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NASA NTRS2002

Modeling and control for closed environment plant production systems

A computer program was developed to study multiple crop production and control in controlled environment plant production systems. The program simulates crop growth and development under nominal and off-nominal environments. Time-series crop models for wheat (Triticum aestivum), soybean (Glycine max), and white potato (Solanum tuberosum) are integrated with a model-based predictive controller. The controller evaluates and compensates for effects of environmental disturbances on crop production scheduling. The crop models consist of a set of nonlinear polynomial equations, six for each crop, developed using multivariate polynomial regression (MPR). Simulated data from DSSAT crop models, previously modified for crop production in controlled environments with hydroponics under elevated atmospheric carbon dioxide concentration, were used for the MPR fitting. The model-based predictive controller adjusts light intensity, air temperature, and carbon dioxide concentration set points in response to environmental perturbations. Control signals are determined from minimization of a cost function, which is based on the weighted control effort and squared-error between the system response and desired reference signal.

NASA Discipline Life Support Systems↗

NASA NTRS2024

The Economics of Farming Expansion in the Brazilian Cerrado Under Possible Effects of Climate Change

This analysis assesses the financial viability of legally investing in native Cerrado vegetation deforestation for crop production, considering climate change. The study uses data from twelve different crop models based on three different climate models to predict potential future crop yields in cleared land for growing soy and maize. The outcomes show that in many micro-regions, investments in clearing land for crop production would destroy economic value, that is, generate a negative net present value because of low/negative and volatile cashflows driven primarily by future yields as affected by climate. Our analysis was carried out based on present agricultural practices and technology. As climate changes, farmers may adapt their practices, which can lead to more resilient and productive crops, or grow different crops, which could provide better returns on investment in clearing land than the ones resulting from our analysis. Despite various uncertainties, farmers, policy makers and financial institutions should be aware of the climatic and financial risks associated with land clearing in Brazil, mainly in micro-regions in which all scenarios resulted in negative outcomes in the investment analysis.

agricultural expansion↗

DOE OSTI2022

Land use for bioenergy: Synergies and trade-offs between sustainable development goals

Bioenergy aims to reduce greenhouse gas (GHG) emissions and contribute to meeting global climate change mitigation targets. Nevertheless, several sustainability concerns are associated with bioenergy, especially related to the impacts of using land for dedicated energy crop production. Cultivating energy crops can result in synergies or trade-offs between GHG emission reductions and other sustainability effects depending on context-specific conditions. Using the United Nations Sustainable Development Goals (SDGs) framework, the main synergies and trade-offs associated with land use for dedicated energy crop production were identified. Furthermore, the context-specific conditions (i.e., biomass feedstock, previous land use, climate, soil type and agricultural management) which affect those synergies and trade-offs were also identified. The most recent literature was reviewed and a pairwise comparison between GHG emission reduction (SDG 13) and other SDGs was carried out. A total of 427 observations were classified as either synergy (170), trade-off (176), or no effect (81). Most synergies with environmentally-related SDGs, such as water quality and biodiversity conservation, were observed when perennial crops were produced on arable land, pasture or marginal land in the ‘cool temperate moist’ climate zone and ‘high activity clay’ soils. Most trade-offs were related to food security and water availability. Previous land use and feedstock type are more impactful in determining synergies and trade-offs than climatic zone and soil type. This study highlights the importance of considering context-specific conditions in evaluating synergies and trade-offs and their relevance for developing appropriate policies and practices to meet worldwide demand for bioenergy in a sustainable manner.

09 BIOMASS FUELS↗

NASA NTRS2021

Challenges and opportunities for enhancing food security and greenhouse gas mitigation in smallholder farming in sub-Saharan Africa. A review

Smallholder farmers struggle to achieve food security in many countries of sub-Saharan Africa (SSA). It is urgently required to find appropriate practices for enhancing crop production while avoiding large increases in greenhouse gas (GHG) emissions in SSA. This review aims to identify common smallholder farming practices for enhancing crop production, to assess how these affect GHG emissions and to identify strategies that not only enhance crop production but also mitigate GHG emissions in SSA. To increase crop production and ensure food security, smallholder farmers usually expand agricultural land, develop water harvesting and irrigation techniques and increase cropping intensity and fertilizer use. These practices may result in changing carbon stocks and GHG emissions, potentially creating trade-offs between food security and GHG mitigation. Agricultural land expansion at the expense of forests is the most dominant source of GHG emissions in SSA. While water harvesting and irrigation can increase soil organic carbon, they can trigger GHG emissions. Increasing cropping intensity can enhance the decomposition of soil organic matter, thus releasing carbon dioxide. Increasing nitrogen fertilizer use can enhance soil organic carbon, but also leads to increasing nitrous oxide emissions. An integrated land, water and nutrient management strategy is necessary to enhance crop production and mitigate GHG emissions. Among the most relevant strategies found, agroforestry practices in degraded and marginal lands could replace expanding agricultural croplands. In addition, water management, via adequate rainwater harvesting and irrigation techniques, together with appropriate nutrient management should be considered. Therefore, a land-water-nutrient nexus (LWNN) approach will enable an integrated and sustainable solution to increasing crop production and mitigating GHG emissions. Various technical, economic and policy barriers hinder implementing the LWNN approach on the ground, but these may be overcome through developing appropriate technologies, disseminating them through farmer to farmer approaches and developing specific policies to address smallholder land tenure issues and motivate long-term investment.

Sub-Sahara Africa↗

DOE OSTI2024

Soil application of high-lignin fermentation byproduct to increase the sustainability of liquid biofuel production from crop residues

When digestates from anaerobic digestion of crop residues are added to soil, a considerable body of information indicates that soil organic carbon (SOC) levels are comparable to those when crop residues are left in the field. This occurs although the amount of digestate added to soil is diminished by digestion and implies that digestion increases the proportion of carbon inputs stabilized as SOC. Here we examine the likelihood and implications of these features being manifested for soil application of high lignin-fermentation byproduct (HLFB) from liquid biofuel production. We show that steady-state SOC levels are much less sensitive to crop residue removal with HLFB return than without it, and provide an example supporting the feasibility of foregoing process energy and coproduct revenue when HLFB is returned to the soil. Informed by this review and analysis, we expect with moderate confidence that long-term SOC levels for soils amended with HLFB from some liquid cellulosic biofuel processes will not be substantially lower than those occurring when crop residues are left in the field. We have high confidence that the economically optimum rate of fertilizer nitrogen (N) application and N 2 O emissions will be lower at most sites for HLFB return to the soil than if crop residues were left in the field. We estimate that the per hectare N demand for processing crop residues to liquid biofuels is about a third of the per hectare demand for crop production, giving rise to an opportunity to use N twice and thereby realize cost savings and environmental benefits. These observations support but do not prove the hypothesis that a ‘win-win’ is possible wherein large amounts of liquid biofuel feedstock can be obtained from crop residues while improving the economics and sustainability of food and feed production. A research agenda aimed at exploring and testing this hypothesis is offered.

09 BIOMASS FUELS↗

DOE OSTI2025

Interactions Between Climate Mean and Variability Drive Future Agroecosystem Vulnerability

ABSTRACT Agriculture is crucial for global food supply and dominates the Earth's land surface. It is unknown, however, how slow but relentless changes in climate mean state, versus random extreme conditions arising from changing variability , will affect agroecosystems' carbon fluxes, energy fluxes, and crop production. We used an advanced weather generator to partition changes in mean climate state versus variability for both temperature and precipitation, producing forcing data to drive factorial‐design simulations of US Midwest agricultural regions in the Energy Exascale Earth System Model. We found that an increase in temperature mean lowers stored carbon, plant productivity, and crop yield, and tends to convert agroecosystems from a carbon sink to a source, as expected; it also can cause local to regional cooling in the earth system model through its effects on the Bowen Ratio. The combined effect of mean and variability changes on carbon fluxes and pools was nonlinear, that is, greater than each individual case. For instance, gross primary production reduces by 9%, 1%, and 13% due to change in mean temperature, change in temperature variability, and change in both temperature mean and variability, respectively. Overall, the scenario with change in both temperature and precipitation means leads to the largest reduction in carbon fluxes (−16% gross primary production), carbon pools (−35% vegetation carbon), and crop yields (−33% and −22% median reduction in yield for corn and soybean, respectively). By unambiguously parsing the effects of changing climate mean versus variability and quantifying their nonadditive impacts, this study lays a foundation for more robust understanding and prediction of agroecosystems' vulnerability to 21st‐century climate change.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRSDate not supplied

Selection Factors for Space Crops

NASA is actively researching space crop production to determine its potential to contribute to food system security on long duration missions beyond Low Earth Orbit. Our near-term focus is on nutrient and variety supplementation of prepackaged food with fresh produce that requires little or no processing. The longer-term goal is caloric replacement to become less dependent on Earth, and this will require cultivation of staple crops, processing and cooking equipment, integration with spacecraft air, water, and power systems, and automation. There are numerous technology and knowledge gaps remaining for sustainable space crop production systems, but one high-impact area is in the development of crops specifically customized to meet the needs of controlled environment crop production, astronaut health and well-being, and space-unique environments. Modern crop breeding and genome engineering tools are allowing for rapid development of new genotypes with incredible specificity. Targeted aspects to optimize crops for space have been identified and characterized into five categories: plant growth and development, plant physiology, produce nutrition, produce organoleptic acceptability, and postharvest characteristics. Within each category there are several targets that further the development of crop production systems for spaceflight, such as crop size and harvest index, tolerance to specific environmental stresses, optimizing target nutrients that are low or degrade in the packaged diet, maintenance time requirements, and less indigestible structural material. NASA-funded PIs are already beginning to develop candidate crops, and spaceflight testing and validation of novel space crops is on the horizon. Crops developed for space also have the potential to benefit terrestrial controlled environment agriculture crop production systems. This research was supported by NASA’s Space Biology and Human Research Programs.

Space Crop Production↗

NASA NTRSDate not supplied

Selection Factors for Space Crops

Space Crop Production↗

DOE OSTI2026

Economic Incentives for Agrivoltaics Systems with Commodity Crops in the Midwestern United States

Declining costs of photovoltaic (PV) technology and rising market and policy incentives are leading to the growing deployment of PV on cropland in the US Midwest, leading to concerns about the displacement of food and feed crop production. Agrivoltaic (AV) technology enables the dual use of land by co-locating PV energy and crop production, potentially reducing land-use competition with crop production. We develop a benefit-cost analysis framework to compare the net economic returns from AV to those with stand-alone PV and crop production on a representative field and show conditions under which AV can be more profitable for both a solar developer and a farmer. We integrate it with a crop and solar energy model to simulate the performance of various field designs and space and height configurations in AV systems to accommodate soybean production with conventional farm equipment under representative conditions in the US Midwest. We find that an AV system with soybean production is less profitable than PV alone for a solar developer due to the high capital costs of raising panel height, and less profitable for a farmer than leasing land for PV due to its adverse effects of shading on crop yield. We discuss the changes in technology and market prices of solar energy and soybeans that are necessary to make the AV system profitable for solar developers and farmers. We show that AV can worsen rather than mitigate the conflict between food crops and solar energy production in the Midwest.

14 SOLAR ENERGY↗

NASA NTRSDate not supplied

Developing a Customizable Composite Drought Index for Pakistan

Pakistan has experienced intense agricultural droughts in recent years, with the southern provinces experiencing the most severe drought conditions. These prolonged dry conditions often result in failed crop production, impacting families and communities. To reduce the impacts a drought may have on a community by identifying dry conditions, drought indices are used. A customizable composite drought index (CDI) is developed to improve the spatial and temporal understanding of historical agricultural droughts that have affected Pakistan. An in depth analysis using 9 input variables will provide information regarding the most important variables for differing locations. This framework can enhance drought monitoring and forecasting systems. The performance of the CDI will be evaluated by using production data from different crops that have significant economic value in Pakistan. These crops include wheat, rice, maize, cotton and barley. The expected relationship between the CDI and crop production would be to see production decrease when the CDI decreases, an indication that crops failed due to drought. The CDI is made more precise by evaluating only agricultural areas using the months of the growing season for each specific crop. This will allow for a custom drought index to be developed based on crop type and geographic location that only uses the most important variables, while still capturing the full extent of historical drought.

Caily Schwartz↗

NASA NTRS2002

Bioregenerative food system cost based on optimized menus for advanced life support

Optimized menus for a bioregenerative life support system have been developed based on measures of crop productivity, food item acceptability, menu diversity, and nutritional requirements of crew. Crop-specific biomass requirements were calculated from menu recipe demands while accounting for food processing and preparation losses. Under the assumption of staggered planting, the optimized menu demanded a total crop production area of 453 m2 for six crew. Cost of the bioregenerative food system is estimated at 439 kg per menu cycle or 7.3 kg ESM crew-1 day-1, including agricultural waste processing costs. On average, about 60% (263.6 kg ESM) of the food system cost is tied up in equipment, 26% (114.2 kg ESM) in labor, and 14% (61.5 kg ESM) in power and cooling. This number is high compared to the STS and ISS (nonregenerative) systems but reductions in ESM may be achieved through intensive crop productivity improvements, reductions in equipment masses associated with crop production, and planning of production, processing, and preparation to minimize the requirement for crew labor.

Non-NASA Center↗

NASA NTRS2015

Rising Temperatures Reduce Global Wheat Production

Crop models are essential tools for assessing the threat of climate change to local and global food production. Present models used to predict wheat grain yield are highly uncertain when simulating how crops respond to temperature. Here we systematically tested 30 different wheat crop models of the Agricultural Model Intercomparison and Improvement Project against field experiments in which growing season mean temperatures ranged from 15 degrees C to 32◦ degrees C, including experiments with artificial heating. Many models simulated yields well, but were less accurate at higher temperatures. The model ensemble median was consistently more accurate in simulating the crop temperature response than any single model, regardless of the input information used. Extrapolating the model ensemble temperature response indicates that warming is already slowing yield gains at a majority of wheat-growing locations. Global wheat production is estimated to fall by 6% for each degree C of further temperature increase and become more variable over space and time.

agriculture↗

NASA NTRS2018

Two-thirds of Global Cropland Area Impacted by Climate Oscillations

The El Nino Southern Oscillation (ENSO) peaked strongly during the boreal winter 2015-2016, leading to food insecurity in many parts of Africa, Asia and Latin America. Besides ENSO, the Indian Ocean Dipole (IOD) and the North Atlantic Oscillation (NAO) are known to impact crop yields worldwide. Here we assess for the first time in a unified framework the relationship between ENSO, IOD and NAO and simulated crop productivity at the sub-country scale. Our findings reveal that during 1961-2010, crop productivity is significantly influenced by at least one large-scale climate oscillation in two-thirds of global cropland area. Besides observing new possible links, especially for NAO in Africa and the Middle East, our analyses confirm several known relationships between crop productivity and these oscillations. Our results improve the understanding of climatological crop productivity drivers, which is essential for enhancing food security in many of the most vulnerable places on the planet.

IOD↗

DOE OSTI2020

In silico assessment of the potential of basalt amendments to reduce N 2 O emissions from bioenergy crops

Abstract The potential of large‐scale deployment of basalt to reduce N 2 O emissions from cultivated soils may contribute to climate stabilization beyond the CO 2 ‐removal effect from enhanced weathering. We used 3 years of field observations from maize ( Zea mays ) and miscanthus ( Miscanthus × giganteus ) to improve the nitrogen (N) module of the DayCent model and evaluate the potential of basalt amendments to reduce N losses and increase yields from two bioenergy crops. We found 20%–60% improvement in our N 2 O flux estimates over previous model descriptions. Model results predict that the application of basalt would reduce N 2 O emissions by 16% in maize and 9% in miscanthus. Lower N 2 O emissions responded to increases in the N 2 :N 2 O ratio of denitrification with basalt‐induced increases in soil pH, with minor contributions from the impact of P additions (a minor component of some basalts) on N immobilization. The larger reduction of N 2 O emissions in maize than in miscanthus was likely explained by a synergistic effect between soil pH and N content, leading to a higher sensitivity of the N 2 :N 2 O ratio to changes in pH in heavily fertilized maize. Basalt amendments led to modest increases in modeled yields and the nitrogen use efficiency (i.e., fertilizer‐N recover in crop production) of maize but did not affect the productivity of miscanthus. However, enhanced soil P availability maintained the long‐term productivity of crops with high nutrient requirements. The alleviation of plant P limitation led to enhanced plant N uptake, thereby contributing to lower microbial N availability and N 2 O emissions from crops with high nutrient requirements. Our results from the improved model suggest that the large‐scale deployment of basalt, by reducing N 2 O fluxes of cropping systems, could contribute to the sustainable intensification of agriculture and enhance the climate mitigation potential of bioenergy with carbon capture and storage strategies.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS1993

The dynamics of hydroponic crops for simulation studies of the CELSS initial reference configurations

During the past several years, the NASA Program in Controlled Ecological Life Support Systems (CELSS) has continued apace with crop research and logistic, technological, and scientific strides. These include the CELSS Test Facility planned for the space station and its prototype Engineering Development Unit, soon to be active at Ames Research Center (as well as the advanced crop growth research chamber at Ames); the large environmental growth chambers and the planned human test bed facility at Johnson Space Center; the NSCORT at Purdue with new candidate crops and diverse research into the CELSS components; the gas exchange data for soy, potatoes, and wheat from Kennedy Space Center (KSC); and the high-precision gas exchange data for wheat from Utah State University (USU). All these developments, taken together, speak to the need for crop modeling as a means to connect the findings of the crop physiologists with the engineers designing the system. A need also exists for crop modeling to analyze and predict the gas exchange data from the various locations to maximize the scientific yield from the experiments. One fruitful approach employs what has been called the 'energy cascade'. Useful as a basis for CELSS crop growth experimental design, the energy cascade as a generic modeling approach for CELSS crops is a featured accomplishment in this report. The energy cascade is a major tool for linking CELSS crop experiments to the system design. The energy cascade presented here can help collaborations between modelers and crop experimenters to develop the most fruitful experiments for pushing the limits of crop productivity. Furthermore, crop models using the energy cascade provide a natural means to compare, feature for feature, the crop growth components between different CELSS experiments, for example, at Utah State University and Kennedy Space Center.

Volk, Tyler↗

NASA NTRS2016

Uncertainty of Wheat Water Use: Simulated Patterns and Sensitivity to Temperature and CO2

Projected global warming and population growth will reduce future water availability for agriculture. Thus, it is essential to increase the efficiency in using water to ensure crop productivity. Quantifying crop water use (WU; i.e. actual evapotranspiration) is a critical step towards this goal. Here, sixteen wheat simulation models were used to quantify sources of model uncertainty and to estimate the relative changes and variability between models for simulated WU, water use efficiency (WUE, WU per unit of grain dry mass produced), transpiration efficiency (Teff, transpiration per kg of unit of grain yield dry mass produced), grain yield, crop transpiration and soil evaporation at increased temperatures and elevated atmospheric carbon dioxide concentrations ([CO2]). The greatest uncertainty in simulating water use, potential evapotranspiration, crop transpiration and soil evaporation was due to differences in how crop transpiration was modelled and accounted for 50 of the total variability among models. The simulation results for the sensitivity to temperature indicated that crop WU will decline with increasing temperature due to reduced growing seasons. The uncertainties in simulated crop WU, and in particularly due to uncertainties in simulating crop transpiration, were greater under conditions of increased temperatures and with high temperatures in combination with elevated atmospheric [CO2] concentrations. Hence the simulation of crop WU, and in particularly crop transpiration under higher temperature, needs to be improved and evaluated with field measurements before models can be used to simulate climate change impacts on future crop water demand.

Sensitivity↗

NASA NTRSDate not supplied

Adding Fresh Produce to the Space Diet

NASA’s Human Research Program has identified a Risk of inadequate nutrition and food stability as a risk of high concern for future long duration exploration missions such as a mission to Mars. One possible solution to help reduce this risk is the addition of in situ produced supplemental fresh produce to supplement and enhance the astronaut diet. A number of ground and ISS-based studies are being conducted which are helping to reduce this risk by filling the gaps in knowledge and technology associated with space crop production. These include studies with the Veggie vegetable production system on the International Space Station. Key focus areas include water and nutrient delivery, food safety and the crop microbiome, crop selection and testing, automation and robotics, radiation impacts, seed storage and handling, and scalability for different concepts and architectures. All of these areas have significant challenges that need to be addressed prior to successful integration of crop production into the crew diet. Data from these studies is feeding the development of new technologies and systems for space crop production. This research was funded by NASA Space Biology and NASA’s Human Research Program.

Space Crop Production↗

DOE OSTI2023

Predicting Biomass Yields of Advanced Switchgrass Cultivars for Bioenergy and Ecosystem Services Using Machine Learning

The production of advanced perennial bioenergy crops within marginal areas of the agricultural landscape is gaining interest due to its potential to sustainably produce feedstocks for biofuels and bioproducts while also improving the sustainability and resilience of commodity crop production. However, predicting the biomass yields of this production system is challenging because marginal areas are often relatively small and spread around agricultural fields and are typically associated with various abiotic conditions that limit crop production. Machine learning (ML) offers a viable solution as a biomass yield prediction tool because it is suited to predicting relationships with complex functional associations. The objectives of this study were to (1) evaluate the accuracy of commonly applied ML algorithms in agricultural applications for predicting the biomass yields of advanced switchgrass cultivars for bioenergy and ecosystem services and (2) determine the most important biomass yield predictors. Datasets on biomass yield, weather, land marginality, soil properties, and agronomic management were generated from three field study sites in two U.S. Midwest states (Illinois and Iowa) over three growing seasons. The ML algorithms evaluated in the study included random forests (RFs), gradient boosting machines (GBMs), artificial neural networks (ANNs), K-neighbors regressor (KNR), AdaBoost regressor (ABR), and partial least squares regression (PLSR). Coefficient of determination (R 2 ) and mean absolute error (MAE) were used to evaluate the predictive accuracy of the tested algorithms. Results showed that the ensemble methods, RF (R 2 = 0.86, MAE = 0.62 Mg/ha), GBM (R 2 = 0.88, MAE = 0.57 Mg/ha), and GBM (R 2 = 0.78, MAE = 0.66 Mg/ha), were the most accurate in predicting biomass yields of the Independence, Liberty, and Shawnee switchgrass cultivars, respectively. This is in agreement with similar studies that apply ML to multi-feature problems where traditional statistical methods are less applicable and datasets used were considered to be relatively small for ANNs. Consistent with previous studies on switchgrass, the most important predictors of biomass yield included average annual temperature, average growing season temperature, sum of the growing season precipitation, field slope, and elevation. This study helps pave the way for applying ML as a management tool for alternative bioenergy landscapes where understanding agronomic and environmental performance of a multifunctional cropping system seasonally and interannually at the sub-field scale is critical.

09 BIOMASS FUELS↗