Search Results (12)

Agroforestry in the form of intercropping, boundary plantation, and home garden are parts of traditional land management systems in India. Systematic implementation of agroforestry may help achieve various ecosystem benefits, such as reducing soil erosion, maintaining biodiversity and microclimates, mitigating climate change, and providing food fodder and livelihood. The current study collected ground data for agroforestry patches in the Belpada block, Bolangir district, Odisha state, India. The agroforestry site-suitability analysis employed 15 variables on climate, soil, topography, and proximity, wherein the land use land cover (LULC) map was referred to prescribe the appropriate interventions. The random forest (RF) machine learning model was applied to estimate the relative weight of the determinant variables. The results indicated high accuracy (average suitability >0.87 as indicated by the validation data) and highlighted the dominant influence of the socioeconomic variables compared to soil and climate variables. The results show that >90% of the agricultural land in the study area is suitable for various agroforestry interventions, such as bund plantation and intercropping, based on the cropping intensity. The settlement and wastelands were found to be ideal for home gardens and bamboo block plantations, respectively. The spatially explicit data on agroforestry suitability may provide a baseline map and help the managers and planners. Moreover, the adopted approach can be hosted in cloud-based platforms and applied in the different agro-ecological zones of India, employing the local ground data on various agroforestry interventions. The regional and national scale agroforestry suitability and appropriate interventions map would help the agriculture managers to implement and develop policies. Full article

Mid-infrared (MIR) spectroscopy is emerging as one of the most promising technologies, as it is a rapid and cost-effective alternative to routine laboratory analysis for many soil properties. This study was conducted to evaluate the potential of mid-infrared spectroscopy for the rapid and nondestructive measurement of some important soil properties of Alfisols. A total of 336 georeferenced soil samples fromthe 0–15 cm soil layer of Alfisols that were collected from the eastern Indian states of Odisha and Jharkhand were used. The partial least-squares regression (PLSR), random forest, and support vector machine regression techniques were compared for the calibration of the spectral data with the wet chemistry soil data. The PLSR-based predictive models performed better than the other two regression techniques for all the soil properties, except for the electrical conductivity (EC). Good predictions with independent validation datasets were obtained for the clay and sand percentages and for the soil organic carbon (SOC) content, while satisfactory predictions were achieved for the silt percentage and the pH value. However, the performance of the predictive models was poor in the case of the EC and the extractable nutrients, such as the available phosphorus and potassium contents of the soil. Specific regions of the MIR spectra that contributed to the prediction of the soil SOC, the pH, and the clay and sand percentages were identified. The study demonstrates the potential of the MIR spectroscopic technique in the simultaneous estimation of the SOC content, the sand, clay, and silt percentages, and the pH of Alfisols from eastern India. Full article

Agriculture and agri-food systems of the highly vulnerable Middle East and North Africa (MENA) region needs a radical transformation under a changing climate. Based on a two-year effort, initially we developed a mega hypothesis on how to achieve climate-smart agri-food transformation in the region. In the study, we hypothesized that “Climate-Smart Lifts” implemented in the enabling environments can rapidly facilitate agri-food transformation in the region. In order to gather the stakeholders’ perception about this, we organized a collective conversation among ~400 stakeholders that represent various scales and sectors within the agriculture sector in MENA. These “listening cum learning consultations” were conducted through a survey followed by a series of webinars. The webinar discussions were strategically guided based on our hypothesis, the responses from the surveys and the regional needs. These discussions provided a forum to bring-out the stakeholders’ perspective on what new knowledge, partnerships, instruments and projects were needed in the MENA. The deliberation focused on the opportunities of public–private partnerships focusing in all the four major agroecosystems in MENA (irrigated, rainfed, rangelands, and deserts). In result, we developed an effective framework for strategic resource mobilization in the region, keeping in view the strong regional needs for climate actions and the requisite long-term commitments for the SDGs implementation. Full article

Droughts will increase in frequency, intensity, duration, and spread under climate change. Drought affects numerous sectors in society and the natural environment, including short-term reduced crop production, social conflict over water allocation, severe outmigration, and eventual famine. Early action can prevent escalation of impacts, requiring drought early warning systems (DEWSs) that give current assessments and sufficient notice for active risk management. While most droughts are relatively slow in onset, often resulting in late responses, flash droughts are becoming more frequent, and their sudden onset poses challenging demands on DEWSs for timely communication. We examine several DEWSs at global, regional, and national scales, with a special emphasis on agri-food systems. Many of these have been successful, such as some of the responses to 2015–2017 droughts in Africa and Latin America. Successful examples show that early involvement of stakeholders, from DEWS development to implementation, is crucial. In addition, regional and global cooperation can cross-fertilize with new ideas, reduce reaction time, and raise efficiency. Broadening partnerships also includes recruiting citizen science and including seemingly subjective indigenous knowledge that can improve monitoring, data collection, and uptake of response measures. More precise and more useful DEWSs in agri-food systems will prove even more cost-effective in averting the need for emergency responses, improving global food security. Full article

Accelerating crop improvement for increased yield and better adaptation to changing climatic conditions is an issue of increasing urgency in order to satisfy the ever-increasing global food demand. However, the major bottleneck is the absence of high-throughput plant phenotyping methods for rapid and cost-effective data-driven variety selection and release in plant breeding. Traditional phenotyping methods that rely on trained experts are slow, costly, labor-intensive, subjective, and often require destructive sampling. We explore ways to improve the efficiency of crop phenotyping through the use of unmanned aerial vehicle (UAV)-based multispectral remotely sensed data in maize (Zea mays L.) varietal response to maize streak virus (MSV) disease. Twenty-five maize varieties grown in a trial with three replications were evaluated under artificial MSV inoculation. Ground scoring for MSV infection was carried out at mid-vegetative, flowering, and mid-grain filling on a scale of 1 (resistant) to 9 (susceptible). UAV-derived spectral data were acquired at these three different phenological stages in multispectral bands corresponding to Green (0.53–0.57 μm), Red (0.64–0.68 μm), Rededge (0.73–0.74 μm), and Near-Infrared (0.77–0.81 μm). The imagery captured was stitched together in Pix4Dmapper, which generates two types of multispectral orthomosaics: the NoAlpha and the transparent mosaics for each band. The NoAlpha imagery was used as input into QGIS to extract reflectance data. Six vegetation indices were derived for each variety: normalized difference vegetation index (NDVI), green normalized difference vegetation index (GNDVI), Rededge NDVI (NDVI_rededge), Simple Ratio (SR), green Chlorophyll Index (CI_green), and Rededge Chlorophyll Index (CI_rededge). The Random Forest (RF) classifier was used to evaluate UAV-derived spectral and VIs with and without variable optimization. Correlations between the UAV-derived data and manual MSV scores were significant (R = 0.74–0.84). Varieties were classified into resistant, moderately resistant, and susceptible with overall classification accuracies of 77.3% (Kappa = 0.64) with optimized and 68.2% (Kappa = 0.51) without optimized variables, representing an improvement of ~13.3% due to variable optimization. The RF model selected GNDVI, CI_green, CI_rededge, and the Red band as the most important variables for classification. Mid-vegetative was the most ideal phenological stage for accurate varietal phenotyping and discrimination using UAV-derived multispectral data with RF under artificial MSV inoculation. The results provide a rapid UAV-based remote sensing solution that offers a step-change towards data availability at high spatial (submeter) and temporal (daily/weekly) resolution in varietal analysis for quick and robust high-throughput plant phenotyping, important for timely and unbiased data-driven variety selection and release in plant breeding programs, especially as climate change accelerates. Full article

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 (2008–2017), 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 (β~−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. Full article

Mapping the spatial and temporal dynamics of cropland is an important prerequisite for regular crop condition monitoring, management of land and water resources, or tracing and understanding the environmental impacts of agriculture. Analyzing archives of satellite earth observations is a proven means to accurately identify and map croplands. However, existing maps of the annual cropland extent either have a low spatial resolution (e.g., 250–1000 m from Advanced Very High Resolution Radiometer (AVHRR) to Moderate-resolution Imaging Spectroradiometer (MODIS); and existing high-resolution maps (such as 30 m from Landsat) are not provided frequently (for example, on a regular, annual basis) because of the lack of in situ reference data, irregular timing of the Landsat and Sentinel-2 image time series, the huge amount of data for processing, and the need to have a regionally or globally consistent methodology. Against this backdrop, we propose a reference time-series-based mapping method (RBM), and create binary cropland vs. non-cropland maps using irregular Landsat time series and RBM. As a test case, we created and evaluated annual cropland maps at 30 m in seven distinct agricultural landscapes in Xinjiang, China, and the Aral Sea Basin. The results revealed that RBM could accurately identify cropland annually, with producer’s accuracies (PA) and user’s accuracies (UA) higher than 85% between 2006 and 2016. In addition, cropland maps by RBM were significantly more accurate than the two existing products, namely GlobaLand30 and Finer Resolution Observation and Monitoring of Global Land Cover (FROM–GLC). Full article

Cropland abandonment is globally widespread and has strong repercussions for regional food security and the environment. Statistics suggest that one of the hotspots of abandoned cropland is located in the drylands of the Aral Sea Basin (ASB), which covers parts of post-Soviet Central Asia, Afghanistan and Iran. To date, the exact spatial and temporal extents of abandoned cropland remain unclear, which hampers land-use planning. Abandoned land is a potentially valuable resource for alternative land uses. Here, we mapped the abandoned cropland in the drylands of the ASB with a time series of the Normalized Difference Vegetation Index (NDVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS) from 2003–2016. To overcome the restricted ability of a single classifier to accurately map land-use classes across large areas and agro-environmental gradients, “stratum-specific” classifiers were calibrated and classification results were fused based on a locally weighted decision fusion approach. Next, the agro-ecological suitability of abandoned cropland areas was evaluated. The stratum-specific classification approach yielded an overall accuracy of 0.879, which was significantly more accurate ( $p$ < 0.05) than a “global” classification without stratification, which had an accuracy of 0.811. In 2016, the classification results showed that 13% (1.15 Mha) of the observed irrigated cropland in the ASB was idle (abandoned). Cropland abandonment occurred mostly in the Amudarya and Syrdarya downstream regions and was associated with degraded land and areas prone to water stress. Despite the almost twofold population growth and increasing food demand in the ASB area from 1990 to 2016, abandoned cropland was also located in areas with high suitability for farming. The map of abandoned cropland areas provides a novel basis for assessing the causes leading to abandoned cropland in the ASB. This contributes to assessing the suitability of abandoned cropland for food or bioenergy production, carbon storage, or assessing the environmental trade-offs and social constraints of recultivation. Full article

Global insurance markets are vast and diverse, and may offer many opportunities for remote sensing. To date, however, few operational applications of remote sensing for insurance exist. Papers claiming potential application of remote sensing typically stress the technical possibilities, without considering its contribution to customer value for the insured or to the profitability of the insurance industry. Based on a systematic search of available literature, this review investigates the potential and actual support of remote sensing to the insurance industry. The review reveals that research on remote sensing in classical claim-based insurance described in the literature revolve around crop damage and flood and fire risk assessment. Surprisingly, the use of remote sensing in claim-based insurance appears to be instigated by government rather than the insurance industry. In contrast, insurance companies are offering various index insurance products that are based on remote sensing. For example, remotely sensed index insurance for rangelands and livestock are operational, while various applications in crop index insurance are being considered or under development. The paper discusses these differences and concludes that there is particular scope for application of remote sensing by the insurance industry in index insurance because (1) indices can be constructed that correlate well with what is insured; (2) these indices can be delivered at low cost; and (3) it opens up new markets that are not served by claim-based insurance. The paper finally suggests that limited adoption of remote sensing in insurance results from a lack of mutual understanding and calls for greater cooperation between the insurance industry and the remote sensing community. Full article

The areal extent and spatial distribution of evergreen forests in the tropical zones are important for the study of climate, carbon cycle and biodiversity. However, frequent cloud cover in the tropical regions makes mapping evergreen forests a challenging task. In this study we developed a simple and novel mapping algorithm that is based on the temporal profile analysis of Land Surface Water Index (LSWI), which is calculated as a normalized ratio between near infrared and shortwave infrared spectral bands. The 8-day composites of MODIS Land Surface Reflectance data (MOD09A1) in 2001 at 500-m spatial resolution were used to calculate LSWI. The LSWI-based mapping algorithm was applied to map evergreen forests in tropical Africa, America and Asia (30°N–30°S). The resultant maps of evergreen forests in the tropical zone in 2001, as estimated by the LSWI-based algorithm, are compared to the three global forest datasets [FAO FRA 2000, GLC2000 and the standard MODIS Land Cover Product (MOD12Q1) produced by the MODIS Land Science Team] that are developed through complex algorithms and processes. The inter-comparison of the four datasets shows that the area estimate of evergreen forest from the LSWI-based algorithm fall within the range of forest area estimates from the FAO FRA 2000, GLC2000 and MOD12Q1 at a country level. The area and spatial distribution of evergreen forests from the LSWI-based algorithm is to a large degree similar to those of the MOD12Q1 produced by complex mapping algorithms. The results from this study demonstrate the potential of the LSWI-based mapping algorithm for large-scale mapping of evergreen forests in the tropical zone at moderate spatial resolution. Full article

The goal of this research was to compare the remote-sensing derived irrigated areas with census-derived statistics reported in the national system. India, which has nearly 30% of global annualized irrigated areas (AIAs), and is the leading irrigated area country in the World, along with China, was chosen for the study. Irrigated areas were derived for nominal year 2000 using time-series remote sensing at two spatial resolutions: (a) 10-km Advanced Very High Resolution Radiometer (AVHRR) and (b) 500-m Moderate Resolution Imaging Spectroradiometer (MODIS). These areas were compared with the Indian National Statistical Data on irrigated areas reported by the: (a) Directorate of Economics and Statistics (DES) of the Ministry of Agriculture (MOA), and (b) Ministry of Water Resources (MoWR). A state-by-state comparison of remote sensing derived irrigated areas when compared with MoWR derived irrigation potential utilized (IPU), an equivalent of AIA, provided a high degree of correlation with R² values of: (a) 0.79 with 10-km, and (b) 0.85 with MODIS 500-m. However, the remote sensing derived irrigated area estimates for India were consistently higher than the irrigated areas reported by the national statistics. The remote sensing derived total area available for irrigation (TAAI), which does not consider intensity of irrigation, was 101 million hectares (Mha) using 10-km and 113 Mha using 500-m. The AIAs, which considers intensity of irrigation, was 132 Mha using 10-km and 146 Mha using 500-m. In contrast the IPU, an equivalent of AIAs, as reported by MoWR was 83 Mha. There are “large variations” in irrigated area statistics reported, even between two ministries (e.g., Directorate of Statistics of Ministry of Agriculture and Ministry of Water Resources) of the same national system. The causes include: (a) reluctance on part of the states to furnish irrigated area data in view of their vested interests in sharing of water, and (b) reporting of large volumes of data with inadequate statistical analysis. Overall, the factors that influenced uncertainty in irrigated areas in remote sensing and national statistics were: (a) inadequate accounting of irrigated areas, especially minor irrigation from groundwater, in the national statistics, (b) definition issues involved in mapping using remote sensing as well as national statistics, (c) difficulties in arriving at precise estimates of irrigated area fractions (IAFs) using remote sensing, and (d) imagery resolution in remote sensing. The study clearly established the existing uncertainties in irrigated area estimates and indicates that both remote sensing and national statistical approaches require further refinement. The need for accurate estimates of irrigated areas are crucial for water use assessments and food security studies and requires high emphasis. Full article

The overarching goal of this paper was to espouse methods and protocols for water productivity mapping (WPM) using high spatial resolution Landsat remote sensing data. In a world where land and water for agriculture are becoming increasingly scarce, growing “more crop per drop” (increasing water productivity) becomes crucial for food security of future generations. The study used time-series Landsat ETM+ data to produce WPMs of irrigated crops, with emphasis on cotton in the Galaba study area in the Syrdarya river basin of Central Asia. The WPM methods and protocols using remote sensing data consisted of: (1) crop productivity (ton/ha) maps (CPMs) involvingcrop type classification, crop yield and biophysical modeling, and extrapolating yield models to larger areas using remotely sensed data; (2) crop water use (m³/ha) maps (WUMs) (or actual seasonal evapotranspiration or actual ET) developed through Simplified Surface Energy Balance (SSEB) model; and (3) water productivity (kg/m³) maps (WPMs) produced by dividing raster layers of CPMs by WUMs. The SSEB model calculated WUMs (actual ET) by multiplying the ET fractionby reference ET. The ETfraction was determined using Landsat thermal imagery by selecting the “hot” pixels (zero ET) and “cold” pixels (maximum ET). The grass reference ET was calculated by FAO Penman-Monteith method using meteorological data. The WPMs for the Galaba study area demonstrated a wide variations (0-0.54 kg/m³) in water productivity of cotton fields with overwhelming proportion (87%) of the area having WP less than 0.30 kg/m³, 11% of the area having WP in range of 0.30-0.36 kg/m³, and only 2% of the area with WP greater than 0.36 kg/m³. These results clearly imply that there are opportunities for significant WP increases in overwhelming proportion of the existing croplands. The areas of low WP are spatially pin-pointed and can be used as focus for WP improvements through better land and water management practices. Full article