Near‐infrared reflectance spectroscopy (NIRS) was used to predict the chemical composition of whole maize plants (Zea mays L) in breeding programmes at INIA La Estanzuela, Uruguay. Four hundred samples (n  = 400) were scanned from 400 to 2500 nm in an NIRS 6500 monochromator (NIRSystems, Silver Spring, MD, USA). Modified partial least squares (MPLS) regression was applied to scatter‐corrected spectra (SNV and detrend). Calibration models for NIRS measurements gave multivariate correlation coefficients of determination (R2) and standard errors of cross‐validation (SECV) of 0.72 (SECV 9.5), 0.96 (SECV 7.7), 0.98 (SECV 16.5), 0.96 (SECV 34.3), 0.98 (SECV 17.8) and 0.98 (SECV 6.1) for dry matter (DM), crude protein (CP), acid detergent fibre (ADF), neutral detergent fibre (NDF), in vitro organic matter digestibility (IVOMD) and ash in g kg−1 on a dry weight basis respectively. This paper shows the potential of NIRS to predict the chemical composition of whole maize plants as a routine method in breeding programmes and for farmer advice. © 2000 Society of Chemical Industry

Near-infrared re¯ectance spectroscopy (NIRS) was used to predict the chemical composition of whole maize plants (Zea mays L) in breeding programmes at INIA La Estanzuela, Uruguay. Four hundred samples (n = 400) were scanned from 400 to 2500 nm in an NIRS 6500 monochromator (NIRSystems, Silver Spring, MD, USA). Modi®ed partial least squares (MPLS) regression was applied to scatter-corrected spectra (SNV and detrend). Calibration models for NIRS measurements gave multivariate correlation coef®cients of determination (R 2 ) and standard errors of cross-validation (SECV) of 0.72 (SECV 9.5), 0.96 (SECV 7.7), 0.98 (SECV 16.5), 0.96 (SECV 34.3), 0.98 (SECV 17.8) and 0.98 (SECV 6.1) for dry matter (DM), crude protein (CP), acid detergent ®bre (ADF), neutral detergent ®bre (NDF), in vitro organic matter digestibility (IVOMD) and ash in g kg À1 on a dry weight basis respectively. This paper shows the potential of NIRS to predict the chemical composition of whole maize plants as a routine method in breeding programmes and for farmer advice.

This study analyses variability and trends of atmospheric evaporative demand (AED) across Uruguay in the past four decades. Changes were assessed using pan evaporation measurements from 10 meteorological stations and compared to PenPan model calculations, which is a physically based model that employs meteorological data as input. Results demonstrate a high agreement between the observed AED and those estimated from the PenPan model. Both observations and model estimations agree on a high interannual variability in AED, though being statistically insignificant (p > 0.05) at seasonal and annual scales. Given that AED shows high sensitivity to changes in relative humidity and sunshine duration, as a surrogate of solar radiation, the lack of significant trends in the AED observations and estimations over Uruguay can be linked to the insignificant trend found for these climate variables for the period from 1973 to 2014. This is the first study that reports Pan evaporation trends for this part of the world, helping to infill gaps for mid-latitude Southern Hemisphere areas, which are poorly represented in Pan evaporation trends.

Most countries lack effective policies to manage climate risks, despite growing concerns with climate change. The authors analyzed the policy evolution from a disaster management to a risk management approach, using as a case study four agricultural droughts that impacted Uruguay’s livestock sector in the last three decades. A transdisciplinary team of researchers, extension workers, and policy makers agreed on a common conceptual framework for the interpretation of past droughts and policies. The evidence presented shows that the set of actions implemented at different levels when facing droughts were mainly reactive in the past but later evolved to a more integral risk management approach. A greater interinstitutional integration and a decreasing gap between science and policy were identified during the period of study. Social and political learning enabled a vision of proactive management and promoted effective adaptive measures. While the government of Uruguay explicitly incorporated the issue of adaptation to climate change into its agenda, research institutions also fostered the creation of interdisciplinary study groups on this topic, resulting in new stages of learning. The recent changes in public policies, institutional governance, and academic research have contributed to enhance the adaptive capacity of the agricultural sector to climate variability, and in particular to drought. This study confirms the relevance of and need to work within a transdisciplinary framework to effectively address the different social learning dimensions, particularly those concerning the adaptation to global change.

The aim of this study was to investigate the potential use of near infrared (NIR) reflectance spectroscopy to predict chemical composition in both sunflower whole plant (WPSun) and sunflower silage (SunS). Samples of both WPSun ( n = 73) and SunS ( n = 50) were analysed by reference method and scanned in reflectance using a NIR monochromator instrument (400–2500 nm). Calibration models were developed between NIR data and reference values for dry matter (DM), crude protein (CP), ash, acid detergent fibre (ADFom), neutral detergent fibre (aNDFom), in vitro organic matter digestibility (OMD), ether extract (EE) and pH using partial least squares regression (PLS). Due to the limited number of samples full cross-validation was used to test the calibration models. The best correlations (R 2cal) and lowest standard errors in cross-validation (SECV) were obtained for DM (R 2cal > 0.82, SECV: 27.0 and 35.8 g kg−1), CP (R 2cal> 0.85, SECV: 9.9 and 10.1 g kg−1) and ash (R2cal> 0.85, SECV 11.2 and 8.2 g kg−1) in both WPSun and SunS samples, respectively. For ADFom, aNDFom and OMD the calibrations were considered to be poor (R 2cal < 0.85). In SunS samples a good correlation was found for EE (R 2cal = 0.94, SECV: 15.3 g kg−1).