Abstract
The analysis of multispectral imagery (MSI) acquired by unmanned aerial vehicles (UAVs) and mobile near-infrared reflectance spectroscopy (NIRS) used on-site has become increasingly promising for timely assessments of grassland to support farm management. However, a major challenge of these methods is their calibration, given the large spatiotemporal variability of grassland. This study evaluated the performance of two smart farming tools in determining fresh herbage mass and grass quality (dry matter, crude protein, and structural carbohydrates): an analysis model for MSI (GrassQ) and a portable on-site NIRS (HarvestLabTM 3000). We compared them to conventional look-up tables used by farmers. Surveys were undertaken on 18 multi-species grasslands located on six farms in Switzerland throughout the vegetation period in 2018. The sampled plots represented two phenological growth stages, corresponding to an age of two weeks and four to six weeks, respectively. We found that neither the performance of the smart farming tools nor the performance of the conventional approach were satisfactory for use on multi-species grasslands. The MSI-model performed poorly, with relative errors of 99.7% and 33.2% of the laboratory analyses for herbage mass and crude protein, respectively. The errors of the MSI-model were indicated to be mainly caused by grassland and environmental characteristics that differ from the relatively narrow Irish calibration dataset. The On-site NIRS showed comparable performance to the conventional Look-up Tables in determining crude protein and structural carbohydrates (error ≤ 22.2%). However, we identified that the On-site NIRS determined undried herbage quality with a systematic and correctable error. After corrections, its performance was better than the conventional approach, indicating a great potential of the On-site NIRS for decision support on grazing and harvest scheduling.
Highlights
For many years, indoor housing has been preferred by dairy producers because feeding and caretaking on an individual animal level was simpler and more labor-saving compared to grazing-based systems [1,2]
This study evaluated the performance of two smart farming tools in determining fresh herbage mass and grass quality: an analysis model for multispectral imagery (MSI) (GrassQ) and a portable on-site near-infrared reflectance spectroscopy (NIRS) (HarvestLabTM 3000)
For acid detergent fiber (ADF) and neutral detergent fiber (NDF), rp and correlation coefficient (CCC) were slightly lower than for crude protein (CP) and crude fiber (CF) (ADF: rp 0.896, CCC 0.89; NDF: rp 0.906, CCC 0.91), which shows a moderate concordance for ADF
Summary
Indoor housing has been preferred by dairy producers because feeding and caretaking on an individual animal level was simpler and more labor-saving compared to grazing-based systems [1,2]. Due to modern technology that addresses both labor requirements and precision feed supply on pastures, grazing-based dairy production systems are viable again [3,4]. The spatial variability of grassland yield is very large [8] As it is time-consuming to accurately measure herbage growth or grassland yields, farmers usually settle for a rough estimation. Many farmers estimate standing herbage mass from visual observation or increasingly by means of a rising plate meter [9]. To accurately estimate herbage mass, the rising plate meter requires labor-intensive re-calibration for assessing the specific plots [5], which leads to a trade-off between labor costs and accuracy. Only suited for measuring low grasslands in the early growth stages [9]
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