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The need for sustainable agricultural practices to meet the food, feed, and fuel demands of a growing global population while reducing detrimental environmental impacts has driven research in multi-faceted approaches to agricultural sustainability. Perennial cropping systems and microbial biofertilizer supplements are two emerging strategies to increase agricultural sustainability that are studied in tandem for the first time in this study. During the establishment phase of a perennial switchgrass stand in SW Montana, USA, we supplemented synthetic fertilization with a nitrogen-fixing cyanobacterial biofertilizer (CBF) and were able to maintain aboveground crop productivity in comparison to a synthetic only (urea) fertilizer treatment. Soil chemical analysis conducted at the end of the growing season revealed that late-season nitrogen availability in CBF-supplemented field plots increased relative to urea-only plots. High-throughput sequencing of bacterial/archaeal and fungal communities suggested fine-scale responses of the microbial community and sensitivity to fertilization among arbuscular mycorrhizal fungi, Planctomycetes, Proteobacteria, and Actinobacteria. Given their critical role in plant productivity and soil nutrient cycling, soil microbiome monitoring is vital to understand the impacts of implementation of alternative agricultural practices on soil health.
Agriculture is an important sector in Bhutan, accounting for 19.63% of Bhutan’s GDP in 2020 (World Bank) while also providing livelihoods for approximately 57% of the population (World Bank, 2017). The Department of Agriculture (DoA) in Bhutan still relies on in-field reporting for crop monitoring, which is time-consuming and labour intensive. To promote efficiency in these efforts, the team partnered with the DoA, the Bhutan Foundation, and the Ugyen Wangchuck Institute of Conservation and Environmental Research (UWICER). The team, with the help of the science advisors from NASA SERVIR, expanded the crop mask created in the previous term to the whole country of Bhutan and streamlined the sampling protocols for applicability to any available crop data. The team also created a graphical user interface (GUI) which provided a visual representation of current trends and rice distribution across Bhutan. The team utilized NASA Earth observations, including Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), Landsat 8 Operational Land Imager (OLI), and Shuttle Radar Topography Mission (SRTM), as well as other Earth observations including Sentinel-1 C-band Synthetic Aperture Radar (C-SAR). This project refined the previous term’s methodology to help supplement crop monitoring and increase the frequency of data collected to aid decision-making processes with the use of remote sensing data.
A majority of the countries in the Middle East and North Africa (MENA) region suffer from water scarcity due in part to widespread rainfall deficits, unprecedented levels of water demand, and the inefficient use of renewable freshwater resources. Since a majority of the water withdrawal in the MENA is used for irrigation, there is a desperate need for improved understanding of irrigation practices and agricultural water use in the region. Here, satellite-derived irrigation maps and crop-type agricultural data are applied to the Land Data Assimilation System for the MENA region (MENA LDAS), designed to provide regional, gridded fields of hydrological states and fluxes relevant for water resources assessments. Within MENA-LDAS, the Catchment Land Surface Model (CLSM) simulates the location, timing, and amount of water applied through agricultural irrigation practices over the region from 2002-2012. In addition to simulating the irrigation impact on evapotranspiration, soil moisture, and runoff, we also investigate regional changes in terrestrial water storage (TWS) observed from the Gravity Recovery and Climate Experiment (GRACE) and simulated by CLSM.
The threat of crop disease on food security and agriculture is projected to escalate, leading to reduced crop yields, global economic loss, and endangered food availability to vulnerable populations. Early identification of plant disease is crucial to combatting the crisis but traditional manual methods for disease detection are laborious and may miss early signs of infection. The proposed solution suggests equipping a drone with a hyperspectral camera to collect images and analyzing the data with neural networks trained to flag and classify infected plants. This approach offers a faster, more accurate, and potentially more cost-effective alternative to current practices. While the expensive and complex nature of hyperspectral imaging (HSI) may be an obstacle to the adoption of the drone, rapidly advancing technologies compounded with rental-based usage may make these tools simpler, cheaper, and more accessible to a wider audience. The research further discusses the potential for automated flight paths and the expansion of disease detection to a broader range of crops.
The author has identified the following significant results. Agricultural areas were selected or analysis in southeastern Pennsylvania, north central Montana, and southern Texas. These three sites represent a broad range of soils, soil parent materials, climate, modes of agricultural operation, crops, and field sizes. In each of these three sites, ERTS-1 digital data were processed to determine the feasibility of automatically mapping agricultural land use. In Pennsylvania, forest land, cultivated land, and water were separable within a 25,000 acre area. Four classes of water were also classified and identified, using ground truth. A less complex land use pattern was analyzed in Hill County, Montana. A land use map was prepared shown alternating patterns of summer fallow and stubble fields. The location of farmsteads could be inferred, along with that of a railroad line. A river and a creek flowing into the river were discernible. Six categories of water, related to sediment content and depth, were defined in the reservoir held by the Fresno dam. These classifications were completed on a 150 square mile area. Analysis of the data from Texas is in its formative stages. A test site has been selected and a brightness map has been produced.
California seeks to become a carbon neutral state by 2045. To track progress toward this goal, it is important to quantify the amount of carbon stored by various landcover types across the state. Vineyards and orchards make up a large portion of California's agricultural landcover and store considerable amounts of carbon. For this project, NASA DEVELOP partnered with the California Air Resources Board to estimate the age and carbon stock of crop-specific agricultural regions across California between 1984 and 2021. The DEVELOP team created the Perennial Agriculture Age and Carbon Estimation Tool (PAACET), a Google Earth Engine tool that employs Earth observation from the Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), and Landsat 8 Operational Land Imager (OLI) to estimate the age and carbon stock of vineyards and almond, walnut, pistachio, and orange orchards. The team used an ocular sampling accuracy assessment consisting of 53 sample vineyards and orchards and found that on average, PAACET estimated ages within ±4.3 years of their actual age. The tool estimated that vineyards and walnut orchards are the oldest woody croplands, and almond orchards store the largest total carbon.
Knowledge on geographical location and distribution of crops at global, national and regional scales is an extremely valuable source of information applications. Traditional approaches to crop mapping using remote sensing data rely heavily on reference or ground truth data in order to train/calibrate classification models. As a rule, such models are only applicable to a single vegetation season and should be recalibrated to be applicable for other seasons. This paper addresses the problem of early season large-area winter crop mapping using Moderate Resolution Imaging Spectroradiometer (MODIS) derived Normalized Difference Vegetation Index (NDVI) time-series and growing degree days (GDD) information derived from the Modern-Era Retrospective analysis for Research and Applications (MERRA-2) product. The model is based on the assumption that winter crops have developed biomass during early spring while other crops (spring and summer) have no biomass. As winter crop development is temporally and spatially non-uniform due to the presence of different agro-climatic zones, we use GDD to account for such discrepancies. A Gaussian mixture model (GMM) is applied to discriminate winter crops from other crops (spring and summer). The proposed method has the following advantages: low input data requirements, robustness, applicability to global scale application and can provide winter crop maps 1.5-2 months before harvest. The model is applied to two study regions, the State of Kansas in the US and Ukraine, and for multiple seasons (2001-2014). Validation using the US Department of Agriculture (USDA) Crop Data Layer (CDL) for Kansas and ground measurements for Ukraine shows that accuracies of greater than 90% can be achieved in mapping winter crops 1.5-2 months before harvest. Results also show good correspondence to official statistics with average coefficients of determination R(exp. 2) greater than 0.85.
The Bhutan Agriculture III team aimed to improve agricultural efficiency in Bhutan. Bhutan is a nation heavily reliant on agriculture, but it faces challenges such as geophysical limitations and lack of scientific agricultural practice. The team partnered with a primary end user, Bhutan’s Department of Agriculture (DoA), and with collaborators; the Bhutan Foundation, National Plant Protection Centre (NPPC), Agricultural Research Department Centre (ARDC), National Statistics Bureau (NSB), and the Ugyen Wangchuck Institute for Conservation and Environment Research (UWICER). Advised by NASA SERVIR, the team developed crop masks and monitored rice distribution from 2015 to 2022 utilizing Earth observations such as Landsat 8 Operational Land Imager (OLI), Landsat 9 OLI-2, Sentinel-1 C-Band Synthetic Aperture Radar (C-SAR), Sentinel-2 MultiSpectral Instrument (MSI) and Shuttle Radar Topography Mission (SRTM). The team gathered 5,000 points from the five dzongkhags that yield the most rice in Bhutan (Paro, Punakha, Samtse, Sarpang and Wangue Phodrang) using Collect Earth Online (CEO). With the data collected, the team split the data into training and validation data on Google Earth Engine (GEE) for a random forest (RF) classifier for rice and non-rice classification. After running the data on the Random Forest (RF) model, the team got an accuracy score of 81.48%, a kappa score of 55.75% and an F1 score of 86.11%. This data supports better agricultural decision-making for the governing body of Bhutan, helps enhance farming efficiency and foster sustainable practices, assists in overcoming data inaccuracy and bolsters food security in the country.
A classification procedure for assessing crop areal proportion in multispectral scanner image is discussed. The procedure is into four parts: labeling; classification; proportion estimation; and evaluation. The procedure also has the following characteristics: multitemporal classification; the need for a minimum field information; and verification capability between automatic classification and analyst labeling. The processing steps and the main algorithms involved are discussed. An outlook on the future of this technology is also presented.
Bioreactor retention time is a key process variable that will influence costs that are relevant to long distance space travel or long duration space habitation. However. little is known about the effects of this parameter on the microbiological treatment options that are being proposed for Advanced Life Support (ALS) systems. Two bioreactor studies were designed to examine this variable. In the first one, six retention times ranging from 1.3 to 21.3 days--were run in duplicate, 81 working-volume continuous stirred tank reactors (CSTR) that were fed ALS wheat residues. Ash-free dry weight loss, carbon mineralization, soluble TOC reduction, changes in fiber content (cellulose, hemicellulose, and lignin), bacterial numbers, and mineral recoveries were monitored. At short retention times--1.33 days--biodegradation was poor (total: 16-20%, cellulose - 12%, hemicellulose - 28%) but soluble TOC was decreased by 75-80% and recovery of major crop inorganic nutrients was adequate, except for phosphorus. A high proportion of the total bacteria (ca. 83%) was actively respiring. At the longest retention time tested, 21.3 days, biodegradation was good (total: 55-60%, cellulose ca. 70%, hemicellulose - ca. 55%) and soluble TOC was decreased by 80%. Recovery of major nutrients, except phosphorus, remained adequate. A very low proportion of total bacteria was actively respiring (ca. 16%). The second bioreactor study used potato residue to determine if even shorter retention times could be used (range 0.25-2.0 days). Although overall biodegradation deteriorated, the degradation of soluble TOC continued to be ca. 75%. We conclude that if the goal of ALS bioprocessing is maximal degradation of crop residues, including cellulose, then retention times of 10 days or longer will be needed. If the goal is to provide inorganic nutrients with the smallest volume/weight bioreactor possible, then a retention time of 1 day (or less) is sufficient.
If humans are to live permanently in space, regenerative life support systems are an enabling technology and must replace the picnic approach of taking all supplies required for each mission. These systems are classified by technologies as either physical/chemical or bioregenerative. Both of these system-types can recycle water, remove carbon dioxide, produce oxygen, and recover essential elements from waste products. Bioregenerative can also produce food, thus, making it essential if humans are to exist in space independent of earth. A solely bioregenerative life support system includes plants as a biomass production module and microbial organisms in bioreactors as a resource recovery module. In the Advanced Life Support Program, bioregenerative life support systems are being investigated through a research and technology development project which includes large scale testing as part of the Breadboard Project and human tests conducted in the soon to be constructed BioPlex facility. Research and technology development efforts are directed toward optimizing biomass productivity in controlled chambers by developing light weight, energy efficient, and automated systems; recycling liquid and solid wastes; baselining the operation of bioreactors; determining system microbial stability; assessing chemical contamination; and building models required for long term system operations. The program will include space flight studies in the near future to determine if these life support technologies will function in microgravity. When a bioregenerative system is finally incorporated into a mission, the conversion from a picnic and resupply mentality to permanent recycling and independence from earth will be complete.
Calcium bioavailability of vegetarian diets containing various proportions of candidate crops for a controlled ecological life-support system (CELSS) was determined by femur 45Ca uptake. Three vegetarian diets and a control diet were labeled extrinsically with 45Ca and fed to 5-wk old male rats. A fifth group of rats fed an unlabeled control diet received an intraperitoneal (IP) injection of 45Ca. There was no significant difference in mean calcium absorption of vegetarian diets (90.80 +/- 5.23%) and control diet (87.85 +/- 5.25%) when calculated as the percent of an IP dose. The amounts of phytate, oxalate, and dietary fiber in the diets did not affect calcium absorption.
Edible yield must be maximized for each crop species selected for inclusion in the Controlled Ecological Life-Support System (CELSS) proposed by NASA to support long-term manned space missions. In a greenhouse study aimed at increasing biomass partitioning to rice (Oryza sativa L.) grain, plants of the high yielding semi-dwarf rice cultivar Ai-Nan-Tsao were started in pots under 8-h photoperiods at a density of 212 plants m-2. After different periods of time under 8-h photoperiods, pots were switched to continuous light for the remainder of the cropping cycle. Continuous light did not delay time to first panicle emergence (60 d) or time to harvest (83 d). There was a positive correlation between the length of continuous light treatments and nongrain biomass. Grain yield (1.6 +/- 0.2 g plant-1) did not increase in continuous light. Yield-efficiency rate (grain weight per length of cropping cycle, canopy volume, and weight of nongrain shoot biomass) was used to compare treatments. Small Ai-Nan-Tsao rice canopies grown under 8-h photoperiods were more efficient producers of grain than canopies grown under continuous light for a portion of the rice cropping cycle.
Nutrient release in clinoptilolite-phosphate rock (Cp-PR) systems occurs through dissolution and cation-exchange reactions. Investigating the kinetics of these reactions expands our understanding of nutrient release processes. Research was conducted to model transport kinetics of nutrient release in Cp-PR systems. The objectives were to identify empirical models that best describe NH4, K, and P release and define diffusion-controlling processes. Materials included a Texas clinoptilolite (Cp) and North Carolina phosphate rock (PR). A continuous-flow thin-disk technique was used. Models evaluated included zero order, first order, second order, parabolic diffusion, simplified Elovich, Elovich, and power function. The power-function, Elovich, and parabolic-diffusion models adequately described NH4, K, and P release. The power-function model was preferred because of its simplicity. Models indicated nutrient release was diffusion controlled. Primary transport processes controlling nutrient release for the time span observed were probably the result of a combination of several interacting transport mechanisms.
No abstract available
A controlled ecological life-support system (CELSS) is required to sustain life for long-duration space missions. The challenge is preparing a wide variety of tasty, familiar, and nutritious foods from CELSS candidate crops under space environmental conditions. Conventional food processing technologies will have to be modified to adapt to the space environment. Extrusion is one of the processes being examined as a means of converting raw plant biomass into familiar foods. A nutrition-improved pasta has been developed using cowpea as a replacement for a portion of the durum semolina. A freeze-drying system that simulates the space conditions has also been developed. Other technologies that would fulfill the requirements of a CELSS will also be addressed.
A perspective of the process of obtaining performance robustness of space exploration life support systems is presented. Some useful definitions are made, some relevant issues are addressed, and a procedure for establishing performance robustness, so far as it now known, is explained. An example is given to illustrate the procedure.