nasa-gesdisc/es-publications-researchareas huggingface dataset:
Relationship of population migration, crop production pattern, and (2021)

Abstract
            Global crop production and population distributions have undergone great changes under climate change and socioeconomic development, and have drawn considerable public attention. How to explain the similarity of the migration patterns of crop yield and population density for different countries/regions is still uncertain and worth studying. Here, we estimated the similarity between migrations of main crop caloric yield (i.e. maize, rice, wheat, and soybean) and population density using Frechet distance, and investigated the regression relationship between Frechet distance and related climatic and socioeconomic variables for countries/regions with different economic development stages. The results indicated that different countries/regions showed different Frechet distances during 20002015, with a maximum value of 24.44 for Russia and a minimum value of 0.11 for Georgia. For countries/regions with different economic development stages, the built regression models can explain 39%93% of the variability in the Frechet distance. Log(land area), log(GDP), and log(land area under cereal production) were always included in regression models and had higher importance in explaining the variability of Frechet distance. For the model for all countries/regions, both the log(land area) and log(GDP per capita) may positively link to the Frechet distance. Possible reasons for these results are that countries/regions with high GDP (or GDP per capita) may ease the conflict of land resources between humans and crops to achieve agricultural industrialization, which causes the far connection of the migrations for crop caloric yield and population density. The complicated interactions of crop production, population dynamic, and socioeconomic development should be given greater attention in the future.

Research areas: Agriculture
Precipitation drivers of cropping frequency in the Brazilian Cerrado: Evidence and implications for decision-making (2017)

Abstract The Amazon basin has been subjected to unprecedented rates of land-use change over the past several decades, primarily as a result of the expansion of agriculture. Enhanced rain forest conservation efforts toward the end of the twentieth century slowed deforestation of the Amazon but, in turn, increased demand for land repurposing in the adjacent Cerrado (savanna) region, where conservation regulations are less strict. To maintain or increase yields while minimizing the need for additional land, agricultural producers adopted a form of intensification in which two rain-fed crops are planted within a single growing season (double cropping). Using 10 years (August 2002 to July 2012) of MODIS and TRMM data, it is demonstrated that there exists a threshold growing season rainfall amount (1759 mm) for double cropping. But more nuanced is the relationship between observable precipitation information available to farmers at the time of planting decision and the choice to double crop in a given year. An evaluation of decision-available precipitation characteristics provides strong evidence for the importance of high rainfall frequency during a critical period prior to, and including, the rainy season onset.

Research areas: Habitat Conversion/Fragmentation, Agriculture
Deep transfer learning for crop yield prediction with remote sensing data (2018)

Accurate prediction of crop yields in developing countries in advance of harvest time is central to preventing famine, improving food security, and sustainable development of agriculture. Existing techniques are expensive and difficult to scale as they require locally collected survey data. Approaches utilizing remotely sensed data, such as satellite imagery, potentially provide a cheap, equally effective alternative. Our work shows promising results in predicting soybean crop yields in Argentina using deep learning techniques. We also achieve satisfactory results with a transfer learning approach to predict Brazil soybean harvests with a smaller amount of data. The motivation for transfer learning is that the success of deep learning models is largely dependent on abundant ground truth training data. Successful crop yield prediction with deep learning in regions with little training data relies on the ability to fine-tune pre-trained models.

Research areas: Agriculture
Soybean yield components at different densities and planting seasons in Paraguay (2021)

The aim of this research was to evaluate the performance of soybean cultivars using different population densities and planting seasons. The experiments were established using a completely randomized block design with a 1432 factorial arrangement, where factor A consisted of 14 soybean cultivars, factor B were low (177,700 plants ha-1), medium (266,600 plants ha-1), and high (355,500 plants ha-1) population densities, and factor C consisted of early and late planting seasons. We evaluated the number of pods per plant (NPP), number of grains m-2 (NG), 1000-grain weight (TGW), and yield (kg ha-1). The interaction between cultivar and planting season affected the NG, TGW, and yield. Cultivars DM-6563-IPRO, TMG-7062-IPRO, 6505-B, NA-5909-RG, M-6410-IPRO, DM-6262-IPRO, SOJAPAR-R19, 6806-IPRO, 6205-B, M-5947-IPRO and SYN-1163-RR showed higher yields in the early planting season and cultivar NS-5959-IPRO in the late planting season. Cultivars 5907-IPRO and DM-5958 showed similar yields for the two planting seasons evaluated. The highest yields were obtained from a density of 266,600 plants ha-1. The cultivarplanting season interaction affected the TGW, with the early planting season showing a greater TGW for most of the cultivars evaluated. The NPP depended on the interaction between cultivar, density, and planting season. The combination of the NG and the TGW showed a more significant influence on the generation of yield in the cultivars. This study highlights the importance of selecting genotypes according to their response to variations in planting date and plant density. This information could help Paraguayan farmers to maximize production in the same area, optimizing the available resources.

Research areas: Agriculture
Regional estimation of garlic yield using crop, satellite and climate data in Mexico (2021)

Garlic (Allivium sativum L.) has been consumed by humans since the beginning of recorded history. Despite its culinary and economical importance, little efforts are reported in the literature to forecast garlic yield. In this study, we developed the first attempt to predict garlic yield at large scale using crop, satellite (TERRA-MODIS) and climate data (ERA-5). The study area was located in Mexico (2004  2018). We compared the predictive capacity of three Machine Learning (ML) methods: generelised linear model (glm), support vector machine radial (svmR) and random forest (rf); as well as different combinations of predictors. The best performance was obtained by the svmR model (R2 = 0.68 and %RMSE = 20.08%) which combined YieldBaseLine, NDVI and climate data under Feature Selection scenario (FS) = 0.90. The rf method performed similarly well (R2 = 0.66), while glm achieved lower performance (R2 = 0.61). To test the predictive capacity of the models in practice, we additionally used a leave-one-year-out method in a time-wise manner. Models gradually improved over the years as more data was incorporated for training. The svmR and rf methods presented an overall performance of R2 ~0.60 (%RMSE ~20.5%), reaching the highest accuracy (R2 = 0.78, %RMSE ~16.6%) in 2014. Both approaches reveal the potentiality of the proposed models to account for the inter-annual and spatial garlic yield variability in Mexico. This procedure can be adjusted and used for other crop types or locations.

Research areas: Agriculture
Distribution of Antarctic toothfish Dissostichus mawsoni along East Antarctica: Environmental drivers and management implications (2019)

As Antarctic fish species experience increasing anthropogenic pressures there is a growing need to characterise the structure and function of their populations and understand how they may respond to changes in their environment. We integrated fishery-dependent and environmental data from years 20032017 to investigate the distribution of Antarctic toothfish Dissostichus mawsoni along a c. 3000 nm expanse of the shelf and slope along East Antarctica (30150 E). Spatially-explicit generalised additive mixed models were used to characterise the environmental drivers of relative abundance, mean individual weight, maturity-stage composition and sex ratio. Antarctic toothfish were not randomly distributed across the region, and spatial variations were characterised by complex relationships involving topography, temperature, salinity and sea ice. In particular, catch rates were highest at depths between 10001700 m. Mean weight and the proportion of fish that were mature both increased with depth and decreased with bottom temperature. Model predictions were also used to develop hypotheses relating to population function, including the location of nursery and spawning areas. This characterisation of the population can facilitate evaluation of fishing impacts in East Antarctica, and improve our understanding of the role of toothfish in Antarctic and Southern Ocean ecosystems.

Research areas: Agriculture
FarmCan: a physical, statistical, and machine learning   model to forecast crop water deficit for farms (2022)

In the coming decades, a changing climate, the loss of high-quality land, the slowing in the annual yield of cereals, and increasing fertilizer use indicate that better agricultural water management strategies are needed. In this study, we designed FarmCan, a novel, robust remote sensing and machine learning (ML) framework to forecast farms' needed daily crop water quantity or needed irrigation (NI). We used a diverse set of simulated and observed near-real-time (NRT) remote sensing data coupled with a random forest (RF) algorithm and inputs about farm-specific situations to predict the amount and timing of evapotranspiration (ET), potential ET (PET), soil moisture (SM), and root zone soil moisture (RZSM). Our case study of four farms in the Canadian Prairies Ecozone (CPE) shows that 8 d composite precipitation (P) has the highest correlation with changes () of RZSM and SM. In contrast, 8 d PET and 8 d ET do not offer a strong correlation with 8 d P. Using R2, root mean square error (RMSE), and KlingGupta efficiency (KGE) indicators, our algorithm could reasonably calculate daily NI up to 14 d in advance. From 2015 to 2020, the R2 values between predicted and observed 8 d ET and 8 d PET were the highest (80 % and 54 %, respectively). The 8 d NI also had an average R2 of 68%. The KGE of the 8 d ET and 8 d PET in four study farms showed an average of 0.71 and 0.50, respectively, with an average KGE of 0.62. FarmCan can be used in any region of the world to help stakeholders make decisions during prolonged periods of drought or waterlogged conditions, schedule cropping and fertilization, and address local government policy concerns.

Research areas: Agriculture
Contribution of above- and belowground bioenergy crop residues to soil (2017)

GHG mitigation by bioenergy crops depends on crop type, management practices, and the input of residue carbon (C) to the soil. Perennial grasses may increase soil C compared to annual crops because of more extensive root systems, but it is less clear how much soil C is derived from above‑ vs. belowground inputs. The objective of this study was to synthesize the existing knowledge regarding soil C inputs from above‑ and belowground crop residues in regions cultivated with sugarcane, corn, and miscanthus, and to predict the impact of residue removal and tillage on soil C stocks. The literature review showed that aboveground inputs to soil C (to 1‑m depth) ranged from 70% to 81% for sugarcane and corn vs. 40% for miscanthus. Modeled aboveground C inputs (to 30 cm depth) ranged from 54% to 82% for sugarcane, but were 67% for miscanthus. Because 50% of observed miscanthus belowground biomass is below 30 cm depth, it may be necessary to increase the depth of modeled soil C dynamics to reconcile modeled belowground C inputs with measured. Modeled removal of aboveground corn residue (25–100%) resulted in C stock reduction in areas of corn–corn–soybean rotation under conventional tillage, while no‑till management lessoned this impact. In sugarcane, soil C stocks were reduced when total aboveground residue was removed at one site, while partial removal of sugarcane residue did not reduce soil C stocks in either area. This study suggests that aboveground crop residues were the main C‑residue source to the soil in the current bioethanol sector (corn and sugarcane) and the indiscriminate removal of crop residues to produce cellulosic biofuels can reduce soil C stocks and reduce the environmental benefits of bioenergy. Moreover, a switch to feedstocks such as miscanthus with more allocation to belowground C could increase soil C stocks at a much faster rate.

Research areas: Energy Production/Use
Balancing food production within the planetary water boundary (2020)

Freshwater use is recognized as one of the nine planetary boundaries. However, water scarcity is a local or regional phenomenon, meaning that the global boundary must be spatially downscaled to reflect differences in water availability. In China, as in most countries, irrigation is the major freshwater user, closely linking food security to the freshwater boundary. To provide evidence supporting environmentally sustainable water use in Chinas food production, this study explores how a grain production shift affects the national water-scarcity footprint (WSF) and the potential to reach sustainable water use limits while maintaining the current grain production level. We found that the historical breadbasket shift towards water-scarce northern regions has increased the WSF by 40% from 1980 to 2015. To operate within the boundary, national irrigation needs to be reduced by 18% in hotspot regions, with implications of a 21% loss of grain production. However, this loss can be reduced to around 8% by closing yield gaps in water-rich regions. It demonstrates the high potential of integrating crop redistribution and closing yield gaps to achieve grain production goals within freshwater boundaries. This Chinese case study can be representative of the challenges faced by many of the worlds countries, where pressures on land and water resources are high and a sustainable means of increasing food supply must be found.

Research areas: Agriculture
Forested watersheds mitigate the thermal degradation of headwater fish assemblages under future climate change (2022)

Certain habitat features of stream ecosystems can reduce their sensitivity to climate change and help protect the integrity of cold‑water aquatic resources. Identifying such features is imperative for conserving the climate refugia of cold‑water species. Using a combination of stream temperature and fish assemblage data, we quantified thermal sensitivities of 192 headwater streams in Northwest Pennsylvania to identify which landscape features best explained stream susceptibility to temperature change. We then projected changes in native brook trout (Salvelinus fontinalis) distributions, non‑native brown trout (Salmo trutta) distributions and declines in cold‑water thermal integrity under future climate warming and land use change scenarios. Brook trout were predicted to become increasingly relegated to smaller streams under future stream warming. However, we found that streams with intact forest cover at the watershed level had low thermal sensitivities, which slowed rates of projected warming. As a result, streams with forested watersheds were predicted to have smaller declines in thermal integrity and lower extirpation probabilities of brook trout. Additionally, non‑native brown trout were not predicted to expand distributions under projected warming, suggesting minimal synergistic effects between non‑native species and climate change. Forest cover buffers headwater streams from the effects of global change, similar to how groundwater inputs reduce the rate of stream warming. Forest restoration at riparian and watershed levels should help mitigate thermal‑induced degradation of cold‑water aquatic resources.

Research areas: Ecosystems
Wheat and maize yield forecasting for the Tisza river catchment using MODIS NDVI time series and reported crop statistics (2018)

Stakeholders, policy makers, government planners and agricultural market participants in Central Eastern Europe require accurate and timely information about wheat and maize yield and production. The study site, the lowlands (altitude below 200 m) of the Tisza river catchment is by far the most important wheat and corn producing region in the Carpathian basin, and even in Central Eastern Europe. The conventional sampling of on-field data and data processing for crop forecasting requires significant amounts of time before official reports can be released. Several studies have shown that wheat and maize yield can be effectively forecast using satellite remote sensing. In this study, a freely available MODIS NDVI satellite data based wheat and maize yield forecasting methodology was developed and evaluated for estimating yield losses effected by drought. Wheat and maize yield was derived by regressing reported yield values against time series of 15 different peak-season MODIS-derived NDVI. The lowest RMSE values at the river basin level for both wheat and maize yield forecast versus reported yield were found when using at least six or more years of training data. Wheat forecast for the 2000 to 2015 growing seasons were within 0.819% and 19.08% of final reported yield values. Maize forecast at county level for the 2000 to 2015 growing seasons were within 0.299% and 17.14% of final reported yield values. The NashSutcliffe efficiency index (E1) is positive with E1 = 0.322 in the case of wheat forecast, and with E1 = 0.401 in the case of maize forecast, which means the developed and evaluated forecasting method performs acceptable forecast efficiency. Nevertheless the occurrence of extreme drought or extreme precipitation can alter the forecasting efficiency resulting over or underestimation. Overall statement, which based on MODIS NDVI, possible yield losses can easily be forecasted 68 weeks before harvesting and applying simple threshold levels, yield losses can be mapped simply.

Research areas: Agriculture
Finding a Suitable Niche for Cultivating Cactus Pear (Opuntia ficus-indica) as an Integrated Crop in Resilient Dryland Agroecosystems of India (2019)

Climate change poses a significant threat to agroecosystems, especially in the dry areas, characterized by abrupt precipitation pattern and frequent drought events. Ideal crops, tolerant to these events, such as cactus, can perform well under such changing climatic conditions. This study spatially maps land suitability for cactus (Opuntia ficus-indica) cultivation in India using the analytical hierarchical process (AHP). Nine essential growth factors that include the climate and edaphic components were considered for the period 2000 to 2007. About 32% of the total geographic area of the country is in the high to moderate suitable category. Remaining 46% falls under the marginally suitable and 22% under the low to very low suitable category. The suitability analysis, based on the precipitation anomaly (20082017), suggests a high probability of cactus growth in the western and east-central part of India. The relationship with aridity index shows a decreasing rate of suitability with the increase of aridity in the western and east-central provinces (~1 to 2). We conclude that integrating cactus into dryland farming systems and rangelands under changing climate can be one plausible solution to build resilient agro-ecosystems that provide food and fodder while enhancing the availability of ecosystem services.

Research areas: Agriculture
Mapping crop types in southeast India with smartphone crowdsourcing and deep learning (2020)

High resolution satellite imagery and modern machine learning methods hold the potential to fill existing data gaps in where crops are grown around the world at a sub-field level. However, high resolution crop type maps have remained challenging to create in developing regions due to a lack of ground truth labels for model development. In this work, we explore the use of crowdsourced data, Sentinel-2 and DigitalGlobe imagery, and convolutional neural networks (CNNs) for crop type mapping in India. Plantix, a free app that uses image recognition to help farmers diagnose crop diseases, logged 9 million geolocated photos from 20172019 in India, 2 million of which are in the states of Andhra Pradesh and Telangana in India. Crop type labels based on farmer-submitted images were added by domain experts and deep CNNs. The resulting dataset of crop type at coordinates is high in volume, but also high in noise due to location inaccuracies, submissions from out-of-field, and labeling errors. We employed a number of steps to clean the dataset, which included training a CNN on very high resolution DigitalGlobe imagery to filter for points that are within a crop field. With this cleaned dataset, we extracted Sentinel time series at each point and trained another CNN to predict the crop type at each pixel. When evaluated on the highest quality subset of crowdsourced data, the CNN distinguishes rice, cotton, and other crops with 74% accuracy in a 3-way classification and outperforms a random forest trained on harmonic regression features. Furthermore, model performance remains stable when low quality points are introduced into the training set. Our results illustrate the potential of non-traditional, high-volume/high-noise datasets for crop type mapping, some improvements that neural networks can achieve over random forests, and the robustness of such methods against moderate levels of training set noise. Lastly, we caution that obstacles like the lack of good Sentinel-2 cloud mask, imperfect mobile device location accuracy, and preservation of privacy while improving data access will need to be addressed before crowdsourcing can widely and reliably be used to map crops in smallholder systems.

Research areas: Agriculture