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.
The objective of this study was to compare the dynamics of neutral detergent fibre (NDF), and the 24-h in-vitro digestibility of NDF (NDFD) and dry matter (DMD) in leaf blades of two tall fescue (Lolium arundinaceum (Schreb.) Darbysh.) cultivars of different leaf softness: a soft- and a tough-leaved cultivar. The experiment was conducted during the summer regrowth of three replicated, dense mini-swards per cultivar arranged in a completely randomised design, all grown under non-limiting water, nitrogen and phosphorus. Cultivars were harvested eight times over 14 weeks to measure morphogenetic traits and nutritive value in six predefined leaf-age categories (from growing to complete senescence). The leaf lifespan and leaf length of the first three successive leaves were measured on 30 marked tillers throughout the experiment. Following analysis of variance, linear regression models were fitted to describe variations of NDF, NDFD and DMD with increasing leaf age and leaf length. Similar leaf NDF contents were found for the two cultivars, which remained stable throughout the leaf lifespan and increased markedly during leaf senescence. Leaf NDFD and leaf DMD both declined with increasing leaf age and length for the two cultivars. However, owing to shorter leaf lifespan of the soft-leaved cultivar, this decline in leaf NDFD and leaf DMD was faster for the soft- than for the tough-leaved cultivar. These results suggest that the soft-leaved cultivar will require more frequent defoliations than the tough-leafed cultivar to prevent decreases in nutritive value.
Core Ideas Model‐based approach identified sets of adaptive practices for pasture management across seasons. Suitable combinations of N rate and residual heights can improve the use of N fertilizer and water. The increment in residual pasture mass and N fertilizer may be crucial for more efficient use of water. Pasture growth responses to residual leaf area increased with N fertilization. The objectives of this research were to (i) evaluate the effects of N fertilizer, irrigation, and residual pasture heights on pasture growth, (ii) validate the ability of the SALUS model to predict dynamics of pasture growth, and (iii) evaluate during long‐term period the effects of using different N fertilizer levels and defoliation strategies on pasture growth, N fertilizer use, and water use efficiency (WUE). Eight single‐season experiments were performed at plot scale (8 m2) in Buenos Aires (Argentina, ARG) and Michigan. In ARG different N fertilizer rates (from 0–500 kg N ha−1) were imposed on both rainfed and irrigated tall fescue [Lolium arundinaceum (Schreb.) Darbysh.] pasture during autumn, spring, and summer. In the United States, three residual pasture height treatments (30, 60, and 120 mm) were imposed on both tall fescue and ryegrass (Lolium perenne L.) pasture in the spring and summer. The SALUS was parameterized to simulate tall fescue and ryegrass growth using soil, weather, and different pasture treatments previously tested in ARG and the United States. Results showed that the SALUS accurately represented the response of herbage mass to irrigation and added N in the ARG site (RMSE < 650 kg DM ha−1) and to differences in residual pasture heights in the U.S. experiment (RMSE < 509 kg DM ha−1). Ten‐year simulations (2000–2010) demonstrated that suitable combinations of N fertilizer and residual pasture heights can significantly improve the use of N fertilizer by ∼300% and water by ∼230% through increases in herbage production.
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