Increased periods of water shortage and higher temperatures, together with a reduction in nutrient availability, have been proposed as major factors that negatively impact plant development. Photosynthetic CO2 assimilation is the basis of crop production for animal and human food, and for this reason, it has been selected as a primary target for crop phenotyping/breeding studies. Within this context, knowledge of the mechanisms involved in the response and acclimation of photosynthetic CO2 assimilation to multiple changing environmental conditions (including nutrients, water availability, and rising temperature) is a matter of great concern for the understanding of plant behavior under stress conditions, and for the development of new strategies and tools for enhancing plant growth in the future. The current review aims to analyze, from a multi-perspective approach (ranging across breeding, gas exchange, genomics, etc.) the impact of changing environmental conditions on the performance of the photosynthetic apparatus and, consequently, plant growth.

The application of ammonium N source, especially when split into three amendments, has an analogous effect on grain protein content and composition to applications at a higher N rate, leading to higher N use efficiency.

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The availability and management of N are major determinants of crop productivity, but N excessive use has an associated agro-ecosystems environmental impact. The aim of this work was to investigate the influence of N fertilization on yield and grain quality of 6 durum wheat genotypes, selected from 20 genotypes as high- and low-yielding genotypes. Two N levels were applied from anthesis to maturity: high (½ Hoagland nutrient solution) and low (modified ½ Hoagland with one-third of N). Together with the agronomic characterization, grain quality analyses were assessed to characterize carbohydrates concentration, mineral composition, glutenin and gliadin concentrations, polyphenol profile, and anti-radical activity. Nitrogen supply improved wheat grain yield with no effect on thousand-grain weight. Grain soluble sugars and gluten fractions were increased, but starch concentration was reduced, under high N. Mineral composition and polyphenol concentrations were also improved by N application. High-yielding genotypes had higher grain carbohydrates concentrations, while higher concentrations in grain minerals, gluten fractions, and polyphenols were recorded in the low-yielding ones. Decreasing the amount of N to one-third ensured a better N use efficiency but reduced durum wheat agronomic and quality traits.

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Bread wheat (Triticum aestivum L.) quality is determined by genotype and environment. The aim of this work was to find gluten proteins related to wheat quality independent of crop environment and under nonlimiting N conditions for wheat yield. Field experiments assessing the effect of N rate in soft winter wheat quality parameters were performed in three locations in Navarra (in northern Spain) during 5 yr with a randomized complete block design and four replications. The minimum N rate that produced maximum grain yield (Nop) was determined for each year. Grain samples from N treatments equal to or above Nop were milled. Gliadins and glutenins were extracted from white flour. They were separated into α plus β‐, γ‐, ω‐gliadins, high molecular weight glutenin subunits (HMW‐GS), and low molecular weight glutenin subunits (LMW‐GS) and they were quantified by reverse phase high performance liquid chromatography (RP‐HPLC). Although an increase in N fertilizer rate led to increases in gliadin and glutenin contents as well as dough strength and extensibility, environmental variability differentially affected the synthesis or polymerization of each gliadin and glutenin type. In terms of protein fractions, both HMW‐GS and LMW‐GS were determined to be the protein fractions best correlated to dough strength (R2 = 0.78) whereas γ‐gliadins were the best correlated to dough extensibility (R2 = 0.72) independent of N nutrition, environmental conditions, and water availability.

Reliable methods for estimating wheat grain yield before harvest could help improve farm management and, if applied on a regional level, also help identify spatial factors that influence yield. Regional grain yield can be estimated using conventional methods, but the typical process is complex and labor-intensive. Here we describe the development of a streamlined approach using publicly accessible agricultural data, field-level yield, and remote sensing data from Sentinel-2 satellite to estimate regional wheat grain yield. We validated our method on wheat croplands in Navarre in northern Spain, which features heterogeneous topography and rainfall. First, this study developed stepwise multilinear equations to estimate grain yield based on various vegetation indices, which were measured at various phenological stages in order to determine the optimal timings. Second, the most suitable model was used to estimate grain yield in wheat parcels mapped from Sentinel-2 satellite images. We used a supervised pixel-based random forest classification and the estimates were compared to government-published post-harvest yield statistics. When tested, the model achieved an R2 of 0.83 in predicting grain yield at field level. The wheat parcels were mapped with an accuracy close to 86% for both overall accuracy and compared to official statistics. Third, the validated model was used to explore potential relationships of the mapped per-parcel grain yield estimation with topographic features and rainfall by using geographically weighted regressions. Topographic features and rainfall together accounted for an average for 11 to 20% of the observed spatial variation in grain yield in Navarre. These results highlight the ability of our method for estimating wheat grain yield before harvest and determining spatial factors that influence yield at the regional scale.