The demand for dairy products is ever increasing across the world. The livestock sector is a significant source of greenhouse gas (GHG) emissions globally. The availability of high-quality pasture is a key requirement to increase the productivity of dairy cows as well as manage enteric methane emissions. Warm-season perennial grasses are the dominant forages in tropical and subtropical regions, and thus exploring their nutritive characteristics is imperative in the effort to improve dairy productivity. Therefore, we have collated a database containing a total of 4750 records, with 1277 measurements of nutritive values representing 56 tropical pasture species and hybrid cultivars grown in 26 different locations in 16 countries; this was done in order to compare the nutritive values and GHG production across different forage species, climatic zones, and defoliation management regimes. Average edaphoclimatic (with minimum and maximum values) conditions for tropical pasture species growing environments were characterized as 22.5 °C temperature (range 17.5–29.30 °C), 1253.9 mm rainfall (range 104.5–3390.0 mm), 582.6 m elevation (range 15–2393 m), and a soil pH of 5.6 (range 4.6–7.0). The data revealed spatial variability in nutritive metrics across bioclimatic zones and between and within species. The ranges of these nutrients were as follows: neutral detergent fibre (NDF) 50.9–79.8%, acid detergent fibre (ADF) 24.7–57.4%, crude protein (CP) 2.1–21.1%, dry matter (DM) digestibility 30.2–70.1%, metabolisable energy (ME)3.4–9.7 MJ kg⁻¹ DM, with methane (CH₄) production at 132.9–133.3 g animal⁻¹ day⁻¹. The arid/dry zone recorded the highest DM yield, with decreased CP and high fibre components and minerals. Furthermore, the data revealed that climate, defoliation frequency and intensity, in addition to their interactions, have a significant effect on tropical pasture nutritive values and CH₄ production. Overall, hybrid and newer tropical cultivars performed well across different climates, with small variations in herbage quality. The current study revealed important factors that affect pasture nutritive values and CH₄ emissions, with the potential for improving tropical forage through the selection and management of pasture species.

The vast majority of farmers in the drylands are resource-poor smallholders, whose livelihoods depend heavily on their farming systems. Therefore, increasing the resilience of these smallholders is vital for their prosperity. This study quantified household resilience and identified livelihoods and their influence on resilience in the semiarid tropics of India by analysing 684 households. A resilience capacity index was devised based on the composition of household food and non-food expenditure, cash savings, and food and feed reserves. The index ranged from 8.4 reflecting highly resilient households with access to irrigation characteristics, to −3.7 for households with highly limited resilience and low household assets. The livelihoods were identified through multivariate analysis on selected socioeconomic and biophysical variables; households were heterogeneous in their livelihoods. Irrigated livestock and rainfed marginal types had the highest and lowest resilience capacity index with the mean score of 0.69 and −1.07, respectively. Finally, we quantified the influence of livelihood strategies on household resilience. Household resilience was strengthened by the possession of livestock, crop diversification and access to irrigation. Low resilience is predominantly caused by low household assets. The resilience capacity index and derived livelihood strategies helps to understand the complexity of household resilience, and will aid in targeting technology interventions for development.

Wine grape quality heavily influences the price received for a product. Hyperspectral imaging has the potential to provide a non-destructive technique for predicting various enological parameters. This study aims to explore the feasibility of applying hyperspectral imaging to measure the total soluble solids (TSS) and titratable acidity (TA) in wine grape berries. A normalized difference spectral index (NDSI) spectral preprocessing method was built and compared with the conventional preprocessing method: multiplicative scatter correction and Savitzky–Golay smoothing (MSC+SG). Different machine learning models were built to examine the performance of the preprocessing methods. The results show that the NDSI preprocessing method demonstrated better performance than the MSC+SG preprocessing method in different classification models, with the best model correctly classifying 93.8% of the TSS and 84.4% of the TA. In addition, the TSS can be predicted with moderate performance using support vector regression (SVR) and MSC+SG preprocessing with a root mean squared error (RMSE) of 0.523 °Brix and a coefficient of determination (R²) of 0.622, and the TA can be predicted with moderate performance using SVR and NDSI preprocessing (RMSE = 0.19%, R² = 0.525). This study demonstrates that hyperspectral imaging data and NDSI preprocessing have the potential to be a method for grading wine grapes for producing quality wines.