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

Date Published: March 13, 2019

Publisher: Public Library of Science

Author(s): Juan R. Insua, Santiago A. Utsumi, Bruno Basso, Juan J Loor.

http://doi.org/10.1371/journal.pone.0212773

Abstract

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.

Partial Text

In most livestock systems, animal feed represents the highest proportion of variable costs. Therefore, a general aim for most grazing-based animal production systems is to maximize profitability by increasing the amount of homegrown forages converted into animal product (meat or milk). Livestock grazing-based systems are required to provide a large quantity of forage of high-nutritive value in the most efficient and cost-effective way. Frequent monitoring of pasture cover is one way to schedule grazing rotations and to allocate forage according to the herd forage demand (pasture removal by grazing or harvest). A proactive approach to allocate pasture forage to animals must consider grazing management as a set of dynamic decisions that take into account the temporal and spatial variation of pasture growth associated mainly to weather, soil nutrients and grazing management factors. However, this approach can be time consuming and requires adequate methods and techniques to systematically monitor changes in pasture cover [1].

The aim of this work was to develop and test an integrated systems approach to monitor, assess and plan grazing management on farms (Fig 1). This novel system is based on the integration of remotely collected data by UAV-mounted sensors and process-based models. Overall, the results reported in this paper provide sufficient evidence both to support the feasibility of the proposed approach and its validation by using data both from plot and field scale experiments. The results also suggest potential for scalability of the present UAV- modeling approach to other grass species, soils and weather conditions, but the testing and validation of this hypothesis warrants further carefully conducted investigations.

The present UAV-modeling approach integrated pasture NDVI data remotely collected by UAV-mounted sensors and process-based models for prediction of pasture growth, morphogenesis, and forage nutritive value. Both, results from plot and filed scale experiments provide sufficient evidence to support feasibility for the UAV-modeling approach and potential for broad scalability to other grass species, soil types and weather conditions, but the testing and validation of this hypothesis warrants further carefully conducted investigations.

 

Source:

http://doi.org/10.1371/journal.pone.0212773

 

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