Monitoring and management of plant water status over the critical period between flowering and veraison, plays a significant role in producing grapes of premium quality. Hyperspectral spectroscopy has been widely studied in precision farming, including for the prediction of grapevine water status. However, these studies were presented based on various combinations of transformed spectral data, feature selection methods, and regression models. To evaluate the performance of different modeling pipelines for estimating grapevine water status, a study spanning the critical period was carried out in two commercial vineyards at Martinborough, New Zealand. The modeling used six hyperspectral data groups (raw reflectance, first derivative reflectance, second derivative reflectance, continuum removal variables, simple ratio indices, and vegetation indices), two variable selection methods (Spearman correlation and recursive feature elimination based on cross-validation), an ensemble of selected variables, and three regression models (partial least squares regression, random forest regression, and support vector regression). Stem water potential (used as a proxy for vine water status) was measured by a pressure bomb. Hyperspectral reflectance was undertaken by a handheld spectroradiometer. The results show that the best predictive performance was achieved by applying partial least squares regression to simple ratio indices (R² = 0.85; RMSE = 110 kPa). Models trained with an ensemble of selected variables comprising multicombination of transformed data and variable selection approaches outperformed those fitted using single combinations. Although larger data sizes are needed for further testing, this study compares 38 modeling pipelines and presents the best combination of procedures for estimating vine water status. This may lead to the provision of rapid estimation of vine water status in a nondestructive manner and highlights the possibility of applying hyperspectral data to precision irrigation in vineyards.

The development of the Canopy Pasture Probe (CAPP), for acquisition of in situ pasture canopy reflectance factor, required a suitable large reflectance standard. Spectralon^® has been successfully used worldwide as a reflectance standard, but large panels (greater than 20 cm diameter) suitable for use with the CAPP are very expensive. In this context, a large, durable and low cost reflectance standard has been evaluated for use with an Analytical Spectral Devices FieldSpec^® Pro FR spectroradiometer attached to the CAPP. In this study various ceramic tiles, barium sulphate powders, white paint and a Kodak card were tested. The material which best suited the requirements for a reflectance standard was the white ceramic tile sourced from Argentina. This tile produced around 80% total reflectance with reasonable uniformity over the spectral range analysed (400–2400 nm); and the vegetation spectra acquired using this tile as reference sample were very similar in shape to the vegetation spectra acquired using spectralon^® as a white reference.

Monitoring and management of grapevine water status (GWS) over the critical period between flowering and veraison plays a significant role in producing grapes of premium quality. Although unmanned aerial vehicles (UAVs) can provide efficient mapping across the entire vineyard, most commercial UAV-based multispectral sensors do not contain a shortwave infrared band, which makes the monitoring of GWS problematic. The goal of this study is to explore whether and which of the ancillary variables (vegetation characteristics, temporal trends, weather conditions, and soil/terrain data) may improve the accuracy of GWS estimation using multispectral UAV and provide insights into the contribution, in terms of direction and intensity, for each variable contributing to GWS variation. UAV-derived vegetation indices, slope, elevation, apparent electrical conductivity (EC_a), weekly or daily weather parameters, and day of the year (DOY) were tested and regressed against stem water potential (Ψstem), measured by a pressure bomb, and used as a proxy for GWS using three machine learning algorithms (elastic net, random forest regression, and support vector regression). Shapley Additive exPlanations (SHAP) analysis was used to assess the relationship between selected variables and Ψstem. The results indicate that the root mean square error (RMSE) of the transformed chlorophyll absorption reflectance index-based model improved from 213 to 146 kPa when DOY and elevation were included as ancillary inputs. RMSE of the excess green index-based model improved from 221 to 138 kPa when DOY, elevation, slope, EC_a, and daily average windspeed were included as ancillary inputs. The support vector regression best described the relationship between Ψstem and selected predictors. This study has provided proof of the concept for developing GWS estimation models that potentially enhance the monitoring capacities of UAVs for GWS, as well as providing individual GWS mapping at the vineyard scale. This may enable growers to improve irrigation management, leading to controlled vegetative growth and optimized berry quality.

Nutrient loss in surface runoff from hill country catchments is typically generated from small areas and over short time periods. This study compared nutrient and sediment losses in surface runoff when cattle were fed winter hay supplement on two hill country sub‐catchments (∼0.3 ha) with contrasting soil types (imperfectly‐drained and well‐drained soils). Runoff samples were collected during seven events (June‐August). During this period, two herds of 16 pregnant, mixed aged Angus cows were supplemented with 2 kg DM. cow day ⁻¹ of hay in a defined feeding area within each sub‐catchment. The imperfectly‐drained soil measured 4.8 times the volume of surface runoff compared to the well‐drained soil. As a result, the imperfectly‐drained soil lost 2.5 times the amount of sediment, 6.3 and 5.1 times the amount of total phosphorus (TP) and dissolved reactive phosphorus respectively and 4.5 times the amount of total nitrogen (TN). Surface runoff losses of nitrate‐N from the well‐drained soil were undetectable over the study period. Whilst overall nutrient losses were low over the short study period (0.22 kg TP ha ⁻¹ and 0.68 kg TN ha ⁻¹ ), the results highlight the potential benefit of strategically placing cattle feed supplements on soils less prone to surface runoff to improve freshwater outcomes.