What drives migration in northern Gujarat?

In a new IGC working paper, Ram Fishman, Meha Jain, and Avinash Kishore investigate factors that drive environmental migration and the economic impact of geographical mobility

An automated approach to map winter cropped area of smallholder farms across large scales using MODIS imagery

Fine-scale agricultural statistics are an important tool for understanding trends in food production and their associated drivers, yet these data are rarely collected in smallholder systems. These statistics are particularly important for smallholder systems given the large amount of fine-scale heterogeneity in production that occurs in these regions. To overcome the lack of ground data, satellite data are often used to map fine-scale agricultural statistics. However, doing so is challenging for smallholder systems because of (1) complex sub-pixel heterogeneity; (2) little to no available calibration data; and (3) high amounts of cloud cover as most smallholder systems occur in the tropics. We develop an automated method termed the MODIS Scaling Approach (MSA) to map smallholder cropped area across large spatial and temporal scales using MODIS Enhanced Vegetation Index (EVI) satellite data. We use this method to map winter cropped area, a key measure of cropping intensity, across the Indian subcontinent annually from 2000-2001 to 2015-2016. The MSA defines a pixel as cropped based on winter growing season phenology and scales the percent of cropped area within a single MODIS pixel based on observed EVI values at peak phenology. We validated the result with eleven high-resolution scenes (spatial scale of 5 × 5 m2 or finer) that we classified into cropped versus non-cropped maps using training data collected by visual inspection of the high-resolution imagery. The MSA had moderate to high accuracies when validated using these eleven scenes across India (R2 ranging between 0.19 and 0.89 with an overall R2 of 0.71 across all sites). This method requires no calibration data, making it easy to implement across large spatial and temporal scales, with 100% spatial coverage due to the compositing of EVI to generate cloud-free data sets. The accuracies found in this study are similar to those of other studies that map crop production using automated methods and use no calibration data. To aid research on agricultural production at fine spatial scales in India, we make our annual winter crop maps from 2000-2001 to 2015-2016 at 1 × 1 km2 produced in this study publically available through the NASA Socioeconomic Data and Applications Center (SEDAC) hosted by the Center for International Earth Science Information Network (CIESIN) at Columbia University. We also make our R script available since it is likely that this method can be used to map smallholder agriculture in other regions across the globe given that our method performed well in disparate agro-ecologies across India

Mapping cropping intensity of smallholder farms: A comparison of methods using multiple sensors

The food security of smallholder farmers is vulnerable to climate change and climate variability. Cropping intensity, the number of crops planted annually, can be used as a measure of food security for smallholder farmers given that it can greatly affect net production. Current techniques for quantifying cropping intensity may not accurately map smallholder farms where the size of one field is typically smaller than the spatial resolution of readily available satellite data. We evaluated four methods that use multi-scalar datasets and are commonly used in the literature to assess cropping intensity of smallholder farms: 1) the Landsat threshold method, which identifies if a Landsat pixel is cropped or uncropped during each growing season, 2) the MODIS peak method, which determines if there is a phenological peak in the MODIS Enhanced Vegetation Index time series during each growing season, 3) the MODIS temporal mixture analysis, which quantifies the sub-pixel heterogeneity of cropping intensity using phenological MODIS data, and 4) the MODIS hierarchical training method, which quantifies the sub-pixel heterogeneity of cropping intensity using hierarchical training techniques. Each method was assessed using four criteria: 1) data availability, 2) accuracy across different spatial scales (at aggregate scales 250 × 250 m, 1 × 1 km, 5 × 5 km, and 10 × 10 km), 3) ease of implementation, and 4) ability to use the method over large spatial and temporal scales. We applied our methods to two regions in India (Gujarat and southeastern Madhya Pradesh) that represented diversity in crop type, soils, climatology, irrigation access, cropping intensity, and field size. We found that the Landsat threshold method is the most accurate (R2 greater than or equal to 0.71 and RMSE less than or equal to 0.14), particularly at smaller scales of analysis. Yet given the limited availability of Landsat data, we find that the MODIS hierarchical training method meets multiple criteria for mapping cropping intensity over large spatial and temporal scales. Furthermore, the adjusted R2 between predicted and validation data generally increased and the RMSE decreased with spatial aggregation greater than or equal to 5 × 5 km (R2 up to 0.97 and RMSE as low as 0.00). Our model accuracy varied based on the region and season of analysis and was lowest during the summer season in Gujarat when there was high sub-pixel heterogeneity due to sparsely cropped agricultural land-cover. While our results specifically apply to our study regions in India, they most likely also apply to smallholder agriculture in other locations across the globe where the same types of satellite data are readily available

Sensitivity of crop cover to climate variability: Insights from two Indian agro-ecoregions

Crop productivity in India varies greatly with inter-annual climate variability and is highly dependent on monsoon rainfall and temperature. The sensitivity of yields to future climate variability varies with crop type, access to irrigation and other biophysical and socio-economic factors. To better understand sensitivities to future climate, this study focuses on agro-ecological subregions in Central and Western India that span a range of crops, irrigation, biophysical conditions and socioeconomic characteristics. Climate variability is derived from remotely-sensed data products, Tropical Rainfall Measuring Mission (TRMM – precipitation) and Moderate Resolution Imaging Spectroradiometer (MODIS – temperature). We examined green-leaf phenologies as proxy for crop productivity using the MODIS Enhanced Vegetation Index (EVI) from 2000 to 2012. Using both monsoon and winter growing seasons, we assessed phenological sensitivity to inter-annual variability in precipitation and temperature patterns. Inter-annual EVI phenology anomalies ranged from −25% to 25%, with some highly anomalous values up to 200%. Monsoon crop phenology in the Central India site is highly sensitive to climate, especially the timing of the start and end of the monsoon and intensity of precipitation. In the Western India site, monsoon crop phenology is less sensitive to precipitation variability, yet shows considerable fluctuations in monsoon crop productivity across the years. Temperature is critically important for winter productivity across a range of crop and management types, such that irrigation might not provide a sufficient buffer against projected temperature increases. Better access to weather information and usage of climate-resilient crop types would play pivotal role in maintaining future productivity. Effective strategies to adapt to projected climate changes in the coming decades would also need to be tailored to regional biophysical and socio-economic conditions

Researching farmer behaviour in climate change adaptation and sustainable agriculture: lessons learned from five case studies

Understanding farmer behaviour is needed for local agricultural systems to produce food sustainably while facing multiple pressures. We synthesize existing literature to identify three fundamental questions that correspond to three distinct areas of knowledge necessary to understand farmer behaviour: 1) decision-making model; 2) cross-scale and cross-level pressures; and 3) temporal dynamics. We use this framework to compare five interdisciplinary case studies of agricultural systems in distinct geographical contexts across the globe. We find that these three areas of knowledge are important to understanding farmer behaviour, and can be used to guide the interdisciplinary design and interpretation of studies in the future. Most importantly, we find that these three areas need to be addressed simultaneously in order to understand farmer behaviour. We also identify three methodological challenges hindering this understanding: the suitability of theoretical frameworks, the trade-offs among methods and the limited timeframe of typical research projects. We propose that a triangulation research strategy that makes use of mixed methods, or collaborations between researchers across mixed disciplines, can be used to successfully address all three areas simultaneously and show how this has been achieved in the case studies. The framework facilitates interdisciplinary research on farmer behaviour by opening up spaces of structured dialogue on assumptions, research questions and methods employed in investigation

Winter crop sensitivity to inter-annual climate variability in central India

India is predicted to be one of the most vulnerable agricultural regions to future climate changes. Here, we examined the sensitivity of winter cropping systems to inter-annual climate variability in a local market and subsistence-based agricultural system in central India, a data-rich validation site, in order to identify the climate parameters to which winter crops – mainly wheat and pulses in this region – might be sensitive in the future. We used satellite time-series data to quantify inter-annual variability in multiple climate parameters and in winter crop cover, agricultural census data to quantify irrigation, and field observations to identify locations for specific crop types. We developed three mixed-effect models (250 m to 1 km scale) to identify correlations between crop cover (wheat and pulses) and twenty-two climate and environmental parameters for 2001-2013. We find that winter daytime mean temperature (November–January) is the most significant factor affecting winter crops, irrespective of crop type, and is negatively associated with winter crop cover. With pronounced winter warming projected in the coming decades, effective adaptation by smallholder farmers in similar landscapes would require additional strategies, such as access to fine-scale temperature forecasts and heat-tolerant winter crop varieties

Winter crop sensitivity to inter-annual climate variability in central India

India is predicted to be one of the most vulnerable agricultural regions to future climate changes. Here, we examined the sensitivity of winter cropping systems to inter-annual climate variability in a local market and subsistence-based agricultural system in central India, a data-rich validation site, in order to identify the climate parameters to which winter crops – mainly wheat and pulses in this region – might be sensitive in the future. We used satellite time-series data to quantify inter-annual variability in multiple climate parameters and in winter crop cover, agricultural census data to quantify irrigation, and field observations to identify locations for specific crop types. We developed three mixed-effect models (250 m to 1 km scale) to identify correlations between crop cover (wheat and pulses) and twenty-two climate and environmental parameters for 2001-2013. We find that winter daytime mean temperature (November–January) is the most significant factor affecting winter crops, irrespective of crop type, and is negatively associated with winter crop cover. With pronounced winter warming projected in the coming decades, effective adaptation by smallholder farmers in similar landscapes would require additional strategies, such as access to fine-scale temperature forecasts and heat-tolerant winter crop varieties

EFFECT OF PSYCHOLOGICAL FIRST AID ON MENTAL HEALTH IN HOSPITALIZED STABLE COVID-19 PATIENTS: A PRE-POST RESEARCH DESIGN

Background: The COVID-19 pandemic is known to affect mental health of sufferers. Psychological First Aid (PFA) is a mental health service for individuals in crisis, which can be provided to anyone regardless of age and it does not require mental health expertise. Its effect on mental health issues of COVID-19 patients has not been studied effectively. The present study aimed to assess the psychological impact and effect of PFA on mental health in stable COVID-19 hospitalized patients. Subjects and methods: This was an interventional study with a pre-post research design in a tertiary government teaching hospital in eastern India. 93 stable patients who were admitted in a period of a month with COVID-19 were included in the study after obtaining appropriate consent. They were provided PFA (both structured individual and group sessions) by trained nurses. The Depression, Anxiety, and Stress scale (DASS-21) was used to assess depression, anxiety, and stress in the patients before and after intervention. Results: The mean age of study population which comprised of 68.8% males was 56.2 ± 13.7 years. Median scores for depression, anxiety and stress were 4, 6 and 6 on admission and 0, 2 and 2 respectively before discharge after intervention (P<0.001). 13%, 25.9% and 8.6% were the combined percentages scores of patients with varying levels of depression, anxiety and stress at the time of admission which were reduced to 4.3% (P=0.046), 5.4% (P=0.001), 2.2% (P=0.03) respectively before discharge after intervention within one week. Conclusion: PFA may be a cost-effective intervention in stable COVID-19 admitted patients who had depression, anxiety, and stress

Regional evapotranspiration from an image-based implementation of the Surface Temperature Initiated Closure (STIC1.2) model and its validation across an aridity gradient in the conterminous US

Recent studies have highlighted the need for improved characterizations of aerodynamic conductance and temperature (gA and T0) in thermal remote sensing-based surface energy balance (SEB) models to reduce uncertainties in regional-scale evapotranspiration (ET) mapping. By integrating radiometric surface temperature (TR) into the Penman-Monteith (PM) equation and finding analytical solutions of gA and T0, this need was recently addressed by the Surface Temperature Initiated Closure (STIC) model. However, previous implementations of STIC were confined to the ecosystem-scale using flux tower observations of infrared temperature. This study demonstrates the first regional-scale implementation of the most recent version of the STIC model (STIC1.2) that physically integrates Moderate Resolution Imaging Spectroradiometer (MODIS)-derived TR and ancillary land surface variables in conjunction with NLDAS (North American Land Data Assimilation System) atmospheric variables into a combined structure of the PM and Shuttleworth-Wallace framework for estimating ET at 1 km × 1 km spatial resolution. Evaluation of STIC1.2 at thirteen core AmeriFlux sites covering a broad spectrum of climates and biomes across an aridity gradient in the conterminous US suggests that STIC1.2 can provide spatially explicit ET maps with reliable accuracies from dry to wet extremes. When observed ET from one wet, one dry, and one normal precipitation year from all sites were combined, STIC1.2 explained 66 % of the variability in observed 8-day cumulative ET with a root mean square error (RMSE) of 7.4 mm/8-day, mean absolute error (MAE) of 5 mm/8-day, and percent bias (PBIAS) of -4 %. These error statistics show higher accuracies than a widely-used SEB-based Surface Energy Balance System (SEBS) and PM-based MOD16 ET, which were found to overestimate (PBIAS = 28 %) and underestimate ET (PBIAS = -26 %), respectively. The performance of STIC1.2 was better in forest and grassland ecosystems as compared to cropland (20 % underestimation) and woody savanna (40 % overestimation). Model inter-comparison suggested that ET differences between the models are robustly correlated with gA and associated roughness length estimation uncertainties which are intrinsically connected to TR uncertainties, vapour pressure deficit (DA), and vegetation cover. A consistent performance of STIC1.2 in a broad range of hydrological and biome categories as well as the capacity to capture spatio-temporal ET signatures across an aridity gradient points to its potential for near real time ET mapping from regional to continental scales.NASA Land-Cover Land-Use Change Grant (NNX17AH97G)NASA new investigator program award (NNX16AI19G)BIOTRANS (grant number, 00001145)CAOS-2 project grant (INTER/DFG/14/02)STEREOIII (INTER/STEREOIII/13/03/HiWET; CONTRACT NR SR/00/301)https://deepblue.lib.umich.edu/bitstream/2027.42/143157/1/hess-2017-535.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/143157/4/hess-22-2311-2018.pdfDescription of hess-2017-535.pdf : SUPERSEDED: for historical purposes onl

Mapping cover crop species in southeastern Michigan using Sentinel-2 satellite data and Google Earth Engine

Cover crops are a critical agricultural practice that can improve soil quality, enhance crop yields, and reduce nitrogen and phosphorus losses from farms. Yet there is limited understanding of the extent to which cover crops have been adopted across large spatial and temporal scales. Remote sensing offers a low-cost way to monitor cover crop adoption at the field scale and at large spatio-temporal scales. To date, most studies using satellite data have mapped the presence of cover crops, but have not identified specific cover crop species, which is important because cover crops of different plant functional types (e.g., legumes, grasses) perform different ecosystem functions. Here we use Sentinel-2 satellite data and a random forest classifier to map the cover crop species cereal rye and red clover, which represent grass and legume functional types, in the River Raisin watershed in southeastern Michigan. Our maps of agricultural landcover across this region, including the two cover crop species, had moderate to high accuracies, with an overall accuracy of 83%. Red clover and cereal rye achieved F1 scores that ranged from 0.7 to 0.77, and user's and producer's accuracies that ranged from 63.3% to 86.2%. The most common misclassification of cover crops was fallow fields with remaining crop stubble, which often looked similar because these cover crop species are typically planted within existing crop stubble, or interseeded into a grain crop. We found that red-edge bands and images from the end of April and early July were the most important for classification accuracy. Our results demonstrate the potential to map individual cover crop species using Sentinel-2 imagery, which is critical for understanding the environmental outcomes of increasing crop diversity on farms