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All sourcesNASA NTRSDOE OSTI
NASA NTRS2020

Agriculture's Historic Twin-Challenge Toward Sustainable Water Use and Food Supply for All

A sustainable and just future, envisioned by the UN's 2030 Agenda for Sustainable Development, puts agricultural systems under a heavy strain. The century-old quandary to provide ever-growing human populations with sufficient food takes on a new dimension with the recognition of environmental limits for agricultural resource use. To highlight challenges and opportunities toward sustainable food security in the twenty first century, this perspective paper provides a historical account of the escalating pressures on agriculture and freshwater resources alike, supported by new quantitative estimates of the ascent of excessive human water use. As the transformation of global farming into sustainable forms is unattainable without a revolution in agricultural water use, water saving and food production potentials are put into perspective with targets outlined by the Sustainable Development Goals (SDGs). The literature body and here-confirmed global estimates of untapped opportunities in farm water management indicate that these measures could sustainably intensify today's farming systems at scale. While rigorous implementation of sustainable water withdrawals (SDG 6.4) might impinge upon 5% of global food production, scaling-up water interventions in rainfed and irrigated systems could over-compensate such losses and further increase global production by 30% compared to the current situation (SDG 2.3). Without relying on future technological fixes, traditional on-farm water and soil management provides key strategies associated with important synergies that needs better integration into agro-ecological landscape approaches. Integrated strategies for sustainable intensification of agriculture within planetary boundaries are a potential way to attain several SDGs, but they are not yet receiving attention from high-level development policies.

sustainable development goals↗
NASA NTRSDate not supplied

HydroSAR: A Cloud-based SAR Data Analysis Service to Monitor Hydrological Disasters and their Impact on Population and Agriculture

Weather-related hazards are ubiquitous around the world including: 1) hurricane storm surges, 2) rapid snowmelt and heavy rainfall, 3) severe weather leading to flash floods, and 4) seasonal freeze and thaw of rivers that may lead to ice jams. Each of these hazards affects human settlements and has the potential to impact agricultural productivity. In each setting, end-users in disaster management need access to data processing tools helpful in mapping past and current disasters. Analysis of past events supports risk mitigation by understanding what has already occurred and how to alleviate those impacts in the future. Having capabilities to generate the same products in a response setting means that lessons learned from risk analysis will carry forward to event response. Synthetic aperture radar (SAR) data are particularly useful for these activities due to their all-weather 24/7 monitoring capabilities. In this effort we present HydroSAR, a cloud-based SAR data analysis service for the mapping of meteorological and hydrological disasters as well as their impact on population and agriculture. As part of this project we have developed a series of SAR-based value added products for the monitoring of surface hydrology (image time series, change detection, flood extent, flood depth) and the assessment of impacts on population (flood depth) and agriculture (active agriculture, inundated agriculture, flood duration). We also developed a cloud-based platform for generating these products over affected areas and are working with end-users to integrate derived product into decision-making workflows The paper will briefly introduce the SAR-based products that were developed for this effort. We describe the cloud-based production pipeline that was built to automatically generate these products in near-real time over extended regions. The integration of SAR-based information into hazard preparation and response activities is described for a number of recent disasters including the 2019 forest fires in Alaska, 2019 flooding in the U.S. Midwest, the 2020 U.S. severe weather easter outbreak, 2020 tropical storm Christobal, 2020 cyclone Amphan, 2020, Alaska Spring breakup flooding, and the 2020 flood season in Eastern India, Bangladesh, and Nepal.

Franz Josef Meyer↗
NASA NTRS2021

Bhutan Agriculture: Developing a Crop Mask for Rice and Creating a Data Collection Protocol Utilizing Remotely Sensed Data in Bhutan

Rice cultivation in Bhutan has been increasingly threatened by deteriorating soil health and outbreaks of diseases and pests associated with the global change in climate patterns. Field surveys, which the national government of Bhutan has relied on to monitor remote agricultural lands, are becoming increasingly overwhelmed by growing threats to agricultural health. To address these concerns, NASA DEVELOP partnered with the Department of Agriculture of Bhutan, the Bhutan Foundation, and the Ugyen Wangchuck Institute of Conservation and Environmental Research (UWICER) and worked to increase the government of Bhutan’s agricultural monitoring capacity. Utilizing Earth observations including Landsat 8 Operational Land Imager (OLI), Sentinel-1 C-band Synthetic Aperture Radar (C-SAR), Shuttle Radar Topography Mission (SRTM), and Planet imagery, the DEVELOP team worked with NASA SERVIR and created a sampling protocol to identify rice plantations and supplement field surveys for more efficient agriculture monitoring. The analysis focused on districts Paro, Punakha, Samtse, Sarpang, Trongsa, Zhemgang, Wangdue Phodrang, and Samdrup Jongkhar in the year 2020 during the period of transplantation (June) to harvesting of rice (November). The team provided the partners with a sampling protocol for integrating NASA Earth observations into their crop monitoring methods, as well as a crop mask for rice identification and to aid crop management. The crop mask for rice was developed using the Random Forest (RF) classifier for the eight districts of Bhutan. Visually, the random forest model has proved to be more accurate and precise than the classification and Regression Tree model. Statistically, the Random Forest model was 91.8% accurate in identifying rice in Bhutan.

Yeshey Seldon↗
DOE OSTI2023

Ecologically-Informed Precision Conservation: A framework for increasing biodiversity in intensively managed agricultural landscapes with minimal sacrifice in crop production

Conservation actions are urgently needed to tackle biodiversity loss in intensively managed agricultural landscapes. Production lands are usually heterogeneous and contain low-yield areas that can be set aside for biodiversity conservation without serious yield losses. Here, we introduce Ecologically-Informed Precision Conservation, a framework that integrates yield mapping and ecological theory to select the best areas to create new set-asides while ensuring high crop yields at the farm/landscape level. Long-term yield maps can be generated using globally available satellite data and basic information on field/farm crop yield from farmers. Ecological principles are then used to select the subset of areas with the highest potential for biodiversity conservation by prioritising those that increase connectivity, maximise habitat heterogeneity and decrease landscape grain size. Here, the created non-crop habitats can be permanent and thus ensure biodiversity support over time. In addition, agricultural management efficiency can be enhanced by improving field shapes. The framework provides the basis for a practical, user-friendly tool that informs all interested stakeholders on how to rationalise existing agricultural landscapes using already-existing farming systems and available technologies. High cost-effectiveness from an economic and conservation perspective, along with the creation of heterogeneous non-crop habitats, make our framework a promising solution to re-design agricultural landscapes.

Precision agriculture↗
DOE OSTI2024

Modeling the Effects of Artificial Drainage on Agriculture-Dominated Watersheds Using a Fully Distributed Integrated Hydrology Model

In agriculture-dominated watersheds where natural drainage is poor, agricultural ditches (narrow engineered channels) and tile drains (perforated pipes) are widely employed to enhance surface and subsurface drainage, respectively. Despite their relatively small scale, these features exert substantial control over the hydro-biogeochemical function of watersheds and their effects need to be represented in the models. We introduce a novel strategy to incorporate the effects of artificial agricultural drainage into a fully distributed basin-scale integrated surface-subsurface hydrology models. In our approach, narrow agriculture ditches for surface drainage are resolved efficiently using ditch-aligned computational meshes that are hydrologically conditioned to ensure connectivity in the stream/ditch network. For tile drainage in the subsurface, we use the physically based Hooghoudt's drainage equation as a subgrid model and route the water drained through tiles to the nearest ditch. Without site-specific calibration, this model reproduced observed streamflow in the Portage River Watershed (>1,000 km 2 ) as recorded by a USGS gauge with good accuracy (normalized KGE = 0.81) and outperformed a calibrated SWAT model (normalized KGE = 0.68). Numerical experiments confirm that artificial drainage reduces surface inundations and effectively controls the water table. At the watershed scale, artificial drainage increases baseflow but has little effect on watershed discharges above the 90th percentile. The strong physical underpinnings and reduced need for calibration allow us to study the impacts of artificial drainage on distributed hydrological response in terms of fluxes and states and provide a platform for investigating watershed-scale nutrient transport.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Influence of Agricultural Managed Aquifer Recharge (AgMAR) and Stratigraphic Heterogeneities on Nitrate Reduction in the Deep Subsurface

This data package contains input files for TOUGHREACT for a modeling study examining the effects of managed aquifer recharge on agricultural lands on nitrate cycling and transport in the Central Valley of CA near Modesto. The files contain all the geochemical species, reactions, and hydrological parameters for the model. The files are text files used for the TOUGH family of code created by LBNL. To use the files a license is required. https://tough.lbl.gov/licensing-download/toughreact-licensing-download/Accompanying Paper Abstract: Agricultural managed aquifer recharge (AgMAR) is a proposed management strategy whereby surface water flows are used to intentionally flood croplands with the purpose of recharging underlying aquifers. However, legacy nitrate (NO3-) contamination in agriculturally-intensive regions poses a threat to groundwater resources under AgMAR. To address these concerns, we use a reactive transport modeling framework to better understand the effects of AgMAR management strategies (i.e., by varying the frequency, duration between flooding events, and amount of water) on N leaching to groundwater under different stratigraphic configurations and antecedent moisture conditions. In particular, we examine the potential of denitrification and nitrogen retention in deep vadose zone sediments (~15 m) using variable AgMAR application rates on two-dimensional representations of differently textured soils, soils with discontinuous bands/channels, and soils with preferential flow paths characteristic of typical agricultural field sites. Our results indicate that finer textured sediments, such as silt loams, alone or embedded within high flow regions, are important reducing zones providing conditions needed for denitrification. Simulation results further suggest that applying water all-at-once rather than in increments for a fixed volume of recharge transports higher concentrations of NO3- deeper into the profile, which has the potential to exacerbate groundwater quality. This transport into deeper depths can be aggravated by wetter antecedent soil moisture conditions. However, applying water all-at-once also increases denitrification within the vadose zone by promoting anoxic conditions. We conclude that AgMAR management strategies can be designed to enhance denitrification in the subsurface and reduce N leaching to groundwater, while specifically accounting for lithologic heterogeneity, antecedent soil moisture conditions, and depth to the water table. Our findings are potentially relevant to other systems that experience flooding inundation such as riparian corridors, floodplains, wetlands, and other managed landscapes like dedicated recharge basins.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Luminescent Enhancement for Combined Solar and Commercial Agriculture. Final Report

This proposal will develop a new racking / mounting system combined with a new agriculture-tuned luminescent solar panel for low cost high tunnel implementation to produce both crops and electricity for small local farms. Crop trials performed under this specialized panel/racking system can be extended to open field agriculture with standard utility scale racking system in phase II. The US agricultural industry uses about 300 billion KW-hr of energy / year. Of this energy consumption, roughly 20% goes towards food production and 20% goes towards food processing. This proposal will allow farm land to simultaneously generate electricity, food AND provide jobs. Employment is one of the largest hurdles for the introduction of solar into rural areas. The ability to provide agricultural jobs in addition to solar energy from a plot of land will pave the way for greater adoption of photovoltaics in rural areas. A cost effective and highly durable hybrid power generating high tunnel greenhouse will be developed that generates electricity at less than $2.50/watt INSTALLED cost and can be used for crop production of high values crops. The high tunnel will use a new design of Soliculture greenhouse integrated luminescent solar panels to selectively absorb the green portion of the solar spectrum that is not used by plants and convert the light to red light with higher photosynthetic efficiency and to enhance power output of solar cells. Green light is selectively downshifted to red light using luminescent materials that match the photosythetically active region for plants and the efficiency maximum of Si photovoltaic cells. The color tuning associated with converting green light to red light has been shown to facilitate fruit production in commercial greenhouses growing cucumbers, lettuce and herbs.

14 SOLAR ENERGY↗
DOE OSTI2022

Scaling Sustainable Biochar Research & Commercialization for Agriculture & Conservation: A Summary from a Stakeholder Convening

Biochar is a carbon-rich substance with the potential to increase soil carbon sequestration, reduce greenhouse gas (GHG) emissions and improve soil health. When used as part of soil health management systems, biochar can provide multiple synergistic benefits for the agricultural sector and society, while also supporting the production of renewable, climate-neutral biofuels. Establishing a pyrolysis biochar bioenergy industry (PBBI) is necessary to harness the environmental benefits of biochar. To galvanize the necessary coordination to support a PBBI, the Foundation for Food & Agriculture Research (FFAR), the National Center for Appropriate Technology (NCAT), and American Farmland Trust (AFT) co-hosted a virtual convening event on biochar research and commercialization in March 2022. Participants represented a diverse set of stakeholders, including biochar producers, agricultural producers, and members of nonprofit, industry and government agencies. This whitepaper offers a summary of the convening, as well as insights from subsequent stakeholder engagement. Sustainably sourced, fit-for-purpose biochar can be a powerful tool in the soil health management systems and climate-smart toolbox, with decades of research supporting the soil, environment, production, and climate benefits. However, because biochar represents a range of feedstocks, processes and products, there is a critical need to: • Coordinate efforts to characterize and test the application of biochar types across locations with diverse soil and management conditions • Address research and decision-support gaps • Support the growing industry These objectives can be addressed or advanced through a coordinated strategy. To support establishment of this strategy, the convening participants identified key gaps in implementation and research. The experiences of foresters, ranchers, and farmers already using biochar, some of whom participated in the convening, helped inform priorities. Key gaps include: • Practical, applied on-farm research • Supporting management for agricultural productivity • Maximum benefits for conservation • Decision support • Life Cycle Assessment (LCA) • Quantification of climate mitigation potential Existing research gaps include: • Potential synergies among biochar, soil organic matter, and enhanced rock weathering • Development of new storage reservoirs for biochar in industrial products Convening participants further provided next steps for rapidly developing a PBBI to produce sustainable, fit-for-purpose biochars that increase soil health and mitigate climate change. Participants also stressed the need for actionable, coordinated, large-scale research relevant to commercial production in the next five years. To have a chance to reduce greenhouse gas emissions, we need an array of tools that would aid in mitigation; a coordinated strategy to enable biochar to be an effective tool in the climate-smart toolbox must be created; We cannot wait 50 years.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

A First Investigation of Agriculture Sector Perspectives on the Opportunities and Barriers for Agrivoltaics

Agrivoltaic systems are a strategic and innovative approach to combine solar photovoltaic (PV)-based renewable energy generation with agricultural production. Recognizing the fundamental importance of farmer adoption in the successful diffusion of the agrivoltaic innovation, this study investigates agriculture sector experts’ perceptions on the opportunities and barriers to dual land-use systems. Using in-depth, semistructured interviews, this study conducts a first study to identify challenges to farmer adoption of agrivoltaics and address them by responding to societal concerns. Results indicate that participants see potential benefits for themselves in combined solar and agriculture technology. The identified barriers to adoption of agrivoltaics, however, include: (i) desired certainty of long-term land productivity, (ii) market potential, (iii) just compensation and (iv) a need for predesigned system flexibility to accommodate different scales, types of operations, and changing farming practices. The identified concerns in this study can be used to refine the technology to increase adoption among farmers and to translate the potential of agrivoltaics to address the competition for land between solar PV and agriculture into changes in solar siting, farming practice, and land-use decision-making.

14 SOLAR ENERGY↗
NASA NTRS2014

Climate Forcing Datasets for Agricultural Modeling: Merged Products for Gap-Filling and Historical Climate Series Estimation

The AgMERRA and AgCFSR climate forcing datasets provide daily, high-resolution, continuous, meteorological series over the 1980-2010 period designed for applications examining the agricultural impacts of climate variability and climate change. These datasets combine daily resolution data from retrospective analyses (the Modern-Era Retrospective Analysis for Research and Applications, MERRA, and the Climate Forecast System Reanalysis, CFSR) with in situ and remotely-sensed observational datasets for temperature, precipitation, and solar radiation, leading to substantial reductions in bias in comparison to a network of 2324 agricultural-region stations from the Hadley Integrated Surface Dataset (HadISD). Results compare favorably against the original reanalyses as well as the leading climate forcing datasets (Princeton, WFD, WFD-EI, and GRASP), and AgMERRA distinguishes itself with substantially improved representation of daily precipitation distributions and extreme events owing to its use of the MERRA-Land dataset. These datasets also peg relative humidity to the maximum temperature time of day, allowing for more accurate representation of the diurnal cycle of near-surface moisture in agricultural models. AgMERRA and AgCFSR enable a number of ongoing investigations in the Agricultural Model Intercomparison and Improvement Project (AgMIP) and related research networks, and may be used to fill gaps in historical observations as well as a basis for the generation of future climate scenarios.

Climate Forcing Data↗
NASA NTRS2015

A Climate Generator for Agricultural Planning in Southeastern South America

A method is described for the generation of climate scenarios in a form suitable for driving agricultural models. The scenarios are tailored to the region in southeastern South America bounded by 25-40 deg S, 45-65 deg W, denoted here as SESA. SESA has been characterized by increasing summer precipitation, particularly during the late 20th century, which, in the context of favorable market conditions, has enabled increases in agricultural production. Since about year 2000, however, the upward tendency appears to have slowed or possibly stopped, raising questions about future climate inputs to regional agricultural yields. The method is not predictive in the deterministic sense, but rather attempts to characterize uncertainty in near-term future climate, taking into account both forced trends and unforced, natural climate fluctuations. It differs from typical downscaling methods in that GCM information is utilized only at the regional scale, subregional variability being modeled based on the observational record. Output, generated on the monthly time scale, is disaggregated to daily values with a weather generator and used to drive soybean yields in the crop model DSSAT-CSM, for which preliminary results are discussed. The simulations produced permit assessment of the interplay between long-range trends and near-term climate variability in terms of agricultural production.

climate simulation↗
NASA NTRS2015

The Agricultural Model Intercomparison and Improvement Project: Phase I Activities by a Global Community of Science

The Agricultural Model Intercomparison and Improvement Project (AgMIP) was founded in 2010. Its mission is to improve substantially the characterization of world food security as affected by climate variability and change, and to enhance adaptation capacity in both developing and developed countries. The objectives of AgMIP are to: Incorporate state-of-the-art climate, crop/livestock, and agricultural economic model improvements into coordinated multi-model regional and global assessments of future climate impacts and adaptation and other key aspects of the food system. Utilize multiple models, scenarios, locations, crops/livestock, and participants to explore uncertainty and the impact of data and methodological choices. Collaborate with regional experts in agronomy, animal sciences, economics, and climate to build a strong basis for model applications, addressing key climate related questions and sustainable intensification farming systems. Improve scientific and adaptive capacity in modeling for major agricultural regions in the developing and developed world, with a focus on vulnerable regions. Improve agricultural data and enhance data-sharing based on their intercomparison and evaluation using best scientific practices. Develop modeling frameworks to identify and evaluate promising adaptation technologies and policies and to prioritize strategies.

farm crops↗
NASA NTRS2015

Introduction The Role of the Agricultural Model Intercomparison and Improvement Project

Climate impacts on agriculture are of increasing concern in both the scientific and policy communities because of the need to ensure food security for a growing population. A special challenge is posed by the changes in the frequency and intensity of heat-waves, droughts, and episodic rainstorms already underway in many parts of the world. Changes in production are directly linked to such variations in temperature and precipitation during the growing season, and often to offseason changes in weather affecting soil-water storage and availability to crops. This is not an isolated problem but one of both global and regional importance, because of impacts on the livelihoods of smallholder farmers as well as consequences for the world food trade system. This two-part set the Agricultural Model Intercomparison and Improvement Project (AgMIP): Integrated Crop and Economic Assessments is the first to be entirely devoted to AgMIP (www.agmip.org). AgMIP is a major international research program focused on climate change and agriculture. The goal of the two parts is to advance the field by providing detailed information on new simulation techniques and assessments being conducted by this program. It presents information about new methods of global and regional integrated assessment, results from agricultural regions, and adaptation strategies for maintaining food security under changing climate conditions.

security↗
NASA NTRS2016

Drought Impacts on Agricultural Production and Land Fallowing in California's Central Valley in 2015

The ongoing drought in California substantially reduced surface water supplies for millions of acres of irrigated farmland in California's Central Valley. Rapid assessment of drought impacts on agricultural production can aid water managers in assessing mitigation options, and guide decision making with respect to mitigation of drought impacts. Satellite remote sensing offers an efficient way to provide quantitative assessments of drought impacts on agricultural production and increases in fallow acreage associated with reductions in water supply. A key advantage of satellite-based assessments is that they can provide a measure of land fallowing that is consistent across both space and time. We describe an approach for monthly and seasonal mapping of uncultivated agricultural acreage developed as part of a joint effort by USGS, USDA, NASA, and the California Department of Water Resources to provide timely assessments of land fallowing during drought events. This effort has used the Central Valley of California as a pilot region for development and testing of an operational approach. To provide quantitative measures of uncultivated agricultural acreage from satellite data early in the season, we developed a decision tree algorithm and applied it to time-series data from Landsat TM (Thematic Mapper), ETM+ (Enhanced Thematic Mapper Plus), OLI (Operational Land Imager), and MODIS (Moderate Resolution Imaging Spectroradiometer). Our effort has been focused on development of indicators of drought impacts in the March-August timeframe based on measures of crop development patterns relative to a reference period with average or above average rainfall. To assess the accuracy of the algorithms, monthly ground validation surveys were conducted across 650 fields from March-September in 2014 and 2015. We present the algorithm along with updated results from the accuracy assessment, and data and maps of land fallowing in the Central Valley in 2015.

Valleys↗
NASA NTRS2016

Simulating US Agriculture in a Modern Dust Bowl Drought

Drought-induced agricultural loss is one of the most costly impacts of extreme weather, and without mitigation, climate change is likely to increase the severity and frequency of future droughts. The Dust Bowl of the 1930s was the driest and hottest for agriculture in modern US history. Improvements in farming practices have increased productivity, but yields today are still tightly linked to climate variation and the impacts of a 1930s-type drought on current and future agricultural systems remain unclear. Simulations of biophysical process and empirical models suggest that Dust-Bowl-type droughts today would have unprecedented consequences, with yield losses approx.50% larger than the severe drought of 2012. Damages at these extremes are highly sensitive to temperature, worsening by approx.25% with each degree centigrade of warming. We find that high temperatures can be more damaging than rainfall deficit, and, without adaptation, warmer mid-century temperatures with even average precipitation could lead to maize losses equivalent to the Dust Bowl drought. Warmer temperatures alongside consecutive droughts could make up to 85% of rain-fed maize at risk of changes that may persist for decades. Understanding the interactions of weather extremes and a changing agricultural system is therefore critical to effectively respond to, and minimize, the impacts of the next extreme drought event.

climate change impacts↗
NASA NTRS2022

Potential impacts of climate change on agriculture and fisheries production in 72 tropical coastal communities

Climate change is expected to profoundly affect key food production sectors, including fisheries and agriculture. However, the potential impacts of climate change on these sectors are rarely considered jointly, especially below national scales, which can mask substantial variability in how communities will be affected. Here, we combine socioeconomic surveys of 3,008 households and intersectoral multi-model simulation outputs to conduct a sub-national analysis of the potential impacts of climate change on fisheries and agriculture in 72 coastal communities across five Indo-Pacific countries (Indonesia, Madagascar, Papua New Guinea, Philippines, and Tanzania). Our study reveals three key findings: First, overall potential losses to fisheries are higher than potential losses to agriculture. Second, while most locations (> 2/3) will experience potential losses to both fisheries and agriculture simultaneously, climate change mitigation could reduce the proportion of places facing that double burden. Third, potential impacts are more likely in communities with lower socioeconomic status.

Climate change↗
NASA NTRS2023

Future Climate Change Impacts on U.S. Agricultural Yields, Production, and Market

This study provides estimates of climate change impacts on U.S. agricultural yields and the agricultural economy through the end of the 21st century, utilizing multiple climate scenarios. Results from a process-based crop model project future increases in wheat, grassland, and soybean yield due to climate change and atmospheric CO2 change; corn and sorghum show more muted responses. Results using yields from econometric models show less positive results. Both the econometric and process-based models tend to show more positive yields by the end of the century than several other similar studies. Using the process-based model to provide future yield estimates to an integrated agricultural sector model, the welfare gain is roughly $16B/year (2019 USD) for domestic producers and $6.2B/year for international trade, but domestic consumers lose $10.6B/year, resulting in a total welfare gain of $11.7B/year. When yield projections for major crops are drawn instead from econometric models, total welfare losses of more than $28B/year arise. Simulations using the process-based model as input to the agricultural sector model show large future production increases for soybean, wheat, and sorghum and large price reductions for corn and wheat. The most important factors are those about economic growth, flooding, international trade, and the type of yield model used. Somewhat less, but not insignificant factors include adaptation, livestock productivity, and damages from surface ozone, waterlogging, and pests and diseases.

Agricultural sector↗
NASA NTRS2023

New York Ecological Conservation: Evaluating Agricultural Conservation Easement Impact Using Earth Observations to Examine Avoided Soil Carbon Loss to Development

Farmland provides ecosystems and communities with services ranging from habitat conservation to food security. As total U.S. farmland continues to decline, agricultural lands near urban areas are especially vulnerable. Our project partners—Finger Lakes Land Trust, Genesee Land Trust, and Saratoga Preserving Land and Nature (PLAN)—can use study results to better profile farmland vulnerability, issuing conservation easements to protect maximum acreage in Saratoga County and the Finger Lakes Region of New York. Multiple existing studies effectively use remote sensing imagery to analyze historical land cover and forecast future change. This study examined soil carbon stocks and land cover change to estimate avoided soil carbon loss. We also predicted farmland vulnerability. We completed these analyses using European Space Agency (ESA) and NASA Earth observations that include Landsat 5 Thematic Mapper (TM), Landsat 8 Operational Land Imager (OLI), Landsat 9 OLI-2, Sentinel-2 Multispectral Instrument (MSI), and Suomi National Polar-Orbiting Partnership (NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) for nighttime lights data that aided in the land change model analysis. We determined that the conversion of agriculture to development from 1990 to 2022 occurred at rates of 0.91% (Finger Lakes) and 7% (Saratoga). Urban development is predicted to increase surrounding urban centers through 2030 and 2050. We also estimate that between 26.5 and 348,101 kilotons (Finger Lakes) and 3.7 and 58,006 kilotons (Saratoga) of soil carbon losses have been avoided through agricultural easements. Findings from this study will support our partners in determining agricultural conservation easement benefits and prioritizing the acquisition of future easement sites.

Agriculture↗