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gaia: An R package to estimate crop yield responses to temperature and precipitation

gaia is an open-source R package designed to estimate crop yield shocks in response to annual weather variations and CO 2 concentrations at the country scale for 17 major crops. This innovative tool streamlines the workflow from raw climate data processing to projections of annual shocks to crop yields at the country level, using the response surfaces from an empirical econometric model developed and documented in Waldhoff et al. (2020), which leverages historical weather, CO 2 , and crop yield data for robust empirical fitting for 17 crops. gaia uses these response surfaces with monthly temperature and precipitation projections (e.g., from the Coupled Model Intercomparison Project Phase 6 (CMIP6) (O’Neill et al., 2016) climate data bias-adjusted and statistically downscaled by the ISIMIP3BASD approach (Lange, 2019) in the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) (Warszawski et al., 2014)) to project yield shocks that can be applied to agricultural productivity changes at the country level for use in multisectoral economic models. The historical and future projections use gridded, country-and-crop specific monthly growing season precipitation and temperature data, aggregated to the national level, and weighted by cropland area derived from the global Monthly Irrigated and Rainfed Crop Areas around the year 2000 (MIRCA2000) dataset (Portmann et al., 2010). These annual, country, and crop-specific yield shocks can be aggregated to different definitions of regions, crop commodities, and time periods, as needed by specific multisectoral economic models. gaia serves as a lightweight, powerful tool that can aid exploration of crop yield responses under a broad range of future climate projections, enhancing human-Earth system analysis capabilities.

60 APPLIED LIFE SCIENCES↗

Drought-tolerant Biofuel Crops could be a Critical Hedge for Biorefineries

Numerous researchers and policymakers have raised concerns over the adverse effects that growing bioenergy crops on prime agricultural land has on food prices, carbon balances, water demand and environmental health. Biofuel policies such as the California Low Carbon Fuel Standard (LCFS) and the second US Renewable Fuel Standard (RFS2) already regulate biofuels for carbon emissions that occur due to indirect land use change. Biofuel producers can address these concerns and sidestep steep penalties for indirect land use change if they grow biofuel crops on land that has little or no economic value. The focus of most biofuel research over the last decade has been the development of biofuels from cellulosic feedstocks. It has been argued that deriving biofuels from non-food biomass, like switchgrass, and growing the crop on low value land would minimize competition with food production. Moreover, using non-irrigated rain-fed feedstocks can substantially reduce the lifecycle footprint of cellulosic biofuels. This has motivated scientists to study if biofuel crops can be genetically engineered to thrive on low-value, marginal lands. One example is the development of switchgrass genomics designed to enhance productivity. For a biofuel business, investing in the development of a product in expectation of a specific policy regime (i.e. the regulation of land use in fuel policy) is fraught with risk. While we believe that all fuel policies worldwide should reward biofuel crops grown on land that has little or no potential for growing food crops, there is good reason to believe that such globally coordinated policy on the subject of land use is unlikely. Hence, in this article, we assess the economic benefits of drought-tolerant switchgrass that may accrue to biofuel businesses independent of any incentives for the use of marginal land. The frequency of extreme weather and drought has increased in the U.S. over the last decade. While some researchers estimate that droughts are decreasing in severity and duration, most climate scientists report that advancing climate change is expected to worsen droughts throughout the U.S., especially in the Midwest, which is the nation’s largest producer of food crops. Scientists are increasingly convinced that climate change is leading to warmer extremes including severity of precipitation extremes. In the more likely scenario of worsening drought, we find that drought-tolerance of the cellulosic crop is critical for the biofuel industry to remain competitive, irrespective of whether the crop is grown on marginal or prime agricultural land. In other words, we find that over the lifetime of a typical biorefinery, there could be one or more years when a crop is likely to be severely diminished due to drought conditions, thereby driving cellulosic ethanol prices substantially higher than would otherwise be the case.

09 BIOMASS FUELS↗

Soil oxygen dynamics: a key mediator of tile drainage impacts on coupled hydrological, biogeochemical, and crop systems

Tile drainage removes excess water and is an essential, widely adopted management practice to enhance crop productivity in the US Midwest and throughout the world. Tile drainage has been shown to significantly change hydrological and biogeochemical cycles by lowering the water table and reducing the residence time of soil water, although examining the complex interactions and feedbacks in an integrated hydrology–biogeochemistry–crop system remains elusive. Oxygen dynamics are critical to unraveling these interactions and have been ignored or oversimplified in existing models. Understanding these impacts is essential, particularly so because tile drainage has been highlighted as an adaptation under projected wetter springs and drier summers in the changing climate in the US Midwest. We used the ecosys model that uniquely incorporates first-principle soil oxygen dynamics and crop oxygen uptake mechanisms to quantify the impacts of tile drainage on hydrological and biogeochemical cycles and crop growth in corn–soybean rotation fields. The model was validated with data from a multi-treatment, multi-year experiment in Washington, IA. The relative root mean square error (rRMSE) for the corn and soybean yield in validation is 5.66 % and 12.57 %, respectively. The Pearson coefficient (r) of the monthly tile flow during the growing season is 0.78. Plant oxygen stress turns out as an emergent property of the equilibrium between the soil oxygen supply and biological demand. The impact of tile drainage on the system is achieved through a series of coupled feedback mechanisms. The model results show that tile drainage reduces the soil water content and enhances soil oxygenation. It additionally increases the subsurface discharge and elevates inorganic nitrogen leaching, with seasonal variations influenced by climate and crop phenology. The improved aerobic condition alleviates crop oxygen stress during wet springs, thereby promoting crop root growth during the early growth stage. The development of greater root density, in turn, mitigates water stress during dry summers, leading to an overall increase in the crop yield by ∼6 %. These functions indicate the potential of tile drainage in bolstering crop resilience to climate change and the use of this modeling tool for large-scale assessments of tile drainage. The model reveals the underlying causal mechanisms that drive the agroecosystem response to drainage on the coupled hydrology, biogeochemistry, and crop system dynamics.

60 APPLIED LIFE SCIENCES↗

The Added Value of SMAP Soil Moisture in Crop Yield Forecasting Over Argentina

Argentina is one of the major producers and exporter of soybeans, corn, and wheat to the world market; therefore, the accurate and timely forecasting of those crops yield is crucial to national crop management and global food security. Previous studies have mainly focused on developing forecasting models for a specific crop type and location using a single source of data (e.g., vegetation indices), thus providing little insight into the forecasting models' performance on different crop types and regions. Besides, these models are based on traditional statistical regression algorithms, while more advanced machine learning approaches have not been explored. This study investigated the estimation of crop yields of three major crops (corn, soybean, and winter wheat) using Multiple Linear Regression (MLR) and Support Vector Machine (SVM), over major growing provinces in Argentina. Our models were trained and evaluated on data from 2015 to 2020, where three remote sensing products (Normalized difference vegetation index (NDVI), SMAP soil moisture, and MODIS evapotranspiration) were used as predictors. Our results indicated that accurate crop yield forecasts using the developed regression models could be made one to two months before harvest. The MLR and SVM model performance varied among different crop types, where soybean and corn exhibited better predictability compare to the wheat. In most cases, the SVM outperformed the multiple linear regression model due to its ability to capture the nonlinear and complex features of the crop-production process. The forecasted model that combines data from multiple sources outperformed single-source satellite data. The highest accuracy was obtained when the three data sources were all considered in the model development. Results also indicated that the inclusion of SMAP soil moisture improved crop yield forecasting in most provinces, and the most significant improvements occurred in the drier region.

Nazmus Shams Sazib↗

Novel Microgreen Crop Testing for Space

Long-duration missions beyond low-Earth orbit will encounter challenges in maintaining adequate nutrition and crew acceptability in the food system. In situ production of fresh produce can supplement nutrient deficiencies in the prepackaged diet. Currently, there are a relatively small number of crops that can be reliably grown in space for space crop production efforts. An intriguing area of new investigation involves novel types of microgreens that have the potential to be sources of calories, fat, carbohydrates, and protein. These sources of nutrition are not obtainable in significant quantities with current pick and eat crops. Many microgreen cultivars are also sources of nutrients of interest, such as Vitamins B1, C, and K, and elements such as potassium. Microgreens should be selected to address specific nutritional deficits, as identified by NASA’s Human Research Program, with an emphasis on having a diversity of crops to meet nutritional requirements and crew acceptability. To achieve this, the concept of Crop Readiness Level (CRL) has been developed to gauge readiness of crops for spaceflight applications. CRL includes assessing environmental compatibility, food safety considerations, relevant nutritional analysis, and sensory analysis. Recent testing at Kennedy Space Center has focused on advancing the CRL of a variety of novel microgreens. These varieties were grown under 150 µmol m -2 s -1 PPFD from LED lights, 3000 ppm CO 2 , and 23°C to simulate an ISS environment. Crops were harvested and yield was assessed. Then, baseline microbiological and nutritional analysis (Vitamins B1, C, K; mineral analysis; proximate analysis) and sensory evaluation were performed. These baseline data are essential to selecting candidate crops for future missions and assessing crop production hardware and changes in environmental conditions on future crop performance and nutritional quality.

nutrition↗

Spatial and Temporal Uncertainty of Crop Yield Aggregations

The aggregation of simulated gridded crop yields to national or regional scale requires information on temporal and spatial patterns of crop-specific harvested areas. This analysis estimates the uncertainty of simulated gridded yield time series related to the aggregation with four different harvested area data sets. We compare aggregated yield time series from the Global Gridded Crop Model Inter-comparison project for four crop types from 14 models at global, national, and regional scale to determine aggregation-driven differences in mean yields and temporal patterns as measures of uncertainty. The quantity and spatial patterns of harvested areas differ for individual crops among the four datasets applied for the aggregation. Also simulated spatial yield patterns differ among the 14 models. These differences in harvested areas and simulated yield patterns lead to differences in aggregated productivity estimates, both in mean yield and in the temporal dynamics. Among the four investigated crops, wheat yield (17% relative difference) is most affected by the uncertainty introduced by the aggregation at the global scale. The correlation of temporal patterns of global aggregated yield time series can be as low as for soybean (r = 0.28).For the majority of countries, mean relative differences of nationally aggregated yields account for10% or less. The spatial and temporal difference can be substantial higher for individual countries. Of the top-10 crop producers, aggregated national multi-annual mean relative difference of yields can be up to 67% (maize, South Africa), 43% (wheat, Pakistan), 51% (rice, Japan), and 427% (soybean, Bolivia).Correlations of differently aggregated yield time series can be as low as r = 0.56 (maize, India), r = 0.05∗Corresponding (wheat, Russia), r = 0.13 (rice, Vietnam), and r = −0.01 (soybean, Uruguay). The aggregation to sub-national scale in comparison to country scale shows that spatial uncertainties can cancel out in countries with large harvested areas per crop type. We conclude that the aggregation uncertainty can be substantial for crop productivity and production estimations in the context of food security, impact assessment, and model evaluation exercises.

Aggregation uncertainty↗

Spatial Sampling of Weather Data for Regional Crop Yield Simulations

Field-scale crop models are increasingly applied at spatio-temporal scales that range from regions to the globe and from decades up to 100 years. Sufficiently detailed data to capture the prevailing spatio-temporal heterogeneity in weather, soil, and management conditions as needed by crop models are rarely available. Effective sampling may overcome the problem of missing data but has rarely been investigated. In this study the effect of sampling weather data has been evaluated for simulating yields of winter wheat in a region in Germany over a 30-year period (1982-2011) using 12 process-based crop models. A stratified sampling was applied to compare the effect of different sizes of spatially sampled weather data (10, 30, 50, 100, 500, 1000 and full coverage of 34,078 sampling points) on simulated wheat yields. Stratified sampling was further compared with random sampling. Possible interactions between sample size and crop model were evaluated. The results showed differences in simulated yields among crop models but all models reproduced well the pattern of the stratification. Importantly, the regional mean of simulated yields based on full coverage could already be reproduced by a small sample of 10 points. This was also true for reproducing the temporal variability in simulated yields but more sampling points (about 100) were required to accurately reproduce spatial yield variability. The number of sampling points can be smaller when a stratified sampling is applied as compared to a random sampling. However, differences between crop models were observed including some interaction between the effect of sampling on simulated yields and the model used. We concluded that stratified sampling can considerably reduce the number of required simulations. But, differences between crop models must be considered as the choice for a specific model can have larger effects on simulated yields than the sampling strategy. Assessing the impact of sampling soil and crop management data for regional simulations of crop yields is still needed.

upscaling↗

Phosphorus and cover crop management practices affect phosphorus speciation in soils and eroded sediments

Abstract Agricultural runoff often contains P in dissolved and sediment‐bound forms, decreasing surface water quality. No‐till and cover cropping conservation practices have been recommended for reducing erosion and nutrient loss from cropping systems. The overall aims of this study were to characterize and evaluate the effects of fertilizer (placement and source) and cover crop management on P speciation in surface runoff sediments and source soil. In 2014, a field‐scale experiment was established in a no‐till, corn (Zea maysL.)–soybean (Glycine maxL.) cropping system with two cover crop treatments (with and without a winter crop; winter wheat [Triticum aestivumL.], rapeseed [Brassica napusL.], hairy vetch [Vicia villosaRoth], winter triticale [×Triticosecale Wittm.], and cereal rye [Secale cerealeL.]) and three P fertilizer management treatments (no P, fall broadcast diammonium phosphate, and spring subsurface injected ammonium polyphosphate). Phosphorus fractionation in the source soil collected in the fall of 2019 and sediment samples collected throughout 2020 were analyzed using a modified sequential P extraction method to evaluate the cumulative effects of imposing the treatment factors over 5 years. The direct P speciation was done using X‐ray absorption near edge structure spectroscopy. The indirect P speciation (fractionation) results showed that the management practices affected the exchangeable, organic matter‐associated, and Fe‐bound P fractions in sediments and the exchangeable and residual fractions in source soil. Direct P speciation results showed a depletion of Fe‐associated P in soil and sediment from cover crop treatment, suggesting that Fe‐associated P species were affected by cover crops. Changes in soil and runoff sediment P speciation would change the proportions and forms of soluble and particulate P in runoff sediments and may influence P bioavailability in aquatic ecosystems. Developing P fertilizer and cropping system management options with an understanding of soil P transformations helps maintain environmental sustainability.

Environmental Sciences & Ecology↗

Community composition and abundance of wild bees at row crop-grassland interfaces in west central Nebraska

Abstract Perennial mixed forb and grassland habitats are crucial to conservation of pollinators and connectivity of habitats in intensely farmed landscapes. This study aims to understand the effects of land use on the pollinator community by describing bee abundance, species richness and community composition in perennial conservation grasslands and adjacent annual row crops located in west central Nebraska. In 2022 and 2023, we collected and identified bees via sticky traps at 4 locations (center and edge of adjacent grasslands and crop fields) at 6 replicated sites. We collected 1,768 specimens from sticky traps, resulting in 70 species within 28 genera. Halictidae accounted for 84% of the specimens collected. Bee abundance was influenced by the simple effects of land use (grassland vs. crops), edge adjacency, and the month and year of collection. Differences in bee abundance within a collection date were found mostly in early 2022 (May and June) and late 2023 (September), when the crop center location was generally the lowest, with some evidence for spillover of bees from the grassland into the crop edge during the early summer months. Bee species richness was affected only by month and was not significantly different by land use and edge adjacency. Bee community composition overlapped across the 4 locations, although there were significant dissimilarities between crop fields and grasslands. Surveys of the plant community revealed very low abundance of blooming stems and plant taxonomic richness at crop locations for all sampling periods, while grassland locations were comparatively high and varied over time. Plant communities showed no overlap between crop field and grassland locations. Overall, we found that conservation grasslands, while not seeded specifically with pollinator-attractive forbs, provide diverse resources to support wild bee communities in west central Nebraska; crop edges may also provide non-plant resources such as nesting sites and irrigation water. Going forward, better understanding pollinator species composition and resource utilization relative to land use characteristics and drought conditions will allow for better tailoring of conservation efforts and management strategies in Nebraska and across the larger region.

Entomology↗

Contrasting effects of bioenergy crops on biodiversity

Agriculture is driving biodiversity loss, and future bioenergy cropping systems have the potential to ameliorate or exacerbate these effects. Using a long-term experimental array of 10 bioenergy cropping systems, we quantified diversity of plants, invertebrates, vertebrates, and microbes in each crop. For many taxonomic groups, alternative annual cropping systems provided no biodiversity benefits when compared to corn (the business-as-usual bioenergy crop in the United States), and simple perennial grass–based systems provided only modest gains. In contrast, for most animal groups, richness in plant-diverse perennial systems was much higher than in annual crops or simple perennial systems. Microbial richness patterns were more eclectic, although some groups responded positively to plant diversity. Future agricultural landscapes incorporating plant-diverse perennial bioenergy cropping systems could be of high conservation value. However, increased use of annual crops will continue to have negative effects, and simple perennial grass systems may provide little improvement over annual crops.

09 BIOMASS FUELS↗

Connecting Nitrogen Transformations Mediated by the Rhizosphere Microbiome to Perennial Cropping System Productivity in Marginal Lands

The demand for energy from biofuel production is increasing, prompting concerns about the environmental impact and long-term sustainability of bioenergy cropping systems. These cropping systems will make up much of our future landscapes, and threaten to take the place of food cropping systems. Many life cycle analyses of bioenergy sustainability focus on carbon accrual and budgets, since they want to maximize carbon accrual while producing alternative fuel. Less attention has been given to nitrogen (N) dynamics in these systems. N is the most commonly limiting nutrient for plants, but applying nitrogen fertilizer- as we do for most cropping systems – is harmful to the environment, energetically costly, and produces greenhouse gases. In other words, adding nitrogen by fertilizer bioenergy systems could add to the very problems (climate change) it is trying to address. This is especially true for the areas that are proposed for bioenergy systems: marginal lands. These more degraded lands do not complete with food crops, but do have limited nitrogen. If we are to use these marginal lands for bioenergy, we need to understand the mechanisms regulating nutrient acquisition, and identify ways that bioenergy crops can get nitrogen in sustainable ways. Nutrient acquisition in the soil is performed by microbes in the root zone, or rhizosphere. Microbes can either mineralize nitrogen in the soil (from organic forms) or fix nitrogen from the air, in a process called nitrogen fixation. The goal of our project was thus to understand how the rhizosphere microbiome provides nutrients to bioenergy crops on marginal lands. We focus especially on the process of nitrogen fixation, since it has potential to get “fertilizer for free” that has much less environmental harm. We investigated this goal using sites from the DOE Great Lakes Bioenergy Research Center (GLBRC) in the upper Midwest, and associated lab and ‘omics methods. We group our findings into three major areas. First, we showed that nitrogen fixation, the conversion of N2 gas from the air to ammonium that is usable by plants, is performed in bioenergy soils, and benefits switchgrass crops. While more well-studied in leguminous plants, free-living nitrogen fixation can occur in some systems, and represents a potential opportunity to gain ‘free’ sustainable nitrogen source. We identified the nitrogen fixing bacteria that were most active in providing switchgrass with N, and showed that the drivers of nitrogen fixation occurred at a microscale; it is not well-predicted by bulk variables like soil moisture or plant phenology. Second, we showed that nitrogen fixation is not suppressed by long-term fertilizer. We expected that plentiful nitrogen would reduce the symbiotic relationship between nitrogen fixers and plants, and ‘downregulate’ fixation. We did not find evidence for this, either after long-term fertilizer in the field, or short-term fertilizer in the greenhouse. Finally, we identified the root exudates, carbon compounds that are emitted from the root, that best stimulate nitrogen fixation. We found that carbohydrates were better at stimulating fixation than organic acids. We expected these exudates to be emitted from the plant in periods of high N demand, but we found they are emitted when N is plentiful. This suggests that the stimulation of N fixation by plants is a passive process. Overall, we show that nitrogen fixation has potential to support bioenergy cropping system, and future management could develop ways to maximize it. However, this may not be best achieved via the plant – we found very little evidence of a ‘transactional’ system by which plants are controlling when and where nitrogen fixation is stimulated. It will be better to understand how management practices like planting and fertilizer application affect the microscale soil dynamics, which will determine nitrogen fixation rates.

59 BASIC BIOLOGICAL SCIENCES↗

Site and Bioenergy Cropping System Similarly Affect Distinct Live and Total Soil Microbial Communities

Bioenergy crops are a promising energy alternative to fossil fuels. During bioenergy feedstock production, crop inputs shape the composition of soil microbial communities, which in turn influences nutrient cycling and plant productivity. In addition to cropping inputs, site characteristics (e.g., soil texture, climate) influence bacterial and fungal communities. We explored the response of soil microorganisms to bioenergy cropping system (switchgrass vs. maize) and site (sandy loam vs. silty loam) within two long-term experimental research stations. The live and total microbial community membership was investigated using 16S and ITS amplicon sequencing of soil RNA and DNA. For both nucleic acid types, we expected fungi and prokaryotes to be differentially impacted by crop and site due their dissimilar life strategies. We also expected live communities to be more strongly affected by site and crop than the total communities due to a sensitivity to recent stimuli. Instead, we found that prokaryotic and fungal community composition was primarily driven by site with a secondary crop effect, highlighting the importance of soil texture and fertility in shaping both communities. Specific highly abundant prokaryotic and fungal taxa within live communities were indicative of site and cropping systems, providing insight into treatment-specific, agriculturally relevant microbial taxa that were obscured within total community profiles. Within live prokaryote communities, predatory Myxobacteria spp. were largely indicative of silty and switchgrass communities. Within live fungal communities, Glomeromycota spp. were solely indicative of switchgrass soils, while a few very abundant Mortierellomycota spp. were indicative of silty soils. Site and cropping system had distinct effects on the live and total communities reflecting selection forces of plant inputs and environmental conditions over time. Comparisons between RNA and DNA communities uncovered live members obscured within the total community as well as members of the relic DNA pool. The associations between live communities and relic DNA are a product of the intimate relationship between the ephemeral responses of the live community and the accumulation of DNA within necromass that contributes to soil organic matter, and in turn shapes soil microbial dynamics.

59 BASIC BIOLOGICAL SCIENCES↗

Priorities for worldwide remote sensing of agricultural crops

The world's crops are ranked according to total harvested area, and comparisons are made among major world regions of differences in crops produced. The eight leading world crops are wheat, rice, corn, barley, millet, soybeans, sorghum, and cotton. Regionally, millet and sorghum are most important in Africa, wheat is the most extensively grown crop in north-central America, Europe, USSR, and Oceania; corn is the dominant crop in South America; and rice is the most extensively grown crop in Asia. Agriculture in the USA is considered in more detail to show the national economic impact of variations in value per hectare among crops. On the world scene, the cereals are the most important crops, but locally, such crops as tobacco can play a dominant role.

Bowker, D. E.↗

Performance of the CELSS Antarctic Analog Project (CAAP) crop production system

Regenerative life support systems potentially offer a level of self-sufficiency and a decrease in logistics and associated costs in support of space exploration and habitation missions. Current state-of-the-art in plant-based, regenerative life support requires resources in excess of allocation proposed for candidate mission scenarios. Feasibility thresholds have been identified for candidate exploration missions. The goal of this paper is to review recent advances in performance achieved in the CELSS Antarctic Analog Project (CAAP) in light of the likely resource constraints. A prototype CAAP crop production chamber has been constructed and operated at the Ames Research Center. The chamber includes a number of unique hardware and software components focused on attempts to increase production efficiency, increase energy efficiency, and control the flow of energy and mass through the system. Both single crop, batch production and continuous cultivation of mixed crops production studies have been completed. The crop productivity as well as engineering performance of the chamber are described. For each scenario, energy required and partitioned for lighting, cooling, pumping, fans, etc. is quantified. Crop production and the resulting lighting efficiency and energy conversion efficiencies are presented. In the mixed-crop scenario, with 27 different crops under cultivation, 17 m2 of crop area provided a mean of 515 g edible biomass per day (85% of the approximate 620 g required for one person). Enhanced engineering and crop production performance achieved with the CAAP chamber, compared with current state-of-the-art, places plant-based life support systems at the threshold of feasibility. c2002 Published by Elsevier Science Ltd on behalf of COSPAR.

Lettuce/growth & development↗

Salience Assignment for Multiple-Instance Data and Its Application to Crop Yield Prediction

An algorithm was developed to generate crop yield predictions from orbital remote sensing observations, by analyzing thousands of pixels per county and the associated historical crop yield data for those counties. The algorithm determines which pixels contain which crop. Since each known yield value is associated with thousands of individual pixels, this is a multiple instance learning problem. Because individual crop growth is related to the resulting yield, this relationship has been leveraged to identify pixels that are individually related to corn, wheat, cotton, and soybean yield. Those that have the strongest relationship to a given crop s yield values are most likely to contain fields with that crop. Remote sensing time series data (a new observation every 8 days) was examined for each pixel, which contains information for that pixel s growth curve, peak greenness, and other relevant features. An alternating-projection (AP) technique was used to first estimate the "salience" of each pixel, with respect to the given target (crop yield), and then those estimates were used to build a regression model that relates input data (remote sensing observations) to the target. This is achieved by constructing an exemplar for each crop in each county that is a weighted average of all the pixels within the county; the pixels are weighted according to the salience values. The new regression model estimate then informs the next estimate of the salience values. By iterating between these two steps, the algorithm converges to a stable estimate of both the salience of each pixel and the regression model. The salience values indicate which pixels are most relevant to each crop under consideration.

Wagstaff, Kiri L.↗

Selection of Hyperspectral Narrowbands (HNBs) and Composition of Hyperspectral Twoband Vegetation Indices (HVIs) for Biophysical Characterization and Discrimination of Crop Types Using Field Reflectance and Hyperion-EO-1 Data

The overarching goal of this study was to establish optimal hyperspectral vegetation indices (HVIs) and hyperspectral narrowbands (HNBs) that best characterize, classify, model, and map the world's main agricultural crops. The primary objectives were: (1) crop biophysical modeling through HNBs and HVIs, (2) accuracy assessment of crop type discrimination using Wilks' Lambda through a discriminant model, and (3) meta-analysis to select optimal HNBs and HVIs for applications related to agriculture. The study was conducted using two Earth Observing One (EO-1) Hyperion scenes and other surface hyperspectral data for the eight leading worldwide crops (wheat, corn, rice, barley, soybeans, pulses, cotton, and alfalfa) that occupy approx. 70% of all cropland areas globally. This study integrated data collected from multiple study areas in various agroecosystems of Africa, the Middle East, Central Asia, and India. Data were collected for the eight crop types in six distinct growth stages. These included (a) field spectroradiometer measurements (350-2500 nm) sampled at 1-nm discrete bandwidths, and (b) field biophysical variables (e.g., biomass, leaf area index) acquired to correspond with spectroradiometer measurements. The eight crops were described and classified using approx. 20 HNBs. The accuracy of classifying these 8 crops using HNBs was around 95%, which was approx. 25% better than the multi-spectral results possible from Landsat-7's Enhanced Thematic Mapper+ or EO-1's Advanced Land Imager. Further, based on this research and meta-analysis involving over 100 papers, the study established 33 optimal HNBs and an equal number of specific two-band normalized difference HVIs to best model and study specific biophysical and biochemical quantities of major agricultural crops of the world. Redundant bands identified in this study will help overcome the Hughes Phenomenon (or "the curse of high dimensionality") in hyperspectral data for a particular application (e.g., biophysical characterization of crops). The findings of this study will make a significant contribution to future hyperspectral missions such as NASA's HyspIRI. Index Terms-Hyperion, field reflectance, imaging spectroscopy, HyspIRI, biophysical parameters, hyperspectral vegetation indices, hyperspectral narrowbands, broadbands.

Vegetation↗

Verification Testing and Veg-05 Tomato Crop Production on the International Space Station

Production of fresh, nutritious, and tasty produce for astronauts during spaceflight may provide health-promoting, bioavailable nutrients and enhance the dietary experience as we move into longer-duration missions. Growing and caring for plants may also reduce the psychological stresses associated with spaceflight and enhance connections to Earth. Requirements to consistently grow a diversity of crops under spaceflight environmental conditions remain poorly defined. The VEG-05 experiment is part of a series of experiments with pick-and-eat salad crops to better define best practices for crop production and handling in space. VEG-05 and predecessor experiments VEG-04A and VEG-04B, use the Veggie vegetable production facilities on the International Space Station to grow salad crops under different spectral compositions. In VEG-04A and B, mizuna mustard was cultivated with two different red: blue light treatments, and in VEG-05 we are cultivating ‘Red Robin’ dwarf cherry tomatoes under the same light spectra. Light can impact the growth habit, yield, nutritional composition, microbial levels, and even flavor attributes within crops, and our team will assess these characteristics for this crop during VEG-05. Prior to launch and installation on ISS in late 2022, both a science verification test (SVT), and an experiment verification test (EVT) were conducted at Kennedy Space Center in ISS Environment Simulator Chambers. Science verification testing, and a previous fertilizer test, grew plants in both plant pillows and PONDS (Passive Orbital Nutrient Delivery System) units and tested two different fertilizer treatments in both sets of hardware, with each test under only one of the light conditions. Because of challenges validating the PONDS hardware on ISS, and with good crop production in plant pillows, the EVT moved forward using only plant pillows with the highest fertilizer composition tested during SVT, and two Veggie units were utilized. One Veggie had light settings consisting of equal levels of red: blue light (150 µmol/m2/s for each color) plus green light (30 µmol/m2/s) while the second Veggie had a 90:10 ratio of red: blue light (270 µmol/m2/s red and 30 µmol/m2/s blue) plus green, so each unit provided 330 µmol/m2/s of photosynthetically active radiation to the tomato crops on average. Our original plan, based on prior ground testing, was to grow the crop for 104 days and harvest at 80, 90 and 104 days after initiation. For SVT, under the equal red: blue light treatment, fruit ripening in plant pillows was delayed and fruit were not ripe by day 80, so actual harvest days were days 90, 97, and 104. For EVT we saw fruit ripening earlier, especially in the high red treatment, and so we harvested at days 83, 90, and 99 days after initiation. In SVT we had mostly daily watering, and this led to excess water in plant pillows, which leaked out. This excess water also caused fungus to grow on one leaf and a couple of plant stems. To reduce this excess moisture, we throttled back the watering for EVT, and used the root mat reservoir more frequently. This led to watering only every other day, reducing crew time needed for plant care, however, two wilting events occurred during this EVT, at days 51 and 75. Plants recovered from these wilting events, but these events may have influenced the rate of fruit ripening and flower formation. Regardless, more than 10 fruit were produced from each plant on average, with the high red treatment producing slightly heavier fruit. Microbial testing from fruit during SVT indicated that fruit were safe for consumption with microbial levels below detection limits. VEG-05 flight and ground operations are expected to run between December 2022 and March 2023. This research was co-funded by the Human Research Program and Space Biology (MTL#1075) in the ILSRA 2015 NRA call.

Gioia D. Massa↗

Space Crop Production Gaps and Challenges

As astronauts venture farther from Earth, and stay for longer periods, the space food system will increase in importance. Crop production can supplement a pre-packaged space diet to provide nutrition and dietary variety for space crews. In future missions, bioregenerative approaches may be used to generate a larger percentage of the diet, as well as help to reduce life support system burdens and resupply from Earth. Plants may also provide behavioral health benefits to crew members living in the isolated, confined environment of a space habitat. A number of unique challenges exist for growth of plants in microgravity and on other reduced gravity surfaces like the moon and Mars. Testing plant growth inside the Veggie and Advanced Plant Habitat (APH) chambers on the International Space Station is allowing us to understand the impacts of gravity and spaceflight on crop growth, nutritional content, acceptability, and the importance of plants to astronauts living and working away from Earth. We are also gaining a better understanding of food safety concerns and the behavior of space plant microbiomes and plant pathogens, but major gaps in knowledge remain. As we move from research towards operational space crop production to enable exploration, there are numerous gaps in technology, knowledge, and practice related to space crop growth that must be addressed. Research and development in key focus areas such as effective water and nutrient delivery at variable gravity levels, autonomous plant health monitoring, growth system cleaning and disinfection, and selection of ideal space crops are needed to fill these gaps. Breeding or engineering custom space crops may impact areas including plant growth and development, plant physiology, produce nutrition, organoleptic acceptability, and post-harvest characteristics, and these may further enable space crop production scenarios. Space crop challenges are multifaceted and require diverse interdisciplinary teams working together to develop effective solutions. Solving these requires an array of skill sets from across the biological and physical sciences, engineering, and human social sciences. Solutions to help ensure food security off-Earth may also translate to more sustainable terrestrial crop production approaches, and regular dialog between industry, academia, and government organizations working in related fields benefit all. Additional help can come from engagement with student researchers at various levels through courses, participatory science projects, and open science activities which can provide useful data. Global coordination and integration between space agencies and partners will be essential.

Gioia Donna Massa↗