Posts Tagged ‘GIS’

Geospatial Technology Holds Potential to Revolutionize Agricultural Interventions

Posted on India-news, News & Announcements, December 1, 2016

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What information is needed to make a reasonably precise agronomic recommendation for a small plot of land, or for an entire district or a state?

Agronomists must collect and analyze a multitude of variables when formulating agronomic recommendations, including crop and soil types, biotic and abiotic stresses, weed and nutrient management practices, weather and available irrigation infrastructure. Moreover, agriculture is extremely dynamic and conditions can change rapidly – with each cropping season and from one farm to another, making the process of formulating agronomic recommendations a challenge.

This is where a Geographic Information System, or GIS, comes into play. GIS is an applied science that analyzes information pooled from various sources at precision and landscape scales and enables evidence-based decision making. When used effectively, it can serve as a powerful, interactive tool that presents complex information in an actionable and simplified form, including as maps, graphs or reports. In combination with remote sensing, which involves the collection of information for a specific geographic area or object remotely – typically by satellite or aircraft – GIS can be used in diverse fields such as natural resource management, transportation and infrastructure planning. In agriculture, although GIS use is in a relatively nascent phase, it is increasingly being used as a basis for crop management and policy making.

CSISA relies on GIS and remote sensing for crop monitoring, area identification and technology targeting. Further, CSISA employs these technologies to complement monitoring, learning and evaluation activities being conducted on the ground. For example, when selecting areas to be surveyed to evaluate the impact of CSISA’s interventions on timely sowing of wheat in Bihar, GIS and remote sensing were used to analyze sowing dates across targeted locations in the state and accordingly classified as early or late sown. This enabled CSISA to ensure the selected sample represented the actual ground conditions and minimized variability or bias in the results.

CSISA is using geospatial technology to study in-field yield variability by using high-resolution satellite data, specifically targeting fertilizer application methods and their effect on crop yield. CSISA is also evaluating in-season adjustments of nitrogen and irrigation based on remote sensing data such as vegetation indices that show the relative density and health of vegetation and thermal band imagery that shows surface temperatures.

It is important to note, however, that much like any other science, the accuracy of GIS-based approaches depends heavily on the quality of inputs provided. For instance, satellite data plays a big role in GIS analysis. CSISA uses MODIS (Moderate Resolution Imaging Spectroradiometer) satellite images to analyze wheat sowing dates instead of Landsat imagery. This is because Modis has a very high temporal resolution but relatively coarser spatial resolution, meaning the satellite passes over the same area every 8 days but captures limited detail (where 1 pixel = 250 meters x 250 meters). Landsat, on the other hand, captures greater detail (where 1 pixel = 30 meters x 30 meters) but only passes over an area once every 16 days.

Other factors such as staff training, equipment quality, favorable weather for remote sensing, sampling plans with sufficient ‘ground truthing’ points, and availability of spatial and non-spatial information are all important to effectively utilize GIS application.

Further, while freely available datasets may be adequate at the landscape level – since data at that scale is largely aggregated – to achieve precision at the farm level paid imagery is crucial. Other challenges are those that may be said to be true for the agricultural sector in general – limited sharing of data between agencies, as well as constraints associated with copyrights, internal policies, and limited budgets for purchasing images and equipment.

GIS and remote sensing technologies have rapidly evolved, experiencing significant advancements in recent years. With the launch of micro satellites, for example, the tradeoffs between spatial and temporal aspects are reducing. Drones are facilitating a higher-level of precision in agricultural data by creating an opportunity for real-time monitoring. At the same time there has been unprecedented growth in open source platforms, which have made these technologies accessible even to small organizations. The United States Geological Survey, in fact, made Landsat images free to use for everyone – a major breakthrough in its own right.

How CSISA Uses Geospatial Technology

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  • CSISA used MODIS satellite data from the last 14 years to evaluate the trend of wheat sowing in Bihar and eastern Uttar Pradesh. By using satellite images and vegetation indices to understand the growth curve of crops, CSISA generated an algorithm to derive sowing dates. The resultant wheat sowing map indicates that wheat is generally sown earlier in the central and northwestern parts of the study area.
  • CSISA monitored field vacating dates after kharif harvesting using the same methodology. The resulting analysis helped CSISA identify which areas had the greatest potential for early sowing of wheat. This figure indicates that fields in the southwestern and north-central study area (the districts of Ara and Buxar) is usually harvested late and becomes available later for wheat sowing. These areas thus have the greatest potential for technologies that would enable early sowing.
  • Monitoring also facilitated CSISA’s efforts to convince farmers to sow wheat early in Bihar and eastern Uttar Pradesh. The encircled areas mark those targeted by CSISA, where early sowing has now been adopted.
  • CSISA also used GIS to identify kharif fallows in Bihar and eastern Uttar Pradesh. CSISA used MODIS images followed by vegetation indices and rigorous ‘ground truthing’ to define a specific threshold, below which one could identify the non-cropped areas. This data was cross-checked with previous years’ data to evaluate whether or not these were permanently fallowed lands. This figure shows that while some fallows shifted across the year, those in south-central Bihar were relatively fallowed throughout, possibly due to insufficient irrigation or other associated factors.
  • GIS and remote sensing also enabled CSISA to identify which areas in Bihar and eastern Uttar Pradesh had topographical limitations or other adverse characteristics that would undermine interventions on certain technologies. CSISA used digital elevation models, thermal bands and vegetation indices to generate a spatial model for identifying such areas. Analysis revealed that one of the challenges for early sowing of wheat in Bihar and eastern UP was the late drainage of water from rice fields. Farmers in these areas were opting for longer duration rice crops, meaning that early wheat sowing would not be feasible. The reason behind late drainage was primarily topographic. This figure depicts areas in the southwest that are usually drained late across years. These were identified as areas that were either low lying or where clay soils were responsible for water retention.
  • In coastal Odisha, CSISA supports the dissemination of direct seeded rice (DSR) technology, which would not be successful in waterlogged areas. CSISA needed to identify coastal areas prone to flooding during the monsoon. An in-season time series analysis helped identify these areas. This figure shows that with the onset of the monsoon in July, the north central areas were most severely affected – especially between the last week of July and early August – and that the water receded only after the last week of August, thereby making it unsuitable for DSR.

This article is authored by Amit Srivastava, GIS Specialist, CIMMYT.

Launch of New Geo-Informatics Tool

Posted on Bangladesh-news, India-news, News & Announcements, June 20, 2016

CSISA recently launched the beta version of the Landscape-scale Crop Assessment Tool (LCAT), a geo-informatics technology that will help scientists to forecast crop yields and identify regions where conditions will support the adoption of specific technologies. Using geo-informatics, for example, CSISA has in the past been able to identify districts in Odisha most prone to flooding and categorize them as areas ill-suited for direct seeded rice. LCAT will provide a platform for extension professionals, policymakers and research scientists to leverage geo-informatics for better decision-making. The tool was developed for South Asia but can be used globally.

“In the eastern Indo-Gangetic Plains, we promote early sowing of wheat, which is one of the most important adaptations to climate change. But we haven’t been able to accurately monitor and measure where it is being implemented and when,” explained Andrew McDonald, CIMMYT principal scientist and CSISA project leader. “In our line of work, it is crucial to understand where you’re making progress. While the data exists, it is often not integrated at the spatial level.”

Considerable environmental and man-made landscape diversity exists across South Asia. LCAT will help to analyze these landscapes and characterize large areas of land based on remote sensing data. It will serve two main purposes – to facilitate technology targeting and provide information such as crop status, phenology and yield goals to support crop management decisions.

“The first version of the tool uses datasets from CSISA sites in Bangladesh and India to characterize the existing cropland. However, the algorithms on which it is based are generic and can hence be applied to describe any dominant agricultural landscape across the globe,” said Balwinder Singh, CIMMYT crop simulation modeler. “Within CSISA, the tool will be used for specific applications extending to crop yield forecasting and monitoring, learning and evaluation.”

Participants in the LCAT training workshop in New Delhi, India.

Participants in the LCAT training workshop in New Delhi, India.

However, critical knowledge gaps between landscape-scale processes and technology targeting remain a challenge. To ensure policymakers and scientists are able to effectively collaborate in using this tool, a team of scientists from Oak Ridge National Laboratories (ORNL) visited New Delhi in May to conduct a training session on LCAT for CSISA staff and government partners from India and Bangladesh. The training not only demonstrated the tool’s beta version but also created greater understanding of its practical applications.

“If you’re a user of data, you spend 60 percent of your time just assembling data before analyzing it. We want to reduce that to 5 percent,” said Suresh Vannan, director of the ORNL Distributed Active Archive Center for Biogeochemical Dynamics and CCSI data theme leader.

LCAT is being developed in collaboration with ORNL and the Group on Earth Observations Global Agricultural Monitoring (GEOGLAM) Initiative. It is funded by CIMMYT as part of a five-year agreement with ORNL signed in 2014.

This article is authored by Ashwamegh Banerjee, Assistant Communications Specialist, CSISA. 

Improving Crop Management through Remote Sensing

Posted on India-news, News - Homepage, News & Announcements, July 16, 2015

Satellite technology provides invaluable data that allows scientists to observe growth trends, study yield gaps and target technology and inputs to increase agricultural productivity. A collaboration between CSISA and Stanford University, U.S., is exploring how remote sensing-based information can help increase wheat yields in the eastern Indo-Gangetic Plains (IGP).

Wheat is a staple crop in northern India, providing approximately 20 percent of household calories. India’s ability to provide enough wheat for its growing population over the coming decades, however, is uncertain given that wheat yields have stagnated and are predicted to decrease due to warming temperatures. Yet, farmers may be able to improve yields by altering their management strategies, like shifting sowing dates or more appropriately targeting inputs. Doing this may help narrow the existing yield gap; some studies estimate that wheat yields are approximately 50 percent of what could be achieved with optimal management practices.

David and Meha

In early March, David Lobell (first from right) and Meha Jain (third from right) from Stanford University, US, visited CSISA sites in Bihar. Stanford is acquiring high resolution remote sensing data for some CSISA sites to validate their yield prediction algorithm and CSISA is helping them acquire ground level data through crop cuts.

With the aim of improving agronomic management practices, Stanford University is working with CSISA to use satellite imagery to better understand the causes and spatial patterns of yield gaps across the eastern IGP and target and assess the impact of CSISA’s different intervention strategies, like the introduction of zero-till machinery and precision broadcasting of fertilizer.

Satellite imagery provides a wealth of data, with which can be used to map the characteristics of farmers’ fields, like crop type, sowing date and yield across space and time. The benefit of using remote sensing of satellite images instead of conventional data collection methods (like social surveys) is that it is a low-cost way to collect information over thousands of farmers’ fields over multiple years. This data can give a historical perspective of farming practices and insight into the heterogeneity among management practices and yields across a given landscape.

As the average size of fields in the region (approximately 0.3 ha) are typically smaller than the resolution of readily-available satellite imagery, like MODIS (250 m) and Landsat (30 m), it has been difficult to map field-level characteristics of smallholder farms in the eastern IGP. To overcome this challenge, Stanford is partnering with satellite companies like Skybox and Planet Labs, which are producing and providing high-resolution data (1–5 m). These high-resolution images will be used to map characteristics of individual farmers’ fields, as well as within-field heterogeneity. Field data from CSISA has been instrumental in testing and validating the models, which researchers at Stanford are currently using to estimate sowing dates and yields using satellite imagery.

Additionally this research will use the information provided by satellite data to help understand yield trends, identify where intervention strategies may best be targeted and measure the impact of various intervention strategies through time. Specifically, it aims to map the yield of wheat across northern India and assess what factors (such as weather, seed variety, sowing date) are responsible for changes in yield through time.

This partnership will also explore the use of satellite data to map key biophysical parameters of the agricultural landscape, which can lead to effective targeting of appropriate interventions. For example, a set of villages that are persistently low yielding compared to their neighbors can be provided with appropriate inputs to help close the yield gap and enhance the production of smallholder farmers.

Written by David Lobell, Associate Professor at Stanford University in Earth System Science, Deputy Director of the Center on Food Security and the Environment and Meha Jain, Postdoctoral Research Fellow at Stanford University in Earth System Science.


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