Canopy temperature (Tc) by thermal imaging is a useful tool to study plant water status and estimate other crop traits. This work seeks to estimate grain yield (GY) and carbon discrimination (Δ¹³C) from stress degree day (SDD = Tc − air temperature, Ta), considering the effect of a number of environmental variables such as the averages of the maximum vapor pressure deficit (VPDmax) and the ambient temperature (Tmax), and the soil water content (SWC). For this, a set of 384 and a subset of 16 genotypes of spring bread wheat were evaluated in two Mediterranean-climate sites under water stress (WS) and full irrigation (FI) conditions, in 2011 and 2012, and 2014 and 2015, respectively. The relationship between the GY of the 384 wheat genotypes and SDD was negative and highly significant in 2011 (r² = 0.52 to 0.68), but not significant in 2012 (r² = 0.03 to 0.12). Under WS, the average GY, Δ¹³C, and SDD of wheat genotypes growing in ten environments were more associated with changes in VPDmax and Tmax than with the SWC. Therefore, the amount of water available to the plant is not enough information to assume that a particular genotype is experiencing a stress condition.

Rice is an essential diet component for a significant portion of the population worldwide. Due to the high water demand associated with rice production, improving water use efficiency and grain quality is critical to increasing the sustainability of the crop. This species includes rice varieties with diverse pigmentation patterns. Grain quality, including industrial, nutritional, and functional quality traits, of two black rice genotypes and a commercial white rice cultivar were evaluated in different locations and under different water regimes. Flooding produced higher grain weight compared to alternate wetting and drying irrigation. A high correlation was found between grain color, total phenolic content (TPC), and antioxidant activity. The black rice genotypes showed higher TPC levels and antioxidant capacity, mainly due to higher levels of cyanidin 3-O-glucoside. The phenolic profile varied between whole and polished grains, while mineral composition was influenced by location and irrigation regime. In turn, the environment influenced grain quality in terms of industrial and nutritional characteristics, with significant differences in quality between whole and polished grains. This study provides valuable information on the genotype–environment relationship in rice and its effect on grain quality, which could contribute to selecting genotypes for an appropriate environment.

Lucerne (alfalfa, Medicago sativa L.) is grown extensively worldwide owing to its high forage biomass production and nutritional value. Although this crop is characterised as being tolerant to drought, its production and persistence are affected by water stress. Selection of genotypes of high yield potential and persistence after a long period of drought is a major objective for lucerne-breeding programmes in Mediterranean environments. This selection could be enhanced and accelerated by the use of physiological and productivity traits and their estimation through remote-sensing methods. A set of nine cultivars of lucerne from Australia and the USA were assessed in four locations in Mediterranean central-south Chile. Several physiological and productivity traits were evaluated: forage yield (FY), stomatal conductance (gs), water potential (WP), leaf area index (LAI), nitrogen (N) content, and isotope composition (δ13C and δ18O) of the dry matter. Spectral-reflectance data were used to estimate the traits through spectral-reflectance indices (SRIs) and multivariate regression methods. For the SRI-based estimations, the R2 values for each assessment were <0.65. However, traits such as LAI, WP, gs, and N content showed higher R2 values when data from the different assessments were combined. Regression-based estimation showed prediction power similar to or higher than the SRI-based approaches. The highest R2 value was for δ13C (0.78), but for most traits the combination of data from different assessments led to higher trait estimation, with respective R2 values for LAI, FY, WP and gs of 0.67, 0.71, 0.63 and 0.85. Among regression methods, the best estimation was achieved by using support vector machine regression. The use of spectral-reflectance data collected at field level and multivariate regression models has great potential to estimate physiological and productivity traits in lucerne under water deficit and could be useful in lucerne-breeding programmes.