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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
NASA NTRS1992

Effects of physical agitation on yield of greenhouse-grown soybean

Agronomic and horticultural crop species experience reductions in growth and harvestable yield after exposure to physical agitation (also known as mechanical stress), as by wind or rain. A greenhouse study was conducted to test the influence of mechanical stress on soybean yield and to determine if exposure to mechanical stress during discrete growth periods has differential effects on seed yield. A modified rotatory shaker was used to apply seismic (i.e., shaking) stress. Brief, periodic episodes of seismic stress reduced stem length, total seed dry weight, and seed number of soybean [Glycine max (L.) Merr.]. Lodging resistance was greater for plants stressed during vegetative growth or throughout vegetative and reproductive growth than during reproductive growth only. Seed dry weight yield was reduced regardless of the timing or duration of stress application, but was lowest when applied during reproductive development. Seismic stress applied during reproductive growth stages R1 to R2 (Days 3 to 4) was as detrimental to seed dry weight accumulation as was stress applied during growth stages R1 to R6 (Days 39 to 42). Seed dry weight per plant was highly correlated with seed number per plant, and seed number was correlated with the seed number of two- and three-seeded pods. Dry weight per 100 seeds was unaffected by seismic-stress treatment. Growth and yield reductions resulting from treatments applied only during the vegetative stage imply that long-term mechanical effects were induced, from which the plants did not fully recover. It is unclear which yield-controlling physiological processes were affected by mechanical stress. Both transient and long-term effects on yield-controlling processes remain to be elucidated.

Non-NASA Center↗
NASA NTRSDate not supplied

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↗
DOE OSTI2022

Post-flowering Soil Waterlogging Curtails Grain Yield Formation by Restricting Assimilates Supplies to Developing Grains

Soil waterlogging is among the major factors limiting the grain yield of winter wheat crops in many parts of the world, including the middle and lower reaches of the Yangtze River China. In a field study, we investigated the relationship between leaf physiology and grain development under a varying duration of post-flowering waterlogging. A winter wheat cultivar Ningmai 13 was exposed to soil waterlogging for 0 (W0), 3 (W3), 6 (W6), and 9 d (W9) at anthesis. Increasing waterlogging duration significantly reduced flag leaf SPAD (soil plant analysis development) values and net photosynthetic rate (Pn). There was a linear reduction in flag leaf Pn and SPAD as plant growth progressed under all treatments; however, the speed of damage was greater in the waterlogged leaves. For example, compared with their respective control (W0), flag leaves of W9 treatment have experienced 46% more reduction in Pn at 21 d after anthesis (DAA) than at 7 DAA. Increasing waterlogging duration also induced oxidative damage in flag leaves, measured as malondialdehyde (MDA) contents. The capacity to overcome this oxidative damage was limited by the poor performance of antioxidant enzymes in wheat leaves. Inhibited leaf Pn and capacity to sustain assimilate synthesis under waterlogged environments reduced grain development. Compared with W0, W6 and W9 plants experienced a 20 and 22% reduction in thousand grain weight (TGW) in response to W6 and W9, respectively at 7 DAA and 11 and 19%, respectively at 28 DAA. Sustained waterlogging also significantly reduced grain number per spike and final grain yield. Averaged across two years of study, W9 plants produced 28% lesser final grain yield than W0 plants. Our study suggested that wheat crops are highly sensitive to soil waterlogging during reproductive and grain filling phases due to their poor capacity to recover from oxidative injury to photosynthesis. Management strategies such as planting time, fertilization and genotype selection should be considered for the areas experiencing frequent waterlogging problems.

Ma, Shangyu↗
DOE OSTI2021

Agricultural Management Affects the Active Rhizosphere Bacterial Community Composition and Nitrification

Cropping system diversity provides yield benefits that may result from shifts in the composition of root-associated bacterial and fungal communities, which either enhance nutrient availability or limit nutrient loss. We investigated whether temporal diversity of annual cropping systems (four versus two crops in rotation) influences the composition and metabolic activities of root-associated microbial communities in maize at a developmental stage when the peak rate of nitrogen uptake occurs. We monitored total (DNA-based) and potentially active (RNA-based) bacterial communities and total (DNA-based) fungal communities in the soil, rhizosphere, and endosphere. Cropping system diversity strongly influenced the composition of the soil microbial communities, which influenced the recruitment of the resident microbial communities and, in particular, the potentially active rhizosphere and endosphere bacterial communities. The diversified cropping system rhizosphere recruited a more diverse bacterial community (species richness), even though there was little difference in soil species richness between the two cropping systems. In contrast, fungal species richness was greater in the conventional rhizosphere, which was enriched in fungal pathogens; the diversified rhizosphere, however, was enriched in Glomeromycetes. While cropping system influenced endosphere community composition, greater correspondence between DNA- and RNA-based profiles suggests a higher representation of active bacterial populations. Cropping system diversity influenced the composition of ammonia oxidizers, which coincided with diminished potential nitrification activity and gross nitrate production rates, particularly in the rhizosphere. The results of our study suggest that diversified cropping systems shift the composition of the rhizosphere’s active bacterial and total fungal communities, resulting in tighter coupling between plants and microbial processes that influence nitrogen acquisition and retention.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2016

Pick-and-Eat Salad-Crop Productivity, Nutritional Value, and Acceptability to Supplement the ISS Food System

The capability to grow nutritious, palatable food for crew consumption during spaceflight has the potential to provide health-promoting, bioavailable nutrients, enhance the dietary experience, and reduce launch mass as we move toward longer-duration missions. However, studies of edible produce during spaceflight have been limited, leaving a significant knowledge gap in the methods required to grow safe, acceptable, nutritious crops for consumption in space. Researchers from Kennedy Space Center, Johnson Space Center, Purdue University and ORBITEC have teamed up to explore the potential for plant growth and food production on the International Space Station (ISS) and future exploration missions. KSC, Purdue, and ORBITEC bring a history of plant and plant-microbial interaction research for ISS and for future bioregenerative life support systems. JSC brings expertise in Advanced Food Technology (AFT), Behavioral Health and Performance (BHP), and statistics. The Veggie vegetable-production system on the ISS offers an opportunity to develop a pick-and-eat fresh vegetable component to the ISS food system as a first step to bioregenerative supplemental food production. We propose growing salad plants in the Veggie unit during spaceflight, focusing on the impact of light quality and fertilizer formulation on crop morphology, edible biomass yield, microbial food safety, organoleptic acceptability, nutritional value, and behavioral health benefits of the fresh produce. The first phase of the project will involve flight tests using leafy greens, with a small Chinese cabbage variety, Tokyo bekana, previously down selected through a series of research tests as a suitable candidate. The second phase will focus on dwarf tomato. Down selection of candidate varieties have been performed, and the dwarf cultivar Red Robin has been selected as the test crop. Four light treatments and three fertilizer treatments will be tested for each crop on the ground, to down select to two light treatments and one fertilizer treatment to test on ISS. Our work will help define light colors, levels, and horticultural best practices to achieve high yields of safe, nutritious leafy greens and tomatoes to supplement a space diet of prepackaged food. Our final deliverable will be the development of growth protocols for these crops in a spaceflight vegetable production system. With this work, and potentially with other pending joint projects, we will continue the synergistic research to help close gaps in the human research roadmap, and enable humans to venture to Mars and beyond. This research was co-funded by the Human Research Program and Space Biology (MTL1075) in the ILSRA 2015 NRA call.

Behavioral Health↗
NASA NTRS2015

Modelling Bambara Groundnut Yield in Southern Africa: Towards a Climate-Resilient Future

Current agriculture depends on a few major species grown as monocultures that are supported by global research underpinning current productivity. However, many hundreds of alternative crops have the potential to meet real world challenges by sustaining humanity, diversifying agricultural systems for food and nutritional security, and especially responding to climate change through their resilience to certain climate conditions. Bambara groundnut (Vigna subterranea (L.) Verdc.), an underutilised African legume, is an exemplar crop for climate resilience. Predicted yield performances of Bambara groundnut by AquaCrop (a crop−water productivity model) were evaluated for baseline (1980−2009) and mid-century climates (2040−2069) under 20 downscaled Global Climate Models (CMIP5-RCP8.5), as well as for climate sensitivities (AgMIPC3MP) across 3 locations in Southern Africa (Botswana, South Africa, Namibia). Different land - races of Bambara groundnut originating from various semi-arid African locations showed diverse yield performances with diverse sensitivities to climate. S19 originating from hot-dry conditions in Namibia has greater future yield potential compared to the Swaziland landrace Uniswa Red-UN across study sites. South Africa has the lowest yield under the current climate, indicating positive future yield trends. Namibia reported the highest baseline yield at optimum current temperatures, indicating less yield potential in future climates. Bambara groundnut shows positive yield potential at temperatures of up to ~31degC, with further warming pushing yields down. Thus, many regions in Southern Africa can utilize Bambara groundnut successfully in the coming decades. This modelling exercise supports decisions on genotypic suitability for present and future climates at specific locations.

farm crops↗
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↗
NASA NTRS1982

Selection of the Argentine indicator region

Determined from available Argentine crop statistics, selection of the Indicator Region was based on the highest wheat, corn, and soybean producing provinces, which were: Buenos Aires, Cordoba, Entre Rios, and Santa Fe. Each province in Argentina was examined for the availability of LANDSAT data; area, yield and production statistics; crop calendars; and other ancillary data. The Argentine Indicator Region is described.

Ramirez, C. J.↗
NASA NTRS1985

Crop Characteristics Research: Growth and Reflectance Analysis

Much of the early research in remote sensing follows along developing spectral signatures of cover types. It was found, however, that a signature from an unknown cover class could not always be matched to a catalog value of known cover class. This approach was abandoned and supervised classification schemes followed. These were not efficient and required extensive training. It was obvious that data acquired at a single time could not separate cover types. A large portion of the proposed research has concentrated on modeling the temporal behavior of agricultural crops and on removing the need for any training data in remote sensing surveys; the key to which is the solution of the so-called 'signature extension' problem. A clear need to develop spectral estimaters of crop ontogenic stages and yield has existed even though various correlations have been developed. Considerable effort in developing techniques to estimate these variables was devoted to this work. The need to accurately evaluate existing canopy reflectance model(s), improve these models, use them to understand the crop signatures, and estimate leaf area index was the third objective of the proposed work. A synopsis of this research effort is discussed.

Badhwar, G. D.↗
DOE OSTI2020

Empirical Evidence for the Potential Climate Benefits of Decarbonizing Light Vehicle Transport in the U.S. with Bioenergy from Purpose-Grown Biomass with and without BECCS

Climate mitigation scenarios limiting global temperature increases to 1.5 °C rely on decarbonizing vehicle transport with bioenergy production plus carbon capture and storage (BECCS), but climate impacts for producing different bioenergy feedstocks have not been directly compared experimentally or for ethanol vs electric light-duty vehicles. A field experiment at two Midwest U.S. sites on contrasting soils revealed that feedstock yields of seven potential bioenergy cropping systems varied substantially within sites but little between. Bioenergy produced per hectare reflected yields: miscanthus > poplar > switchgrass > native grasses ≈ maize stover (residue) > restored prairie ≈ early successional. Greenhouse gas emission intensities for ethanol vehicles ranged from 20 to -179 g CO 2 e MJ –1 : maize stover $\gg$ miscanthus ≈ switchgrass ≈ native grasses ≈ poplar > early successional ≥ restored prairie; direct climate benefits ranged from ~80% (stover) to 290% (restored prairie) reductions in CO 2 e compared to petroleum and were similar for electric vehicles. With carbon capture and storage (CCS), reductions in emission intensities ranged from 204% (stover) to 416% (restored prairie) for ethanol vehicles and from 329 to 558% for electric vehicles, declining 27 and 15%, respectively, once soil carbon equilibrates within several decades of establishment. Extrapolation based on expected U.S. transportation energy use suggests that, once CCS potential is maximized with CO 2 pipeline infrastructure, negative emissions from bioenergy with CCS for light-duty electric vehicles could capture >900 Tg CO 2 e year –1 in the U.S. In the future, as other renewable electricity sources become more important, electricity production from biomass would offset less fossil fuel electricity, and the advantage of electric over ethanol vehicles would decrease proportionately.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

A genome resource for green millet Setaria viridis enables discovery of agronomically valuable loci

Abstract Wild and weedy relatives of domesticated crops harbor genetic variants that can advance agricultural biotechnology. Here we provide a genome resource for the wild plant green millet ( Setaria viridis ), a model species for studies of C 4 grasses, and use the resource to probe domestication genes in the close crop relative foxtail millet ( Setaria italica ). We produced a platinum-quality genome assembly of S. viridis and de novo assemblies for 598 wild accessions and exploited these assemblies to identify loci underlying three traits: response to climate, a ‘loss of shattering’ trait that permits mechanical harvest and leaf angle, a predictor of yield in many grass crops. With CRISPR–Cas9 genome editing, we validated Less Shattering1 ( SvLes1 ) as a gene whose product controls seed shattering. In S. italica , this gene was rendered nonfunctional by a retrotransposon insertion in the domesticated loss-of-shattering allele SiLes1-TE (transposable element). This resource will enhance the utility of S. viridis for dissection of complex traits and biotechnological improvement of panicoid crops.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1983

A meteorologically-driven yield reduction model for spring and winter wheat

A yield reduction model for spring and winter wheat was developed for large-area crop condition assessment. Reductions are expressed in percentage from a base yield and are calculated on a daily basis. The algorithm contains two integral components: a two-layer soil water budget model and a crop calendar routine. Yield reductions associated with hot, dry winds (Sukhovey) and soil moisture stress are determined. Input variables include evapotranspiration, maximum temperature and precipitation; subsequently crop-stage, available water holding percentage and stress duration are evaluated. No specific base yield is required and may be selected by the user; however, it may be generally characterized as the maximum likely to be produced commercially at a location.

Ravet, F. W.↗
NASA NTRS2014

Climate Change Effects on Agriculture: Economic Responses to Biophysical Shocks

Agricultural production is sensitive to weather and thus directly affected by climate change. Plausible estimates of these climate change impacts require combined use of climate, crop, and economic models. Results from previous studies vary substantially due to differences in models, scenarios, and data. This paper is part of a collective effort to systematically integrate these three types of models. We focus on the economic component of the assessment, investigating how nine global economic models of agriculture represent endogenous responses to seven standardized climate change scenarios produced by two climate and five crop models. These responses include adjustments in yields, area, consumption, and international trade. We apply biophysical shocks derived from the Intergovernmental Panel on Climate Change's representative concentration pathway with end-of-century radiative forcing of 8.5 W/m(sup 2). The mean biophysical yield effect with no incremental CO2 fertilization is a 17% reduction globally by 2050 relative to a scenario with unchanging climate. Endogenous economic responses reduce yield loss to 11%, increase area of major crops by 11%, and reduce consumption by 3%. Agricultural production, cropland area, trade, and prices show the greatest degree of variability in response to climate change, and consumption the lowest. The sources of these differences include model structure and specification; in particular, model assumptions about ease of land use conversion, intensification, and trade. This study identifies where models disagree on the relative responses to climate shocks and highlights research activities needed to improve the representation of agricultural adaptation responses to climate change.

farm crops↗
NASA NTRS1985

Evaluation of AIS Data for Agronomic and Rangeland Vegetation: Preliminary Results for August 1984 Flight over Nebraska Sandhills Agricultural Laboratory

Since 1978 scientists from the Center for Agricultural Meteorology and Climatology at the University of Nebraska have been conducting research at the Sandhills Agricultural Laboratory on the effects of water stress on crop growth, development and yield using remote sensing techniques. We have been working to develop techniques, both remote and ground-based, to monitor water stress, phenological development, leaf area, phytomass production and grain yields of corn, soybeans and sorghum. Because of the sandy soils and relatively low rainfall at the site it is an excellent location to study water stress without the necessity of installing expensive rainout shelters. The primary objectives of research with the airborne imaging spectrometer (AIS) data collected during an August 1984 flight over the Sandhills Agricultural Laboratory are to evaluate the potential of using AIS to: (1) discriminate crop type; (2) to detect subtle architectural differences that exist among different cultivars or hybrids of agronomic crops; (3) to detect and quantify, if possible, the level of water stress imposed on the crops; and (4) to evaluate leaf area and biomass differences for different crops.

Blad, B. L.↗
DOE OSTI2024

Continuous cropping of potato changed the metabolic pathway of root exudates to drive rhizosphere microflora

For potato production, continuous cropping (CC) could lead to autotoxicity buildup and microflora imbalance in the field soil, which may result in failure of crops and reduction in yield. In this study, non-targeted metabolomics (via liquid chromatography with tandem mass spectrometry (LC–MS/MS)) combined with metagenomic profiling (via high-throughput amplicon sequencing) were used to evaluate correlations between metabolomics of potato root exudates and communities of bacteria and fungi around potato plants to illustrate the impacts of CC. Potato plants were grown in soil collected from fields with various CC years (0, 1, 4, and 7 years). Metabolomic analysis showed that the contents and types of potential autotoxins in potato root exudates increased significantly in CC4 and CC7 plants (i.e., grown in soils with 4 and 7 years of CC). The differentially expressed metabolites were mainly produced via alpha-linolenic acid metabolism in plant groups CC0 and CC1 (i.e., no CC or 1 year CC). The metabolomics of the groups CC4 and CC7 became dominated by styrene degradation, biosynthesis of siderophore group non-ribosomal peptides, phenylpropanoid biosynthesis, and biosynthesis of various plant secondary metabolites. Continuous cropping beyond 4 years significantly changed the bacterial and fungal communities in the soil around the potato crops, with significant reduction of beneficial bacteria and accumulation of harmful fungi. Correlations between DEMs and microflora biomarkers were established with strong significances. These results suggested that continuous cropping of potato crops changed their metabolism as reflected in the plant root exudates and drove rhizosphere microflora to directions less favorable to plant growth, and it needs to be well managed to assure potato yield.

Xing, Yanhong↗
NASA NTRS1982

Evaluation of the Williams-type spring wheat model in North Dakota and Minnesota

The Williams type model, developed similarly to previous models of C.V.D. Williams, uses monthly temperature and precipitation data as well as soil and topological variables to predict the yield of the spring wheat crop. The models are statistically developed using the regression technique. Eight model characteristics are examined in the evaluation of the model. Evaluation is at the crop reporting district level, the state level and for the entire region. A ten year bootstrap test was the basis of the statistical evaluation. The accuracy and current indication of modeled yield reliability could show improvement. There is great variability in the bias measured over the districts, but there is a slight overall positive bias. The model estimates for the east central crop reporting district in Minnesota are not accurate. The estimate of yield for 1974 were inaccurate for all of the models.

Leduc, S.↗
NASA NTRS2015

Simulated Impacts of Climate Change on Water Use and Yield of Irrigated Sugarcane in South Africa

Reliable predictions of climate change impacts on water use, irrigation requirements and yields of irrigated sugarcane in South Africa (a water-scarce country) are necessary to plan adaptation strategies. Although previous work has been done in this regard, methodologies and results vary considerably. The objectives were (1) to estimate likely impacts of climate change on sugarcane yields, water use and irrigation demand at three irrigated sugarcane production sites in South Africa (Malelane, Pongola and La Mercy) for current (1980-2010) and future (2070-2100) climate scenarios, using an approach based on the Agricultural Model Inter-comparison and Improvement Project (AgMIP) protocols; and (2) to assess the suitability of this methodology for investigating climate change impacts on sugarcane production. Future climate datasets were generated using the Delta downscaling method and three Global Circulation Models (GCMs) assuming atmospheric CO2 concentration [CO2] of 734 ppm(A2 emissions scenario). Yield and water use were simulated using the DSSAT-Canegro v4.5 model. Irrigated cane yields are expected to increase at all three sites (between 11 and 14%), primarily due to increased interception of radiation as a result of accelerated canopy development. Evapotranspiration and irrigation requirements increased by 11% due to increased canopy cover and evaporative demand. Sucrose yields are expected to decline because of increased consumption of photo-assimilate for structural growth and maintenance respiration. Crop responses in canopy development and yield formation differed markedly between the crop cycles investigated. Possible agronomic implications of these results include reduced weed control costs due to shortened periods of partial canopy, a need for improved efficiency of irrigation to counter increased demands, and adjustments to ripening and harvest practices to counter decreased cane quality and optimize productivity. Although the Delta climate data downscaling method is considered robust, accurate and easily-understood, it does not change the future number of rain-days per month. The impacts of this and other climate data simplifications ought to be explored in future work. Shortcomings of the DSSAT-Canegro model include the simulated responses of phenological development, photosynthesis and respiration processes to high temperatures, and the disconnect between simulated biomass accumulation and expansive growth. Proposed methodology refinements should improve the reliability of predicted climate change impacts on sugarcane yield.

Climate change↗