nasa-gesdisc/es-publications-researchareas huggingface dataset:
A historical reconstruction of cropland in China from 1900 to 2016 (2021)

Abstract. A spatially explicit cropland distribution time-series dataset is the basis for the accurate assessment of biogeochemical processes in terrestrial ecosystems and their feedback to the climate system; however, this type of dataset is lacking in China. Existing cropland maps have a coarse resolution, are intermittently covered, or the data are inconsistent. We reconstructed a continuously covered cropland distribution dataset in China spanning from 1900 to 2016 by assimilating multiple data sources. In total, national cropland acreage expanded from 77.72 Mha in 1900 to a peak of 151.00 Mha in 1979, but it consistently decreased thereafter to 134.92 Mha in 2016. The cropland was primarily distributed in three historically cultivated plains in China: the Sichuan Plain, the Northern China Plain, and the Northeast China Plain. Cropland abandonment was approximately 43.12 Mha: it was mainly concentrated in the Northern China Plain and the Sichuan Plain and occurred during the 19902010 period. Cropland expansion was over 74.37 Mha: it was primarily found in the southeast, northern central, and northeast regions of China and occurred before 1950. In comparison, the national total and spatial distribution of cropland in the Food and Agriculture Organization (FAO) of the United Nations and the History Database of the Global Environment (HYDE) were distorted during the period from 1960 to 1980 due to the biased signal from the Chinese Agricultural Yearbook. We advocate that newly reconstructed cropland data, in which the bias has been corrected, should be used as the updated data for regional and global assessments, such as greenhouse gas emission accounting studies and food production simulations. The cropland dataset is available via an open-data repository (https://doi.org/10.6084/m9.figshare.13356680; Yu et al., 2020).

Research areas: Habitat Conversion/Fragmentation
Marginal more than mesic sites benefit from groundnut diversification of (2021)

Sustainable Intensification (SI) interventions are urgently required, particularly those suited to resource poor farms in Africa. Legume crops have been promoted as a key ingredient for SI of rainfed grain production on smallholder farms, with variable results. There is a need to explore the extent to which legume diversification of maize production impacts stability, nutrition, and income. This is particularly so for marginal environments. On-farm experimentation was conducted in Central Malawi over two to seven years on 29 farm sites (120 year-site combinations). The farms were located within four areas that included lakeshore, dissected hills (two locations) and upland plain. Maize diversification included a long-duration legume pigeonpea and a medium duration legume groundnut, grown in rotation or intercropped with maize, and as a doubled-up legume rotation (DLR). To quantify the performance of systems under low, medium, and high yield environments, we used long-term average maize yield to categorize each farm site. All legume diversified systems supported stable grain production in the low yield environment, as shown by 37-41% coefficient of variation for yield, in comparison to 62% for sole maize. The groundnut systems consistently produced the highest grain yield, protein, stable yields, and economic returns, and this performance held up in marginal, low yield environments. In this multi-site, multi-year, on-farm replicated study, the performance of groundnut systems (GnRot and DLR) stood out for high protein (0.529 T ha<SUP>-1</SUP> 2 yr<SUP>-1</SUP> and 0.615 T ha <SUP>-1</SUP> 2 yr<SUP>-1</SUP>, respectively over two years) versus unfertilized maize (0.169 T ha<SUP>-1</SUP> 2 yr<SUP>-1</SUP>). These two groundnut-based systems were produced with half-fertilizer rates compared to sole maize and were economically high performers. However, there was a barrier to adoption of GnRot and DLR in that improved groundnut seed was expensive (USD 157 ha<SUP>-1</SUP> 2 yr<SUP>-1</SUP>), this initial investment being beyond the means of many farmers, despite the cost largely offset by the generation of high income (USD 1636-1993 ha<SUP>-1</SUP> 2 yr<SUP>-1</SUP>). Long-term sustainability was assessed by monitoring soil organic carbon (SOC), which was found to be markedly influenced by soil texture (sites with SOC &gt;1.5% had sand content &lt;50%). Legume diversification effects on SOC were not discerned, possibly due to high sand content on the oldest trial sites. This study highlights the value of longitudinal data and including a wide range of soil texture sites in on-farm experimentation to identify overall legume diversification effects within maize systems.

Research areas: Agriculture
Rainfall anomalies are a significant driver of cropland expansion (2020)

Significance
            Rainfall anomalies are known to have deleterious impacts on agricultural yields, but the resulting consequences on cropland expansion remain uncertain. We study the differential scale of these impacts around the world. We find that repeated dry anomalies increase cropland expansion specifically in developing countries, which are characteristically dominated by small-holder farming, implying that cropland is expanded to compensate for lower yields. Two tests corroborate the results. First, comparable reductions in forest cover due to repeated dry anomalies are found in the same regions where cropland expands. Second, in places where infrastructure buffers yields from rainfall anomalies, cropland expansion halts. Understanding the synchronous challenges facing agriculture and the environment will be critical to inform appropriate policy interventions.
          , 
            Rainfall anomalies have long occupied center stage in policy discussions, and understanding their impacts on agricultural production has become more important as climate change intensifies. However, the global scale of rainfall-induced productivity shocks on changes in cropland is yet to be quantified. Here we identify how rainfall anomalies impact observed patterns of cropped areas at a global scale by leveraging locally determined unexpected variations in rainfall. Employing disaggregated panel data at the grid level, we find that repeated dry anomalies lead to an increase in cropland expansion in developing countries. No discernible effects are detected from repeated wet events. That these effects are confined to developing countries, which are often dominated by small-holder farmers, implies that they may be in response to reduced yields. The estimates suggest that overall, in developing countries, dry anomalies account for 9% of the rate of cropland expansion over the past two decades. We perform several tests to check for consistency and robustness of this relationship. First, using forest cover as an alternative measure, we find comparable reductions in forest cover in the same regions where cropland expands due to repeated dry anomalies. Second, we test the relationship in regions where yields are buffered from rainfall anomalies by irrigation infrastructure and find that the impact on cropland expansion is mitigated, providing further support for our results. Since cropland expansion is a significant driver of deforestation, these results have important implications for forest loss and environmental services.

Research areas: Habitat Conversion/Fragmentation
Crop yield estimation using multi-source satellite image series and deep learning (2020)

Timely monitoring of agricultural production and early yield predictions are essential for food security. Crop growth conditions and yield are related to climate variability and are impacted by extreme events. Remotely sensed time-series could be used to study the variability in crop growth and agricultural production. However, the choice of remotely sensed data and methods is still an issue, as different datasets have different spatiotemporal characteristics. Our primary goal was to test different algorithms and several remotely sensed time-series datasets for yield estimation in U.S. at county and field scale. For a county-level analysis, MODIS-based surface reflectance, Land Surface Temperature, and Evapotranspiration time series were used as input datasets. Field-level analysis was carried out using NASA's Harmonized Landsat Sentinel-2 (HLS) product. For this purpose, 3D convolutional neural network (CNN) and CNN followed by long-short term memory (LSTM) were used. For county-level analysis, the CNN-LSTM model had the highest accuracy, with a mean percentage error of 10.3% for maize and 9.6% for soybean. This model presented robust results for the year 2012, which is considered a drought year. In the case of field-level analysis, all models achieved accurate results with R2 exceeding 0.8 when data from mid growing season were used. The results highlight the potential of using satellite data for yield estimation at different management scales.

Research areas: Agriculture
Long-term prediction of climate change impact on the productivity of grain crops in Ukraine using satellite data (2017)

Aim. To analyze and predict the climate change impact on the crop structure, yield and gross collections of grain crops in short-term (2025), mid-term (2050) and long-term perspective. Methods. Analysis of long-term series of climatic parameters based on satellite data, climatic modeling, statistical analysis of crop yield and gross collection of grain crops. Results. The positive effect of historical and current climate change on grain crop yields in Ukraine is demonstrated. It is predicted that the preservation of this pattern and the implementation of an integrated system of measures for adapting agroecosystems to warming will promote further increase in the grain crop yield and thus its gross collection. Conclusions. According to the analysis of satellite data and climatic models, further climate warming is predicted and its positive impact on grain crop productivity is forecasted. In case of developing and implementing the measures to adapt agroecosystems to climate change, the grain yield in Ukraine
may increase by 25 % in 2025 compared with the current period (2015) and by 2930 % in 2050; the gross collection of grain crops will reach 75.0 million tons (in 2025) and 79.080.0 million tons (in 2050). On condition of effi cient material and technical, scientifi c and informational support, further development of technical means, the reproduction of soil fertility and the improvement of irrigation technologies in the long-term perspective (by 2100), the gross grain collection may reach 9295 million tons.

Research areas: Agriculture
A shift from cattle to camel and goat farming can sustain milk production with lower inputs and emissions in north sub-Saharan Africas drylands (2022)

Abstract
            
              Climate change is increasingly putting milk production from cattle-based dairy systems in north sub-Saharan Africa (NSSA) under stress, threatening livelihoods and food security. Here we combine livestock heat stress frequency, dry matter feed production and water accessibility data to understand where environmental changes in NSSAs drylands are jeopardizing cattle milk production. We show that environmental conditions worsened for 17% of the study area. Increasing goat and camel populations by 14% (7.7 million) and 10% (1.2 million), respectively, while reducing the dairy cattle population by 24% (5.9 million), could result in 0.14 Mt (+5.7%) higher milk production, lower water (1,683.6 million m
              3
              , 15.3%) and feed resource (404.3 Mt, 11.2%) demandand lower dairy emissions by 1,224.6 MtCO
              2
              e (7.9%). Shifting herd composition from cattle towards the inclusion of, or replacement with, goats and camels can secure milk production and support NSSAs dairy production resilience against climate change.

Research areas: Agriculture
A generalized model to predict large-scale crop yields integrating (2022)

Convenient and reliable large-scale crop yield prediction is needed when formulating administrative plans and ensuring food security, especially under changing climate and international conditions. In this study, we explored Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indices- and phenology-based yield prediction generalization model taking the US Corn Belt as an example. We calculated the normalized difference vegetation index (NDVI) and 2-band enhanced vegetation index (EVI2) time series, which were adjusted using greenup dates derived from the Land Cover Dynamics product MCD12Q2. Based on the adjusted VI (NDVI, EVI2) time series, the VI change rate (dVI) time series was calculated, which represents crop growth rate. The first step was to cluster the adjusted VI and dVI time series, called 'greenup groups', according to corresponding greenup dates with a five-day interval. Then in different greenup groups, we constructed empirical univariate models with VI having the maximum correlation with crop yield, and multivariate models with VI and dVI, which were also used to construct the generalized model. After clustering, the days with maximum VI correlation gradually decreased as greenup days increasing, and the univariate VI model and multivariate VI and dVI model performances in different groups improved. The generalized models with specific VI and dVI variables in each group predicted yields of corn and soybean with R<SUP>2</SUP> values mainly ranging from 0.55 to 0.75 and 0.55 to 0.70, while RMSE mainly ranging from 1000 to 1500 kg/ha and 300 to 400 kg/ha for both NDVI and EVI2 from 2008 to 2018 with leave-one-year-out cross-validation for all groups. The model using MODIS data was convenient and scalable with limited data requirements and date-determined variables after greenup, and offered a generalized method to predict crop yields at a large scale before harvest with good performance.

Research areas: Agriculture
A novel approach to the use of earth observation to estimate daily evaporation in a sugarcane plantation in Xinavane, Mozambique (2021)

Efficient irrigation water management for an 18,000 ha sugarcane plantation in Xinavane in southern Mozambique is a challenge. Sugarcane is an irrigation intensive crop and its productivity is sensitive to water stress. Options to adopt field water management best practices and proper irrigation scheduling are limited due to the lack of plot-level information on the actual crop water use and stress levels throughout the growing season. Due to heterogeneity in cropping calendar within the sugarcane plantation, at a certain point of time, different plots are at different growth stages. This makes scheme level irrigation scheduling complex and calls for frequent crop water use information. To fill this gap, this study presents a novel approach where a combination of satellite imagery with local weather data is used to provide daily evaporation rates. The PriestleyTaylor equation is applied to quantify evaporation (soil evaporation + transpiration) using radiation and temperature data from a meteorological station and spatial albedo estimates derived from the Sentinel-2 satellites. The results show 20 meter resolution maximum crop evaporation estimates can be derived with the proposed methodology. Additionally, the results show NDVI in the last two crop stages is able to distinguish between poor and good performing fields. Therefore, NDVI can be a useful index to estimate actual evaporation. First, the evaporation estimates were corrected for the crop stage using NDVI proxies and an additional stress indicator was used to calculate the actual evaporation flux spatially. The spatial evaporation estimates provide the water manager with information on actual crop water use and biomass development, which is relevant to both crop monitoring and irrigation management water management when drought-related stress is filtered.

Research areas: Habitat Conversion/Fragmentation
Ozone pollution will compromise efforts to increase global wheat (2018)

AbstractIntroduction of highperforming crop cultivars and crop/soil water management practices that increase the stomatal uptake of carbon dioxide and photosynthesis will be instrumental in realizing the United Nations Sustainable Development Goal (SDG) of achieving food security. To date, however, global assessments of how to increase crop yield have failed to consider the negative effects of tropospheric ozone, a gaseous pollutant that enters the leaf stomatal pores of plants along with carbon dioxide, and is increasing in concentration globally, particularly in rapidly developing countries. Earlier studies have simply estimated that the largest effects are in the areas with the highest ozone concentrations. Using a modelling method that accounts for the effects of soil moisture deficit and meteorological factors on the stomatal uptake of ozone, we show for the first time that ozone impacts on wheat yield are particularly large in humid rainfed and irrigated areas of major wheatproducing countries (e.g. United States, France, India, China and Russia). Averaged over 20102012, we estimate that ozone reduces wheat yields by a mean 9.9% in the northern hemisphere and 6.2% in the southern hemisphere, corresponding to some 85 Tg (million tonnes) of lost grain. Total production losses in developing countries receiving Official Development Assistance are 50% higher than those in developed countries, potentially reducing the possibility of achieving UN SDG2. Crucially, our analysis shows that ozone could reduce the potential yield benefits of increasing irrigation usage in response to climate change because added irrigation increases the uptake and subsequent negative effects of the pollutant. We show that mitigation of air pollution in a changing climate could play a vital role in achieving the abovementioned UN SDG, while also contributing to other SDGs related to human health and wellbeing, ecosystems and climate change.

Research areas: Agriculture
Coupling remote sensing and crop growth model to estimate national wheat yield in Ethiopia (2022)

Estimation of crop yield at a regional level is essential for making agricultural planning and addressing food security issues in Ethiopia. Remote sensing observations, particularly the leaf area index (LAI), have a strong relationship with crop yield. This study has proposed an approach to estimate wheat yield at field level and regional scale in Ethiopia by assimilating the retrieved MODIS time-series LAI data into the WOrld FOod STudies (WOFOST) model. To improve the estimation of crop yield in the region, the Ensemble Kalman Filter (EnKF) was used to incorporate the LAI into the WOFOST model. The estimation accuracy of wheat crop yield was validated using field-measured yields collected during the 2018 growing season. Our findings indicated that wheat yield was more precisely estimated by WOFOST (at water-limited mode) with EnKF algorithm (R2 = 0.80 and RMSE = 413 kg ha1) compared to that of without assimilating remotely sensed LAI (R2 = 0.58, RMSE = 592 kg ha1). These results demonstrated that assimilating MODIS-LAI into WOFOST has high potential and practicality to give a reference for wheat yield estimation. The findings from this study can provide information to policy, decision-makers, and other similar sectors to implement an appropriate and timely yield estimation measure.

Research areas: Agriculture
GCI30: a global dataset of 30 m cropping intensity using multisource remote sensing imagery (2021)

Abstract. The global distribution of cropping intensity (CI) is essential to our understanding of agricultural land use management on Earth. Optical remote sensing has revolutionized our ability to map CI over large areas in a repeated and cost-efficient manner. Previous studies have mainly focused on investigating the spatiotemporal patterns of CI ranging from regions to the entire globe with the use of coarse-resolution data, which are inadequate for characterizing farming practices within heterogeneous landscapes. To fill this knowledge gap, in this study, we utilized multiple satellite data to develop a global, spatially continuous CI map dataset at 30 m resolution (GCI30). Accuracy assessments indicated that GCI30 exhibited high agreement with visually interpreted validation samples and in situ observations from the PhenoCam network. We carried out both statistical and spatial comparisons of GCI30 with six existing global CI estimates. Based on GCI30, we estimated that the global average annual CI during 20162018 was 1.05, which is close to the mean (1.09) and median (1.07) CI values of the existing six global CI estimates, although the spatial resolution and temporal coverage vary significantly among products. A spatial comparison with two satellite-based land surface phenology products further suggested that GCI30 was not only capable of capturing the overall pattern of global CI but also provided many spatial details. GCI30 indicated that single cropping was the primary agricultural system on Earth, accounting for 81.57 % (12.28106 km2) of the world's cropland extent. Multiple-cropping systems, on the other hand, were commonly observed in South America and Asia. We found large variations across countries and agroecological zones, reflecting the joint control of natural and anthropogenic drivers on regulating cropping practices. As the first global-coverage, fine-resolution CI product, GCI30 is expected to fill the data gap for promoting sustainable agriculture by depicting worldwide diversity of agricultural land use intensity. The GCI30 dataset is available on Harvard Dataverse: https://doi.org/10.7910/DVN/86M4PO (Zhang et al., 2020).

Research areas: Habitat Conversion/Fragmentation, Agriculture
Mid-season empirical cotton yield forecasts at fine resolutions using large yield mapping datasets and diverse spatial covariates (2020)

Forecasts of crop yield are an important tool for a variety of stakeholders, but most studies produce large-scale, late-season yield forecasts that are not appropriate for farmers. Farmers require forecasts of crop yield mid-season and at fine spatial resolutions to guide site-specific adaptive crop management practices. This study created empirical random forest models to forecast cotton yield mid-season at three different spatial resolutions: 30 m, field, and farm aggregation. Large yield mapping datasets from 14 different seasons were utilised to build the model across 68 different fields from six large cotton farms in eastern Australia. A generic conceptual framework is proposed to empirically model crop yield. This topsaw framework represents the spatial and temporal factors that drive yield, including topography (t), organisms (o), plant measurements (p), soil (s), agronomy/management (a), and weather (w). In this study, the specific predictor variables to represent these driving factors of yield included elevation (t), Normalised Difference Vegetation Index, Enhanced Vegetation Index, MODIS Evapotranspiration (p), gamma radiometrics grid, clay content digital soil map (s), rainfall data, and growing day degrees (w). Forecasts of cotton yield increased in accuracy as the spatial resolution of predictions became coarser. Using a leave-one-year-out cross-validation, cotton yield could be forecasted to an accuracy of 0.42 Lin's Concordance Correlation Coefficient (LCCC) and Root Mean Square Error (RMSE) of 2.11 bales ha1 at 30 m resolution. This improved to a 0.63 LCCC and 1.72 bales ha1 RMSE at the field resolution, and a 0.65 LCCC and 0.77 bales ha1 at the aggregation-scale. As the developed approach solely relied on publicly available predictor variables, there is an opportunity to apply this to any cotton field in Australia, and over a much larger area. Overall, this study is an important step in building an operational approach to forecast mid-season cotton yield at fine spatial resolutions, and has the potential to improve production, input use efficiency, and profitability for individual growers and the Australian Cotton Industry as a whole.

Research areas: Agriculture