Impacts of irrigation tank restoration on water bodies and croplands in Telangana State of India using Landsat time series data and machine learning algorithms

In 2014, the State of Telangana in southern India began repairing and restoring more than 46,000 irrigation water tanks (artificial reservoirs) under the Mission Kakatiya project with an investment in excess of USD 2 billion. In this study, we attempted to map the temporal changes that have occurred in cropland areas and water bodies as a result of the project, using remote sensing imagery and applying land use/land cover (LULC) mapping algorithms. We used 16-day time series data from Landsat 8 to study the spatial distribution of changes in water bodies and cropland areas over the 2013–18 period. Ground survey information was used to assess the pixel-based accuracy of the Landsat-derived data. The areas served by these tanks were identified on the basis of training data and Random Forest algorithms using Google Earth Engine. Our spatial analysis revealed a substantial increase in cropped area under irrigation and expansion of water bodies over the study period. We observed a 20% increase in total tank area in 2017–18 and total cropland and irrigated area expansion of the order of 0.6M ha and 0.2M ha, respectively. A comparison of ground survey data and four LULC classes derived from Landsat temporal imagery showed an overall accuracy of 87%, significantly correlated with national agriculture statistics. Periodic monitoring based on remote sensing has proved to be an effective method of capturing LULC changes resulting from the Mission Kakatiya interventions. Higher-resolution satellite data can further improve the accuracy of estimates

Identifying Suitable Watersheds across Nigeria Using Biophysical Parameters and Machine Learning Algorithms for Agri–Planning

Identifying suitable watersheds is a prerequisite to operationalizing planning interventions for agricultural development. With the help of geospatial tools, this paper identified suitable watersheds across Nigeria using biophysical parameters to aid agricultural planning. Our study included various critical thematic layers such as precipitation, temperature, slope, land-use/land-cover (LULC), soil texture, soil depth, and length of growing period, prepared and modeled on the Google Earth Engine (GEE) platform. Using expert knowledge, scores were assigned to these thematic layers, and a priority map was prepared based on the combined weighted average score. We also validated priority watersheds. For this, the study area was classified into three priority zones ranging from ‘high’ to ‘low’. Of the 277 watersheds identified, 57 fell in the high priority category, implying that they are highly favorable for interventions. This would be useful for regional-scale water resource planning for agricultural landscape development

Assessment of Cropland Changes Due to New Canals in Vientiane Prefecture of Laos using Earth Observation Data

The lower catchment area of a Mak Hiao river system is vulnerable to flash floods and water stress. So it is important to construct irrigation structures in this area to minimize floods during the rainy season and store water for the winter season. The Asian Development Bank (ADB) has been supporting the Government of Laos in constructing such small reservoirs like Donkhuay schemes 1 & 2, Mak Hiao, Nalong 3 and Sang Houabor projects in lower catchment areas. Our study evaluated the impacts of small irrigation schemes in terms of land-use/landcover (LULC), crop intensity, and productivity changes, using high resolution satellite imagery, socioeconomic, and ground data. We analyzed the temporal cropping pattern in the Vientiane prefecture of Laos using Planet and Sentinel-2 data. On the other hand, crop intensity and cropland changes were mapped using Sentinel-2 data and spectral matching techniques (SMTs). The crop classification accuracy based on field-plot data was 88.6%. Our results show that irrigation projects in the lower catchment areas brought about significant on-site changes in terms of cropland expansion and increased crop intensity. Remarkable changes in LULC were observed especially in the command areas owing to an increase of about 300% in crop area with access to irrigation and increase of water bodies by 31%. Our study found that interventions at the level of the command area do improved on-site soil, water and environmental services. They study emphasized underline the role of land-use regulations in reducing pressure on natural land-use systems and thereby serving the major goal of up-scaling sustainable natural resource management. The study documented the vital role of small/medium irrigation projects in restoring ecosystem services such as cropping patterns and LULC conversio

Assessing potential locations for flood-based farming using satellite imagery: a case study of Afar region, Ethiopia

The dry lowlands of Ethiopia are seasonally affected by long periods of low rainfall and, coinciding with rainfall in the Amhara highlands, flood waters which flow onto the lowlands resulting in damage to landscapes and settlements. In an attempt to convert water from storm generated floods into productive use, this study proposes a methodology using remote sensing data and geographical information system tools to identify potential sites where flood spreading weirs may be installed and farming systems developed which produce food and fodder for poor rural communities. First, land use land cover maps for the study area were developed using Landsat-8 and MODIS temporal data. Sentinel-1 data at 10 and 20 m resolution on a 12-day basis were then used to determine flood prone areas. Slope and drainage maps were derived from Shuttle RADAR Topography Mission Digital Elevation Model at 90 m spatial resolution. Accuracy assessment using ground survey data showed that overall accuracies (correctness) of the land use/land cover classes were 86% with kappa 0.82. Coinciding with rainfall in the uplands, March and April are the months with flood events in the short growing season (belg) and June, July and August have flood events during the major (meher) season. In the Afar region, there is potentially >0.55 m ha land available for development using seasonal flood waters from belg or meher seasons. During the 4 years of monitoring (2015–2018), a minimum of 142,000 and 172,000 ha of land were flooded in the belg and meher seasons, respectively. The dominant flooded areas were found in slope classes of <2% with spatial coverage varying across the districts. We concluded that Afar has a huge potential for flood-based technology implementation and recommend further investigation into the investments needed to support new socio-economic opportunities and implications for the local agro-pastoral communities

Identifying prospects and potential areas for introducing pearl millet stress-tolerant cultivars in Rajasthan, India: A geospatial analysis

Dryland crops are highly prone to various stresses such as water stress (drought) and heat stress. The identification of stress-prone regions is crucial for effective and efficient implementation of appropriate solutions, such as stress-tolerant crop varieties. This study was conducted in Rajasthan state located in North-western India. Rajasthan is predominantly a rainfed pearl millet ecosystem (>50 % during monsoon season) in India. The pearl millet productivity in Western Rajasthan is the lowest in India with a significant decline in its cultivated area. The present study tried to analyse the pearl millet cropping systems in various ecologies and identified the stressprone areas by analysing the stress pattern from 2011 to 2020 which helps in targeting the stress tolerant cultivars. The spatial distribution of pearl millet areas was mapped using Sentinel-2 time-series data and spectral matching techniques. The mapped pearl millet areas were well correlated with district-level statistics obtained from secondary sources. Application of geospatial techniques for monitoring changes in pearl millet cropped area proves to be a cost-effective, and reliable approach. It also helps in assessing the cultivated area changes as well as the quantification of yield losses caused by abiotic stresses such as drought and heat. Agricultural research institutes, progressive farmers and line departments from the government can use these findings for better targeting and introduction of climate SMART pearl millet technologies in the state. Introduction of resilient technologies minimize the production risks faced by small and marginal farmer thereby reduces the crop income negative deviations. Scaling-up of such technologies not only protects farmer’s livelihoods but also enhances the food and nutritional security in the state

Measuring and Influencing Behavior Change in Dietary Intake: Integrated Photovoice Approach in Nutrition Interventions in Eastern Kenya

A study conducted in two wards of Tharaka Nithi subcounty in Kenya documented the impact of using photovoice as a learning tool to build awareness about diets in order to influence behavior change, as well as a method to measure dietary intake. After a year’s nutrition awareness drive using Smart Food branding, in the intervention area, a total of 60 participants from intervention and control areas were identified for the photovoice exercise. The analysis showed household and women’s dietary diversity scores to be higher in the intervention group by 35% and 45%, respectively. An estimate of nutrient intake revealed a higher intake of calories, protein, calcium, iron and zinc ranging from 70% to 205% in the intervention group. Qualitative feedback on the photovoice approach reflected increased nutrition awareness and behavior change. Results showed the efficacy of the approach in evaluating diets while simultaneously improving participants’ realization of what they were consuming using images captured and a one-on-one dis-cussion with nutritionists. The improvement in dietary diversity scores reflected the effectiveness of this creative participatory and branded approach in imparting a strong message on and enthusiasm for learning about nutrition, resulting in behavior change

The diagnostic conundrum and liver transplantation outcome for combined hepatocellular-cholangiocarcinoma

Combined hepatocellular-cholangiocarcinoma (cHCC-CC) is a rare primary liver malignancy with mixed hepatocellular carcinoma (HCC) and cholangiocarcinoma (CC) histological features. It is almost impossible to obtain an accurate, preoperative noninvasive diagnosis of cHCC-CC with tumor markers or cross-sectional abdominal imaging due to the mixed histological features. Despite these difficulties, accurate cHCC-CC diagnosis remains an important goal with prognostic significance. In our study, we retrospectively reviewed the tumor markers: AFP and CA 19-9, and cross-sectional liver imaging, in light of liver explant findings, to identify and characterize cHCC-CC features followed by liver transplantation (LT) outcome analysis. The results from this 12 patient cohort failed to identify characteristic features for cHCC-CC. None of the imaging features helped to identify the cHCC-CC tumor and they mimicked either HCC or CC, depending on the degree of glandular differentiation expressed histologically. In our cHCC-CC LT recipients, the 1-, 3- and 5-year cumulative survival probabilities were 79%, 66% and 16%, respectively with a 5-year survival comparable to or better than LT for intrahepatic CC but poorer than LT for HCC following the Milan criteria. Conceivably explained by its cholangiocarcinoma component the LT outcome for this rare and hard to diagnose tumor appears poor. © 2010 The American Society of Transplantation and the American Society of Transplant Surgeons

Optimizing Crop Yield Estimation through Geospatial Technology: A Comparative Analysis of a Semi-Physical Model, Crop Simulation, and Machine Learning Algorithms

This study underscores the critical importance of accurate crop yield information for national food security and export considerations, with a specific focus on wheat yield estimation at the Gram Panchayat (GP) level in Bareilly district, Uttar Pradesh, using technologies such as machine learning algorithms (ML), the Decision Support System for Agrotechnology Transfer (DSSAT) crop model and semi-physical models (SPMs). The research integrates Sentinel-2 time-series data and ground data to generate comprehensive crop type maps. These maps offer insights into spatial variations in crop extent, growth stages and the leaf area index (LAI), serving as essential components for precise yield assessment. The classification of crops employed spectral matching techniques (SMTs) on Sentinel-2 time-series data, complemented by field surveys and ground data on crop management. The strategic identification of crop-cutting experiment (CCE) locations, based on a combination of crop type maps, soil data and weather parameters, further enhanced the precision of the study. A systematic comparison of three major crop yield estimation models revealed distinctive gaps in each approach. Machine learning models exhibit effectiveness in homogenous areas with similar cultivars, while the accuracy of a semi-physical model depends upon the resolution of the utilized data. The DSSAT model is effective in predicting yields at specific locations but faces difficulties when trying to extend these predictions to cover a larger study area. This research provides valuable insights for policymakers by providing near-real-time, high-resolution crop yield estimates at the local level, facilitating informed decision making in attaining food security