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Leaf chlorophyll content (Chl l) may serve as an observational proxy for the maximum rate of 38 carboxylation (), which describes leaf photosynthetic capacity and represents the single most 39 important control on modeled leaf photosynthesis within most Terrestrial Biosphere Models (TBMs). The 40 parameterization of is associated with great uncertainty as it can vary significantly between plants 41 and in response to changes in leaf nitrogen (N) availability, plant phenology and environmental 42 conditions. Houborg et al. (2013) outlined a semi-mechanistic relationship between (43 normalized to 25°C) and Chl l based on inter-linkages between , Rubisco enzyme kinetics, N and 44 Chl l. Here, these relationships are parameterized for a wider range of important agricultural crops and 45 embedded within the leaf photosynthesis-conductance scheme of the Community Land Model (CLM), 46 bypassing the questionable use of temporally invariant and broadly defined plant functional type (PFT) 47 specific values. In this study, the new Chl l constrained version of CLM is refined with an updated 48 parameterization scheme for specific application to soybean and maize. 49 The benefit of using in-situ measured and satellite retrieved Chl l for constraining model simulations of 50 Gross Primary Productivity (GPP) is evaluated over fields in central Nebraska, U.S.A between 2001 and 51 2005. Landsat-based Chl l time-series records derived from the Regularized Canopy Reflectance model 52 (REGFLEC) are used as forcing to the CLM. Validation of simulated GPP against 15 site-years of flux 53 tower observations demonstrate the utility of Chl l as a model constraint, with the coefficient of efficiency 54 increasing from 0.91 to 0.94 and from 0.87 to 0.91 for maize and soybean, respectively. Model 55 performances particularly improve during the late reproductive and senescence stage, where the largest 56 temporal variations in Chl l (averaging 35-55 μg cm-2 for maize and 20-35 μg cm-2 for soybean) are 57 observed. While prolonged periods of vegetation stress did not occur over the studied fields, given the 58 usefulness of Chl l as an indicator of plant health, enhanced GPP predictabilities should be expected in 59 fields exposed to longer periods of moisture and nutrient stress. While the results support the use of Chl l 60 as an observational proxy for , future work needs to be directed towards improving the Chl l retrieval 61 accuracy from space observations and developing consistent and physically realistic modeling schemes 62 that can be parameterized with acceptable accuracy over spatial and temporal domains.

Excessive evaporative loss of water from the topsoil in arid-land agriculture is compensated via irrigation, which exploits massive freshwater resources. The cumulative effects of decades of unsustainable freshwater consumption in many arid regions are now threatening food-water security. While plastic mulches can reduce evaporation from the topsoil, their cost and nonbiodegradability limit their utility. In response, we report on superhydrophobic sand (SHS), a bio-inspired enhancement of common sand with a nanoscale wax coating. When SHS was applied as a 5 mm-thick mulch over the soil, evaporation dramatically reduced and crop yields increased. Multi-year field trials of SHS application with tomato (Solanum lycopersicum), barley (Hordeum vulgare), and wheat (Triticum aestivum) under normal irrigation enhanced yields by 17%-73%. Under brackish water irrigation (5500 ppm NaCl), SHS mulching produced 53%-208% higher fruit yield and grain gains for tomato and barley. Thus, SHS could benefit agriculture and city-greening in arid regions.

Given its high nutritional value and capacity to grow in harsh environments, quinoa has significant potential to address a range of food security concerns. Monitoring the development of phenotypic traits during field trials can provide insights into the varieties best suited to specific environmental conditions and management strategies. Unmanned aerial vehicles (UAVs) provide a promising means for phenotyping and offer the potential for new insights into relative plant performance. During a field trial exploring 141 quinoa accessions, a UAV-based multispectral camera was deployed to retrieve leaf area index (LAI) and SPAD-based chlorophyll across 378 control and 378 saline-irrigated plots using a random forest regression approach based on both individual spectral bands and 25 different vegetation indices (VIs) derived from the multispectral imagery. Results show that most VIs had stronger correlation with the LAI and SPAD-based chlorophyll measurements than individual bands. VIs including the red-edge band had high importance in SPAD-based chlorophyll predictions, while VIs including the near infrared band (but not the red-edge band) improved LAI prediction models. When applied to individual treatments (i.e. control or saline), the models trained using all data (i.e. both control and saline data) achieved high mapping accuracies for LAI (R2 = 0.977–0.980, RMSE = 0.119–0.167) and SPAD-based chlorophyll (R2 = 0.983–0.986, RMSE = 2.535–2.861). Overall, the study demonstrated that UAV-based remote sensing is not only useful for retrieving important phenotypic traits of quinoa, but that machine learning models trained on all available measurements can provide robust predictions for abiotic stress experiments.

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