ALBERT

Description: Increasing air and soil temperatures and changes in precipitation patterns as consequences of climate change will affect crop production in agricultural ecosystems. The combined effects of soil warming and altered precipitation on the productivity and product quality of oil crops are not yet well studied. Winter oilseed rape (OSR) (Brassica napus L., cv. Mercedes) was field-grown under elevated soil temperature (+2.5 °C), reduced precipitation amount (−25%), reduced precipitation frequency (−50%) both separately and in combination in order to investigate effects on crop development, seed yield, and seed quality. Soil warming accelerated crop development during early plant growth and during spring. At maturity, however, plants in all treatments were similar in quantitative (aboveground biomass, seed yield) and qualitative (protein and oil content, amino acids, fatty acids) parameters. We observed the long-term effects of the precipitation manipulation on leaf size, leaf senescence and biomass allocation. Seed yield was not affected by the altered climatic factors, perhaps due to adaptation of soil microorganisms to permanent soil warming and to relatively wet conditions during the seed-filling period. Overall, OSR performed well under moderate changes in soil temperature and precipitation patterns; thus, we observed stable seed yield without negative impacts on nutritive seed quality.

Description: 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.