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

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.

Development and Validation of a Model to Combine NDVI and Plant Height for High-Throughput Phenotyping of Herbage Yield in a Perennial Ryegrass Breeding Program

“…However, the number of plants for GS is likely higher than the number of plants traditionally used by breeders to perform selection breeding (e.g., The DairyBio initiative has 48,000 individual plants for genomic sub-selection breeding) where phenotyping of individual plants require accurate evaluation [5]. In recent years, high-throughput phenotyping (HTP) technologies have brought new insights to evaluate phenotypic traits efficiently in large breeding programs [6][7][8][9].Previous studies have used sensor-based data sources from aerial and ground-based platforms to estimate biophysical characteristics of various vegetations, including herbage yield of forage crops [5,10,11]. The aerial-based phenotyping platforms are suitable for lightweight red-green-blue (RGB), multispectral, and hyperspectral imaging systems and have used vegetative indices to build models for herbage yield [10,12] and biomass [13-15] estimation of pasture and cereal crops respectively.…”

“…Previous studies have used sensor-based data sources from aerial and ground-based platforms to estimate biophysical characteristics of various vegetations, including herbage yield of forage crops [5,10,11]. The aerial-based phenotyping platforms are suitable for lightweight red-green-blue (RGB), multispectral, and hyperspectral imaging systems and have used vegetative indices to build models for herbage yield [10,12] and biomass [13-15] estimation of pasture and cereal crops respectively.…”

Field indicators of leaf nutritive value for perennial ryegrass and tall fescue pastures under different growing and management conditions

“…Therefore, we might expect relatively less variation in leaf herbage nutritive value and animal performance in dairy and livestock grazing systems that offer perennial ryegrass pasture of varying residual sward heights, than in tall fescue pasture with the same levels of variation in residual sward heights. Thus, on the basis of current results and those of Insua, Utsumi, and Basso () it appears that the residual sward height remaining after grazing will have a more pronounced impact on the herbage nutritional value of tall fescue compared to perennial ryegrass. From a management point of view, this finding also highlights the relevance of lowering residual sward heights as an effective practice in tall fescue in order to reduce leaf length and improve forage nutritive value to similar levels as those found in perennial ryegrass.…”

ОПЕРАТИВНОЕ И ДОЛГОСРОЧНОЕ ПРОГНОЗИРОВАНИЕ ПРОДУКТИВНОСТИ ПОСЕВОВ НА ОСНОВЕ МАССОВЫХ РАСЧЕТОВ ИМИТАЦИОННОЙ МОДЕЛИ АГРОЭКОСИСТЕМЫ В ГЕОИНФОРМАЦИОННОЙ СРЕДЕ (Обзор)

“…Следствием повышения качества моделей агроэкосистем, а также развития подходов в теории математического моделирования процессов и явлений в системе «почва-растение-атмосфера», является то, что перечень сельскохозяйственных культур и почвенно-климатических условий, используемых в исследованиях с использованием семейств универсальных экофизиологических моделей, становится практически неограниченным (5). Кроме того, большинство современных моделей агроэкосистем основано на все бóльшем числе определяющих процессов и явлений физической и биологической природы (4)(5)(6)(7). Поэтому такие модели, наряду с продуктивностью (урожайностью), позволяют количественно оценивать и иные характеристики агроэкосистем.…”

“…Также все более популярна идея существенного варьирования временнóго и пространственного масштаба динамических моделей, в частности выполнения расчетов не только для одной культуры в одной географической точке для одного срока вегетации, но и для произвольно выбранных периодов времени и территорий. Иначе говоря, под пространственным расширением понимается одновременный расчет пространственно-одномерной модели не только для одной выбранной представительной точки земной поверхности с конкретно заданными свойствами почвы и местности, но и для представительного набора таких точек, формирующих неоднородный агроландшафт (6,7). При этом в отличие от классических одномерных моделей однородного сельскохозяйственного посева, динамические модели агроэкосистем третьего поколения позволяют описывать адаптивно-ландшафтные системы земледелия (5).…”