The second experiment was conducted at field scale and was used to test the UAV’s ability to predict pasture biomass under grazing rotation. The Normalized Difference Vegetation Index (NDVI) calculated from the UAV’s multispectral reflectance (n = 72) was strongly correlated (p < 0.001) to plot measurements of pasture biomass (R2 = 0.80) within the range of ~226 and 4208 kg DM ha-1. Moreover, there was no difference (root mean square error, RMSE < 500 kg DM ha-1) between biomass estimations by the UAV (1971±350 kg ha-1) and two conventional methods used as control, the C-Dax proximal sensor (2073±636 kg ha-1) and ruler (2017±530 kg ha-1). The UAV approach was capable of mapping at high resolution (6 cm) the spatial variability of pasture (16 ha). The integrated UAV-modeling approach properly predicted spatial and temporal changes in pasture biomass (RMSE = 509 kg DM ha-1, CCC = 0.94), leaf length (RMSE = 6.2 cm, CCC = 0.62), leaf stage (RMSE = 0.7 leaves, CCC = 0.65), neutral detergent fiber (RMSE = 3%, CCC = 0.71), digestibility of neutral detergent fiber (RMSE = 8%, CCC = 0.92) and digestibility of dry matter (RMSE = 5%, CCC = 0.93) with reasonable precision and accuracy. These findings therefore suggest potential for the present UAV-modeling approach for use as decision support tool to allocate animals based on spatially and temporally explicit predictions of pasture biomass and nutritive value.
机译：对牧草数量和质量进行系统监控对于使牧草的需求（通过放牧或收割除草）与具有足够营养价值的牧草供应相匹配非常重要。这项研究的目的是通过整合无人飞行器（UAV）和两个基于过程的模型中的信息来监视，评估和管理牧场生长，形态和消化率的变化。第一个模型是土地利用可持续性系统方法（SALUS），它是一种基于过程的作物生长模型，用于基于土壤，气候和管理数据来预测牧场的再生长。第二个模型是牧场的形态发生和消化率（MDP），它使用草场规模的牧草量值作为输入来预测叶片形态发生和牧草营养价值。在泌乳奶牛的旋转放牧条件下，在高羊茅和黑麦草为基础的牧场上进行了两个野外试验。第一个实验是在地块规模上进行的，用于校准无人机和测试模型。第二个实验是在野外进行的，用于测试无人机在放牧旋转下预测草场生物量的能力。根据无人机的多光谱反射率（n = 72）计算出的归一化植被指数（NDVI）与草地生物量（R 2 = 0.80）的测量值之间具有很强的相关性（p <0.001）。 〜226和4208 kg DM ha -1 。此外，UAV估算的生物量之间没有差异（均方根误差，RMSE <500 kg DM ha -1 ）（1971±350 kg ha -1 ） C-Dax近端传感器（2073±636 kg ha -1 ）和标尺（2017±530 kg ha -1 ）是两种常规控制方法。 UAV方法能够以高分辨率（6 cm）绘制牧场（16 ha）的空间变化图。集成的UAV建模方法可以正确预测牧场生物量（RMSE = 509 kg DM ha -1 ，CCC = 0.94），叶长（RMSE = 6.2 cm，CCC = 0.62）的时空变化，叶期（RMSE = 0.7叶片，CCC = 0.65），中性洗涤剂纤维（RMSE = 3％，CCC = 0.71），中性洗涤剂纤维的消化率（RMSE = 8％，CCC = 0.92）和干物质的消化率（RMSE = 5％，CCC = 0.93），并且具有合理的精度和准确性。因此，这些发现暗示了目前的UAV建模方法的潜力，该方法可作为基于牧场生物量和营养价值的时空明确预测的动物分配决策支持工具。

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