No abstract

Agricultural fields in drylands are challenged globally by limited freshwater resources for irrigation and also by elevated soil salinity and sodicity. It is well known that pedogenic carbonate is less soluble than evaporate salts and commonly forms in natural drylands. However, few studies have evaluated how irrigation loads dissolved calcium and bicarbonate to agricultural fields, accelerating formation rates of secondary calcite and simultaneously releasing abiotic CO2 to the atmosphere. This study reports one of the first geochemical and isotopic studies of such “anthropogenic” pedogenic carbonates and CO2 from irrigated drylands of southwestern United States. A pecan orchard and an alfalfa field, where flood-irrigation using the Rio Grande river is a common practice, were compared to a nearby natural dryland site. Strontium and carbon isotope ratios show that bulk pedogenic carbonates in irrigated soils at the pecan orchard primarily formed due to flood-irrigation, and that approximately 20–50% of soil CO2 in these irrigated soils is calcite-derived abiotic CO2 instead of soil-respired or atmospheric origins. Multiple variables that control the salt buildup in this region are identified and impact the crop production and soil sustainability regionally and globally. Irrigation intensity and water chemistry (irrigation water quantity and quality) dictate salt loading, and soil texture governs water infiltration and salt leaching. In the study area, agricultural soils have accumulated up to 10 wt% of calcite after just about 100 years of cultivation. These rates will likely increase in the future due to the combined effects of climate variability (reduced rainfall and more intense evaporation), use of more brackish groundwater for irrigation, and reduced porosity in soils. The enhanced accumulation rates of pedogenic carbonate are accompanied by release of large amounts of abiotic CO2 from irrigated drylands to atmosphere. Extensive field studies and modelling approaches are needed to further quantify these effluxes at local, regional and global scales.

An average temperature increase between 2.6 and 4.8 • C, along with more frequent extreme temperatures, will challenge crop productivity by the end of the century. To investigate genotypic variation in soybean response to elevated temperature, six soybean (Glycine max) genotypes were subjected to elevated air temperature of + 4.5 • C above ambient for 28 days in open-top field chambers. Gas exchange and chlorophyll fluorescence were measured before and during heating and yield as well as seed composition were evaluated at maturity. Results show that long-term elevated air temperature increased nighttime respiration, increased the maximum velocity of carboxylation by Rubisco, impacted seed protein concentration, and reduced seed oil concentration across genotypes. The genotypes in this study varied in temperature responses for photosynthetic CO 2 assimilation, stomatal conductance, photosystem II operating efficiency, quantum efficiency of CO 2 assimilation, and seed protein concentration at maturity. These diverse responses among genotypes to elevated air temperature during seed development in the field, reveal the potential for soybean heat tolerance to be improved through breeding and underlines the importance of identifying efficient selection strategies for stress-tolerant crops.' / F m ' , quantum efficiency of PSII in the light; iWUE, intrinsic water use efficiency; J max , maximum rate of linear electron transport through PSII; NPQ, nonphotochemical quenching; R, nighttime leaf respiration; R d , mitochondrial respiration; T leaf , leaf temperature; V cmax , maximum rate of carboxylation of RuBP; VPD, vapor pressure deficit; ΦCO 2 , quantum efficiency of CO 2 ; ΦPSII, quantum efficiency of PSII.

Soil properties within the Rio Grande valley near El Paso are strongly linked to the types of fluvial deposits that serve as parent material. We used four geophysical techniques (DC resistivity, ground conductivity, capacitively coupled resistivity and magnetics) to distinguish between soil units in an alfalfa field. We combined these observations with geochemical characterization and particle size analysis in order to determine how these soils and irrigation practices influence salt buildup and water availability, and thus crop growth. Soils mapped at the site were derived from crevasse splay and flood plain deposits. Results of our investigation showed that the alfalfa grew better in soils with a 1.25 m thick unit of 40-70% sand that fined into a silty-clay (<20% sand) at greater depths. Poorer growth occurred in soils where a 0.8 m thick silty-clay (<20% sand) was underlain by a less than 2 m thick sand unit (>90% sand) we interpret as an abandoned river channel. The DC resistivity, capacitively coupled resistivity and conductivity surveys were all responsive to the major grain size changes in the upper 3 meters of soil and were able to distinguish the buried river channel. The magnetics survey was not as successful at detecting the channel, but was able to characterize near-surface grain size variability and hence distinguish between the major soil units found at the site. We believe that similar geophysical techniques could be used to rapidly evaluate soil characteristics in other regions where soils are derived from fluvial parent material.

Molecular biology aims to understand cellular responses and regulatory dynamics in complex biological systems. However, these studies remain challenging in non-model species due to poor functional annotation of regulatory proteins. To overcome this limitation, we develop a multi-layer neural network that determines protein functionality directly from the protein sequence. We annotate kinases and phosphatases in Glycine max. We use the functional annotations from our neural network, Bayesian inference principles, and high resolution phosphoproteomics to infer phosphorylation signaling cascades in soybean exposed to cold, and identify Glyma.10G173000 (TOI5) and Glyma.19G007300 (TOT3) as key temperature regulators. Importantly, the signaling cascade inference does not rely upon known kinase motifs or interaction data, enabling de novo identification of kinase-substrate interactions. Conclusively, our neural network shows generalization and scalability, as such we extend our predictions to Oryza sativa, Zea mays, Sorghum bicolor, and Triticum aestivum. Taken together, we develop a signaling inference approach for non-model species leveraging our predicted kinases and phosphatases.