Plant breeding based on grain yield (GY) is an expensive and time-consuming method, so new indirect estimation techniques to evaluate the performance of crops represent an alternative method to improve grain yield. The present study evaluated the ability of canopy reflectance spectroscopy at the range from 350 to 2500 nm to predict GY in a large panel (368 genotypes) of wheat (Triticum aestivum L.) through multivariate ridge regression models. Plants were treated under three water regimes in the Mediterranean conditions of central Chile: severe water stress (SWS, rain fed), mild water stress (MWS; one irrigation event around booting) and full irrigation (FI) with mean GYs of 1655, 4739, and 7967 kg∙ha⁻¹, respectively. Models developed from reflectance data during anthesis and grain filling under all water regimes explained between 77% and 91% of the GY variability, with the highest values in SWS condition. When individual models were used to predict yield in the rest of the trials assessed, models fitted during anthesis under MWS performed best. Combined models using data from different water regimes and each phenological stage were used to predict grain yield, and the coefficients of determination (R²) increased to 89.9% and 92.0% for anthesis and grain filling, respectively. The model generated during anthesis in MWS was the best at predicting yields when it was applied to other conditions. Comparisons against conventional reflectance indices were made, showing lower predictive abilities. It was concluded that a Ridge Regression Model using a data set based on spectral reflectance at anthesis or grain filling represents an effective method to predict grain yield in genotypes under different water regimes.

There is a worldwide increase of heavy metal or potentially toxic element (PTE), contamination in agricultural soils caused mainly by human and industrial action, which leads to food contamination in crops such as in maize. Cadmium (Cd) is a PTE often found in soils and it is ingested through food. It is necessary to determine the bioabsorption, distribution, and accumulation levels in maize to reduce or prevent food chain contamination. Cadmium absorption and accumulation in three maize cultivars were evaluated in three agricultural environments in Chile by increasing CdCl₂ rates (0, 1, and 2 mg·kg⁻¹). Evaluation included Cd accumulation and distribution in different plant tissues, bioaccumulation factor (BAF), bioconcentration factor (BCF), translocation factor (TF), and tolerance index (TI). Cadmium whole-plant uptake was only affected by the CdCl₂ rate; the highest uptake was obtained with 2 mg·kg⁻¹ CdCl₂ (34.4 g·ha⁻¹) (p < 0.05). Cadmium distribution in the maize plant usually exhibited the highest accumulation in the straw (p < 0.05), independently of the environment, Cd rate, and evaluated cultivar. Given the results for TF (TF > 2) and BAF (BAF > 1), the Los Tilos and Chillán environments were classified as having a high capacity to contaminate the food chain for all evaluated cultivars.

The onset and rate of senescence influence key agronomical traits, including grain yield (GY). Our objective was to assess the relationships between stay-green and GY in a set of fourteen spring bread wheat (Triticum aestivum L.) genotypes with contrasting tolerance to water stress. Based on leaf chlorophyll content index (Chl) and normalized vegetation index (NDVI) measurements, the senescence dynamics at leaf and canopy levels, respectively, were quantified. Parameters describing the dynamics of senescence were examined in glasshouse and field experiments under well-watered (WW) and water-limited (WL) regimes, and they included the following stay-green traits: maximum NDVI or Chl near to anthesis (NDVI_max, Chl_max), the senescence rate (SR, rate), the area under curve (Area_NDVI, Area_Chl), and the time from anthesis to 10 (t_onset), 50 (t₅₀, X₅₀) and 90% (t₉₀) senescence. Our results revealed that specific stay-green traits were significantly different among genotypes and water regimes in both glasshouse and field experiments. GY was positively correlated with t_total (0.42), t_onset (0.62) and NDVI_dif (0.63). Under WL, NDVI_dif and NDVI_max correlated with GY (0.66–0.58), but only t₅₀ correlated with GY under WW (0.62), indicating that phenotyping of stay-green trait is a useful tool for tracking the dynamics of senescence in WW and WL environments.

Agronomic practices, climatic variables, and soil conditions are key factors in crop productivity. Although the effects of soil chemical properties and water and agronomic crop management are known, there is little information about effective soil depth and its influence on crop productivity. Since most crop fertilization systems are based on the productive potential associated with climatic conditions and chemical properties of the first 20 cm of soil depth, the objective of this study was to determine the importance of effective depth in terms of increasing fertilization rates on durum wheat (Triticum turgidum L. var. durum) productivity. Two experiments were conducted in the 2006-2007 season in the Santa Rosa Experimental Station (71°54' S, 36°31' W, 220 m a.s.l.) of the Instituto de Investigaciones Agropecuarias (INIA) located in south central Chile. We used the hard wheat cvs. Llareta-INIA and Corcolén-INIA because both respond differently to soil physicochemical properties. Each cultivar was sown in sectors with different depths: 1) 0.45 m depth, silt loam on river material and 2) 1.0 m depth, loam on deep sediments. Fertilization was: 1) control without fertilization, 2) basal fertilization (BF) based on P, K, Ca, Mg, S, B, and Zn plus 90 kg N ha-1, and 3) BF plus 210 kg N ha-1. Grain yield, plant height, and number of stems m-2 were positively affected by increasing depth of soil profile. The increasing fertilization rate affected grain yield and plant height. Grain yield for cv. Corcolén-INIA had a greater response than cv. Llareta-INIA when soil depth was increased

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.