Module 5: Increasing Crop, Livestock and Fishery Productivity Through ICT
Table of Contents:
- Topic Note 5.1: Achieving Good Farming Practices through Improved Soil, Nutrient and Land Management
- Topic Note 5.2: Preventing Yield Losses Through Proper Plannign and Early Warning Systems
- References and Further Reading
Agriculture is a vital sector for the sustained growth of developing countries, especially agriculture-based countries such as those in sub-Saharan Africa. Equally important, a significant portion of the world’s population—86 percent of rural inhabitants—still depends on agriculture for employment and sustenance (World Bank 2007). Demand for food is increasing, too (box 5.1). The Food and Agricultural Policy Research Institute (FAPRI) estimates that an additional 6 million hectares of maize and 4 million hectares of wheat plus a 12 percent increase in global maize and wheat yields will be needed to meet demand for cereals alone in the next decade (Edgerton 2009). Demand for meat is expanding as incomes rise, creating competition for land and other resources. Increasingly unstable weather and temperatures require adaptive agronomic techniques to meet the demand.
The average maize yield per hectare in wealthy countries like Canada is three times higher than the average maize yield in low-income countries (FAO 2008). Growth in yields of rice, the primary staple for a significant number of developing countries, has stagnated in developing countries. Several regions, particularly East Asia, have seen rice yields decline by 10 percent owing to climate change. The factors contributing to low productivity are vast, including the coevolution of pests and pathogens, poor infrastructure, soil loss and degradation, waterlogging and salinity, the impact of climate change, lack of storage facilities, and weak markets. Low investments in agricultural research have reduced the scope for innovative thinking and technological development that could address these contributing factors and improve productivity.
Despite the dim outlook on meeting global food demand in a sustainable manner, successful social, economic, and technological developments have resolved productivity and population issues in the past and may hold some hope for the future. For example, over the past 40 years, annual global cereal production has grown from 420 million to 1.176 million tons (FAO 2000). In the 20th century, yields in the United States rose from 1.6 tons per hectare to 9.5 tons per hectare (Edgerton 2009). Similarly large increases occurred around the world from the mid-1980s to early 2000s, when cereal yields rose by more than 50 percent (World Bank 2007).
Box 5.1: The Food Security Challenge
The lack of food. Increasing agricultural productivity and access to food are the primary development goals of the 21st century. Demand for food has reached new heights, and predictions of future demand are discouraging. Although growth in global demand for cereals will slow in the coming 40 years, demand in sub-Saharan Africa will balloon by as much as 2.6 percent per year.a The food-insecure population in sub-Saharan Africa is also expected to increase by up to 32 percent by 2020, whereas food insecurity is projected to decline in Latin America and Asia.b Overall, the world will need 70–100 percent more food by 2050, when the population increases to 9 billion.c
The lack of nutrients. The lack of food is not the only problem. Almost one billion people were undernourished in 2010, and the lack of nutritious food has serious, long-term consequences for physical and mental health. More than one in seven of the world’s people do not receive enough protein and carbohydrates in their daily diets. These people constitute 16 percent of the developing country population.d
The rising prices. Even with projected reductions in food insecurity, price spikes could keep staple food out of the reach of poor people. The 2008 price spikes led to starvation in many countries, hitting the net food importers—typically the poorest countries—the hardest. Ethiopia, Malawi, Tanzania, and Uganda experienced maize prices that were twice as high as in the previous year. In Kenya and Mozambique, prices rose by 50–85 percent, according to the United States Department of Agriculture. Sharp and unexpected price spikes can provoke riots and political instability, aggravating an already precarious food situation. FAO recently predicted that the total costs of food imports would reach a near-record level in 2010, roughly US$ 1 trillion.e
The changing climate. Climate change has made the challenges of food security and rising prices even more stark. Continued release of greenhouse gases increases the likelihood of unpredictable weather and temperatures. The severe 2010 droughts and fires in Russia, Ukraine, and Kazakhstan raised wheat prices substantially, leading to grain embargos in multiple countries. Russia’s wheat exports fell by 13 million metric tons in one year.e Pakistan’s floods are another warning of the serious climate changes facing developing countries. The loss of soil nutrients that can accompany climatic extremes makes agricultural land less productive and adds to food insecurity. This prospect is ominous, considering the consistent drop in cereal yields over the last decade.f
Source: Authors; (a) Rosegrant et al. 2006; (b) Shapouri et al. 2010; (c) World Bank 2007; (d) FAO 2009; (e) FAO 2010a; (f) Raloff 2010.
Agricultural productivity rose around the world because more land was cultivated and more land was cultivated more intensively. Most of the gains were made through intensification. Agricultural land expanded by only 11 percent between 1961 and 2007 (FAO 2009), but between 1960 and 2000, genetic improvement and agronomic practices contributed to 78 percent of the increase in production (Lal 2010).
Bringing more land into production is infeasible, not only because of the growing number of competing uses for land but because of the environmental and social costs involved. The drive for agricultural land has often resulted in deforestation, reduced biodiversity, and provoked other forms of environmental degradation (Balmford, Green, and Scharleman 2005). It has also removed livelihood opportunities for some communities and elevated greenhouse gas emissions (Millennium Ecosystem Assessment 2005).
Given these constraints, development partners and governments alike continually seek ways to raise crop yields without using additional land. Raising yield per unit of land was observed during the Green Revolution of the 1960s and 1970s, when the use of new cultivars (shorter, higher-yielding varieties of wheat and rice) and improved practices (such as the use of fertilizer and irrigation) significantly increased crop yields throughout most of Latin America and Asia. A similar Green Revolution never arrived in sub-Saharan Africa but is sorely needed, given that almost all of the arable land is being cultivated (Govereh, Nyoro, and Jayne 1999).
Nonetheless, land can be used more intensively as well as more sustainably than in previous years, under innovative farming practices like precision farming, integrated pest management, agroforestry, and aquaculture (Burney, Davis, and Lobella 2010). Sustainable land intensification, in which yields rise but negative environmental impacts are curbed, provides a potential answer to food security and poverty reduction challenges. The sobering consideration, however, is that this type of intensification cannot occur unless 1.5 billion farmers—85 percent of whom farm less than two hectares—obtain and use these and other new technologies (World Bank 2007).
If the goal is to achieve sustainable increases in the global food supply and economic growth, it is important to ask who is responsible for producing food and commodities. Equally crucial, it is important to ask if they have access to technology, the knowledge to use it, and the purchasing power to acquire it (Pretty et al. 2006). The world as a whole, all regions, and all nations depend on farm households to provide food and by 2050 the world will ask farm households to supply double the current amount of food. Today, the farmers that the globe depends on are primarily smallholders with little access to technology, limited knowledge, and few financial resources. Notably, 43 percent are women (FAO 2011). Box 5.2 expands on why gender is a critical consideration in designing and implementing ICT for agriculture productivity.
Box 5.2: Gender in Agricultural Productivity
Exploring how gender disparities affect agricultural productivity is at the forefront of the development agenda. Women play significant and essential roles in agriculture in most developing countries. Their knowledge of local agrobiodiversity and conservation practices makes them prime assets in the sustainable intensification of agriculture. Women are also responsible for processing most crop and animal products and are often more involved than their male counterparts in high-value production. In addition, females play the chief role in care-taking, making them essential to household nutrition and children’s (especially girls’) education. It is widely accepted that women invest more regularly, and to a greater extent than men, in the well-being of future generations. These responsibilities add to a burdensome workload that involves time-consuming activities like fetching water and fuel.
Despite women’s key contributions to agriculture and rural development, they face major challenges in accessing inputs like land, improved tools, and financial services. Cultural, social, and political barriers prevent women from using their assets effectively in the field. Women are much less likely than men to purchase fertilizer or machinery. Women also have lower incomes compared to men: They receive smaller salaries in formal positions, earn less from their livestock, and are typically involved in seasonal, part-time work, if any.a As a result, their productivity is minimized and below that of male smallholders.
This situation represents a major challenge to increasing yields, because the majority of the world’s smallholders are female (75 percent in sub-Saharan Africa). Increasing agricultural productivity requires greater attention to gender differences and women in general. FAO asserts that if women had better access to resources, they could increase yields by 20–30 percent.a Development institutions should use ICT to address these issues—and of course make certain that women can access ICTs in the first place.
Source: Authors; (a) FAO 2011.
Given that the future of food depends to such a great extent on small-scale agriculture, governments and development partners are focusing on how to increase productivity in sustainable ways through new technologies that smallholders can use. Irrigation management, biotechnologies, pest management and eradication, soil assessment, improved nutrient and land management, improved market access, and innovative storage facilities are all strategies for increasing smallholders’ agricultural productivity and improving their access to markets, but the challenge lies in ensuring that smallholders can obtain and use them. ICT provides an incredible opportunity to reach farmers with the technical information they require to increase yields.
Linking Technology for Agricultural Productivity with ICTs
This module discusses two sets of technologies and the links between them:
• Yield technologies, like improved seed, crops developed through biotechnology, tractors, pesticide, fertilizer, and irrigation systems.
• Information and communication technologies, like geographical information systems (GIS), wireless sensor networks, data mediation software, and short message service (SMS).
Though they often work symbiotically at the farm level, and though both are often required to achieve the kinds of development goals discussed in this module, the differences between them need to be understood. Figure 5.1 helps to clarify them.
When farmers have access to biophysical and other yield-enhancing technologies, frequently they do not know how to use them effectively to address their productivity challenges (for example, they may have fertilizer but not know the optimal amount to apply). ICT can fill this gap in knowledge. Global positioning systems (GPSs), radios, mobile phones, digital soil maps, and other ICTs give farmers information to use biophysical technologies appropriately (for example, nitrogen sensors can help to determine the correct fertilizer dose).
Similarly, governments or development partners may know that farmers are using new yield-enhancing technologies but may not have the capacity to understand their impacts. Data-mining technologies, decision-support systems, and modeling software that can clarify the impacts and outputs of yield-enhancing technologies are among the most promising means of linking productivity and ICTs.
This module describes how farmers and governments can use ICTs in their strategies to increase agricultural productivity. The applications are quite broad: ICT can be used to monitor pest thresholds in integrated pest management, provide relevant and timely information and agricultural services, map agrobiodiversity in multiple-cropping systems, forecast disasters, and predict yields. Crop losses diminish as farmers receive relevant and timely information on pests and climate warnings through SMS technology.
Just as important, information can (and should) go both ways: Farmers can alert local governments or other relevant actors about serious crop developments like disease symptoms. This information makes it possible to avoid disasters more effectively and improves economic management, both of which are crucial for adapting to climate change.
ICT can also lead to more optimal use of inputs. Increasing producers’ knowledge of how to use and manage water, equipment, improved seed, fertilizer, and pesticide has improved the intensification of farm practices around the world. In the long run, and after collecting and analyzing multisite, multiyear data, ICT can be used to match cultivars to appropriate environments, increase the understanding of genotype-by-environment interactions, and adapt cropping strategies to the changing climate. Each of these applications increases the profitability of agriculture, reduces transaction costs, facilitates climate change adaptation, and improves livelihoods for the rural poor.
Strategies to increase yields (including strategies to avoid yield losses) include initiatives like soil nutrient assessments, weather forecasting, and crop or animal protection. The ICTs used to enhance these strategies are discussed in the topic notes.
Topic Note 5.1, “Achieving Good Farm Practices through Improved Soil, Nutrient, and Land Management,” focuses on soil testing technologies and tools that characterize field conditions, sometimes at a very fine level of detail. These technologies help farmers apply inputs appropriately and encourage the use of sustainable, profitable farming practices.
Topic Note 5.2, “Preventing Yield Losses through Proper Planning and Early Warning Systems,”focuses on how ICTs can be used to identify and control pests and diseases, improve access to timely weather information, and improve the design and management of irrigation systems.
Various examples and innovative practice summaries are included; it should be noted that most of these practice summaries come from pilot programs in Africa, where many studies and projects are currently underway. Discussions of lessons learned (covering cross-cutting themes, challenges, and key enablers) conclude each note. Finally, the broad ICTs discussed in this module fall into three categories. They are briefly defined in the sections that follow.1
Remote Sensing Technologies: Raw Data Collection
The first type of ICT that improves productivity includes tools that collect agricultural data:
- Geographical information systems (GIS) collect geographic data through computer hardware and software to capture, store, update, and display all forms of geographically referenced information by matching coordinates and time to other variables. Data sets formed by GIS constitute “layers” of information (for example, on topography, population size, or agricultural household income) that can be merged and analyzed to establish relationships and produce maps or charts that visualize geographical traits (GIS.com n.d.).
- Global positioning system (GPS) is a satellite-based positioning and navigation system with three basic components: satellites that orbit the earth, control and monitoring stations on the earth, and the GPS receivers owned by users. GPS receivers pick up signals from the satellites, including precise orbital information (latitude, longitude, and ellipsoidal GPS altitude) of a given object or location, as well as the time. GPSs can function in any weather and are free for public use (GPS.gov n.d.; GARMIN n.d.).
- Satellite imagery is an image of Earth taken from satellites in orbit. There are four types of satellite imagery: spatial (size of surface area); spectral (wavelength interval); temporal (amount of time); and radiometric (levels of brightness)—which capture a variety of variables about a given area of varying size. The resolution (in meters) of these images depends on the satellite system used and its distance from Earth; weather can interfere mainly with satellite systems utilizing visible wavelengths of light. The cost of the technology depends on the satellite system used, on whether new or archive imagery is purchased, and on possible georeferencing to a coordinate system.
- Aerial photography and orthophoto mosaic. An aerial photo is an image (once a photograph, now a digital image) of the ground taken from an airplane, helicopter, or radio-controlled aircraft at a given altitude. Aerial images are presented as an orthophoto mosaic that is an alternative to a map. These images are higher in resolution (deci-meter) than satellite images, proving useful for those who want more details of the terrain such as crop conditions or land use. In addition, modern digital aerial photography is georeferenced—that is, each point has geographical coordinates, whereas satellite imagery requires georeferencing to be geographically accurate and compatible with other geographical data (for example, in GIS) (T. Jantunen, personal communication).
- Laser scanning, or light detection and ranging (LiDAR), is an active airborne sensor using a set of laser beams to measure distance from an aircraft to features on the ground. Airplanes and helicopters can be used for laser scanning. The data from laser scanning are three-dimensional at very high accuracy, and they also allow ground elevation under the tree canopy to be measured. The elevation accuracy of laser scanning data is much better than aerial photography, which makes laser scanning useful for accurate topographic mapping where elevation is critical. The data can also be used to measure forest attributes such as the height and density of trees and thus the volume (aboveground biomass) of the forest(T. Jantunen, personal communication).
Information Management Technologies: Making Sense of the Data
The raw data collected above are fairly useless without analytical tools, both human and inanimate:
- Spatial modeling (among other models). Closely related to spatial analysis or statistics, models are an attempt to simulate real-world conditions and explore systems using their geographic, geometric, or topological properties. GIS (which can also perform analysis), among other ICTs, has increased opportunities to create models that predict occurrences like yield growth and ecosystem degradation.
- Data mining is the extraction of stories or patterns from large amounts of data. Data mining can find four major patterns: clustering (discovering groups), classification (forming a structure), regression (finding a function), and associations (finding relationships). These analyses help to make sense of agricultural data collected by remote sensors (Palace 1996).
- Data mediation is the process of taking many different data sets to produce a single, coherent set of information. Data mediation software organizes different types of data (such as hourly versus daily) and synthesizes different approaches to classification (for example, the use of different classification vocabulary), helping to mediate differences between data sources—particularly those on the Internet.
Dissemination Tools: Getting the Results to the Stakeholders
After analysis, the results must reach those who need to react to the findings, using tools like:
- SMS. Text options that allow interaction between fixed-line and mobile phones.
- Radio. Transmission of information through electromagnetic waves with low frequencies.
- WiFi. Wireless local area network that allows various devices to connect to the Internet remotely.
- Knowledge management system. Electronic system that provides relevant information as it is requested.
It should be noted that extension agents and advisory programs are essential to disseminate knowledge about the ICTs discussed in this module, but this issue is not discussed in detail here; see Module 6.
Key Challenges and Enablers
Increasing smallholder productivity is one the greatest tasks in this century. Although the dimensions of the challenge are huge (growing populations, growing demand for food, rising poverty, economic stagnation, worsening environmental degradation, and climate change), the growing number and sophistication of ICTs offers some hope of raising agricultural productivity, even in smallholders’ fields. Variable rate technology, GIS, GPS, satellite imagery, and other data collection technologies have increased the information available about soil health, weather conditions, and disease outbreaks, making very site-specific farming possible. The key to using these technologies to boost productivity is to remember that complementary technologies are needed: Data analysis technologies (such as data mining or mediation software) and information dissemination technologies (such as mobile phones and radio) are essential to reaching smallholders effectively. Dissemination also includes the crucial human component: Extension agents and farmers themselves must transmit and share knowledge.
As noted, productivity can be increased by expanding the land available for agriculture or by making the land already in use more productive. Given current global circumstances, it seems that the second option is more likely to close the productivity gap and meet demand. In conjunction with technologies developed to raise yields, the use of ICTs such as those discussed in this module may do just that. Mainstreaming the use of ICTs in agriculture will also enable them to be used more effectively. Integrating ICT into national programs, creating a policy environment conducive for ICT investment, and designing digital systems that are compatible and common can help improve access for users. Conducting impact studies and sharing pilot project information is also critical to success with ICTs, as more specific lessons and impacts are learned (IICD 2006).
In closing, it is important to emphasize that the benefits of ICT can be realized on multiple levels. As ICT capacities expand, local farmers and communities as well as nations and regions need to understand their potential uses to increase agricultural productivity. These stakeholders must learn how to tailor ICT solutions to macroeconomic needs as well as local agricultural bottlenecks, while exploring how current infrastructure can harness relevant and appropriate technologies.
Trends and Issues
Residue removal, tillage, overuse of pesticides and fertilizers, lack of crop diversity, overgrazing, overexploitation of natural resources, and deforestation have led to unhealthy soils and yearly reductions in crop output. Greenhouse gases worsen the situation. Changes in atmospheric temperatures (rising in most developing countries) reduce crop performance. Above 30°C, food and fiber crops develop at a faster rate, leaving less time for nutrient assimilation, biomass accumulation, and growth (Qaderi and Reid 2009). With lower yields and continued soil mismanagement, economic growth slows drastically. This outcome is seen most vividly in countries like Rwanda, Tanzania, Mozambique, and Niger, where costs associated with depletion of soil nutrients are estimated to account for 12–25 percent of the agricultural share of GDP (Drechsel et al. 2001).
Good farming practices maximize chances of a good harvest. In the past, conventional farming practices treated entire farms as homogeneous units even though they are often variable in productive potential. This view is changing as technology allows producers to measure soil nutrient status, crop potential, pasture health, and water-use efficiency at specific sites within a field. ICTs like digital soil maps provide extensive soil information that can be stored and accessed online. GPS, satellite imagery, remote sensors, and aerial images help to assess soil and land variations, and mobile applications and the Internet can disseminate the information quickly. With this array of ICTs, precision farming can be employed to optimize crop and livestock management. Until now, however, these techniques have been concentrated in highly mechanized, large-scale agriculture in industrialized countries.
Assessing Soil Properties for Climate-Resilient Agriculture
Accurate soil analyses and improved farming practices are needed urgently because productivity gains are highest in healthy soils and where pesticide, fertilizer, tools, and machinery are used properly. Instruments for mapping and analyzing soil properties have proliferated in the last decade, increasing farmers’ knowledge about the soils on their farms and the need for climate-resilient agricultural practices. The following section discusses these technologies and their associated challenges in broad terms. Subsections discuss innovative technologies specifically related to nitrogen and carbon, two essential chemical components for successful soil conservation and climate change mitigation.
Digital soil maps are the most promising applications for visualizing soil properties and the gravity of soil nutrient depletion in a particular area.2 The International Working Group on Digital Soil Mapping (WG-DSM) defines digital soil mapping as “the creation and the population of a geographically referenced soil database generated at a given resolution by using field and laboratory observation methods coupled with environmental data through quantitative relationships” (Rossiter 2004). A variety of technologies, including satellite remote sensors and cameras, can be used to survey soil and collect data to create digital soil maps.
These technologies collect soil information faster than methods that require scientists to take soil samples from the field. In the latter methods, 80 percent of the work on soil mapping is dedicated to soil identification and boundary mapping, and only 20 percent of the time spent in the field is left to gather data on more complex and equally important topographical features, such as water-holding capacity (Manchanda, Kudrat, and Tiwari 2002). Innovative data collection technologies allow researchers to focus on a variety of soil features (box 5.3).
Box 5.3: Using Remote Sensors and Similar Tools to Measure Soil Properties
for creating digital soil maps. Through near infrared and short-wave infrared sensors, satellites measure spectral reflectance in soils on the ground. Different materials reflect and absorb solar radiation at a variety of wavelengths (see the figure). As a result, remote sensors can measure soil color, texture (sand, silt, and clay), organic matter, moisture, salinity, and absorption processes by detecting and observing the solar radiation reflected (orbit sensing). Reflectance changes depending on the soil’s contents; for example, reflectance is low in areas with low silt content.a This technology gives researchers an accurate assessment of soil properties to use in GIS and computer modeling for digital soil maps.
Source: http://www.crisp.nus.edu.sg/~research/tutorial/optical.htm and (a) Hoffer 1978.
Practitioners can take the soil data collected from the technologies described in box 5.3 and use statistical methods, GIS, and soil inference systems to form “predictive soil maps.” These maps provide information on a soil’s capacity to provide ecosystem services (such as its capacity to infiltrate water, produce crops, or store carbon), geographical representations of soil constraints (such as aluminum toxicity, carbon deficit, or subsoil restrictions), and a baseline for detecting subsequent changes and assessing their impact (AfSIS 2009).
Digital soil maps give practitioners a good picture of soil fertility, vulnerability, and potential. Statistically testing soil maps against other data on human or policy variables (like demographics, land administration, farming practices, and climatic changes) allows researchers and others to explore causes of soil damage and forms of restoration.
At a national or regional level, models created from digital soil maps can be used to improve the selection of crops and varieties (based on which crops and varieties can withstand stressful soil conditions). They can also be used in early warning systems (predicting crop failure, for example), giving policy makers more time to react to shortfalls in domestic and export markets. In addition, fine-resolution soil maps collected from a number of regions could enable climatologists, hydrologists, and crop modelers to more accurately predict the effects of climate change or new technologies on food production and environmental health.
After soil data are collected, analyzed, and reflected in digital soil maps, results need to be shared with policy makers, scientists, and especially farmers, who would otherwise not have such detailed information on soil fertility in their respective farming communities. Recent developments in ICT increase the cost-effectiveness of soil maps: The spread of mobile phones and Internet access can transfer relevant soil information even to remote locations. Collaborating with extension staff, farmers, agrodealers, and others, development institutions can generate integrated soil fertility management schemes that improve a wide range of farming practices. Box 5.4 explains how these results can be applied.
Box 5.4: Collecting African Soil Data Over Time to Understand Soil Degradation Trends
The African Soil Information System (AfSIS) Project, led by the International Center for Tropical Agriculture (CIAT), collects data that will help address food insecurity and environmental degradation in sub-Saharan Africa. AfSIS takes advantage of recent developments in ICT—digital soil mapping, remote sensing, statistics, and soil fertility management—to analyze alternatives for protecting and rehabilitating soil. The project also tests a variety of farming techniques in an effort to discover the most effective methods to suit a wide range of conditions and situations. The soil map website and mobile networks help to ensure that the data collected can reach the complete spectrum of people involved in farming in Africa.
One objective of the AfSIS research, therefore, is to develop a baseline—an overview against which future results can be compared—using standardized tests and procedures. By applying an agreed process of sampling and analysis, scientists will build a comprehensive picture of soil health and degradation in an area of sub-Saharan Africa covering 42 countries and more than 18 million square kilometers.
It is well known that farmers in Africa typically use little fertilizer compared with farmers in the rest of the world. One important initiative in AfSIS investigates methods farmers can use to improve the fertility of their soils. The trials compare the effectiveness of different fertilizers used on a range of soils, the rate of fertilizer application, and the integration of leguminous crops in rotations.
AfSIS information will also be used in a wider international effort to produce a digital map of the world’s soil resources (the Global Digital Soil Properties Map Initiative). Scientists from soil information and agricultural development institutes in Mexico, Canada, and the United States work with the AfSIS team to produce the global map.
Source: AfSIS 2009; ICT Update, “Farming From the Ground Up: Scientists Use the Latest Technology to Produce a Digital Soil Map of Africa,” April 29, 2010 (http://ictupdate.cta.int/en/Feature-Articles/Farming-from-the-ground-up, accessed July 2011).
Challenges in Soil Mapping
Although technological developments have improved access to digital soil maps, major technological and economic challenges remain to be addressed in soil science and development institutions. Broadly speaking, the impacts and outcomes of using digital soil maps in smallholders’ fields have not been captured. Soil assessment techniques certainly contribute to the knowledge of production potential, but the transformative effects of this knowledge (such as the adoption of new practices) have not been tested empirically.Another technical challenge is that some digital soil maps cannot be used in quantitative studies or in models of food production or carbon management. Such studies generally require information on the functional properties of soils, such as available water capacity, permeability, and nutrient supply, which many mapping procedures do not capture. Finally, individual soil map units are shown as discrete polygons with definite boundaries. The data used in polygon maps are difficult to integrate with other forms of data, which are grid-based (like satellite images and digital elevation models) (Hartemink et al. 2010).
Social and financial challenges remain as well. Detailed yet inexpensive soil analysis tools are not widely available for small-scale farming in most developing countries, although they are being developed and piloted. Even where technologies are free to the public (like online satellite images), the resolution is too low to capture soil characteristics on individual plots. Without accurate, affordable soil analysis technologies, resource-poor farmers are unlikely to adopt sustainable and resource-optimizing farming practices. These practices are often more expensive in the short term and are typically more labor intensive. Finally, disseminating knowledge about soil management and farming practices is challenging. Soil science is complex. Soil restoration activities vary based on a diverse set of properties and the agroecological system. Even digital soil maps that create opportunities for soil assessment at the local level will require major dissemination and training efforts by extension staff and other stakeholders.
These challenges are being overcome as technologies advance. For example, GlobalSoilMap.net (along with others) is compiling data on digital soil properties around the world into a comprehensive global map, providing access to a consistent set of soil functional properties that define soil depth, water storage, permeability, fertility, and carbon (information needed for more quantitative studies). Placing maps online helps address some of the challenges related to dissemination and smallholder relevance. GlobalSoilMap.net can be used in a variety of ways to suit a range of purposes: users can view and manipulate the data online (for example, they can compare soil patterns with satellite imagery or land-use maps) or compose and print local maps by combining several sources of online data (soil, climate, terrain, and infrastructure, among others). Development partners, soil scientists, and governments then have a firm basis for formulating policies on land use and can share this information with farmers, so that they can make management decisions such as how much fertilizer to apply.
In addition to digital soil maps, which are useful over larger areas, nitrogen-sensor technologiesare used to manage nutrients and prevent the overuse or underuse of fertilizer at the level of a single field and crop. Ineffective use of nitrogen fertilizers can limit crop biomass production and diminish carbon content in the soil. Conversely, optimal nutrient management raises yields, improves soil health (including soil carbon storage capacity), and maximizes the cost-benefit ratio. An especially important consideration for smallholders is that reduced or more accurately timed fertilizer applications can lower the cost of investing in fertilizer (see “Improving Nitrogen Fertilization in Mexico”).
A key component of soil management is to maintain appropriate amounts of nitrogen in the soil to optimize crop growth and yields. Under certain weather conditions and farming practices, nitrogen applied as fertilizer, which is highly soluble, can be lost from the soil. Successful nitrogen management delivers enough nitrogen to the crop to optimize yield and profitability while minimizing losses to water and air. The timing, rate, and method of fertilizer application largely determine this optimization (Scharf and Lory 2006). Over the years, agronomists have established how much nitrogen various crops require. Using these measures, along with data collected from digital soil maps and other soil data, farmers can apply the right amount of nitrogen at the optimal time to maximize crop performance.
Farmers in developed countries use technologies that measure nitrogen levels and determine rates of fertilizer application. Evidence shows that sensors like the Yara N-Sensor (http://www.yara.co.uk/fertilizer/index.aspx) which measures light reflectance from vegetation and adjusts fertilizer application accordingly, can increase yields by up to 10 percent over standard farm practices while reducing fertilizer costs and minimizing environmental losses (image 5.1). N-tester, a technology developed by the same company, is another example of sensory technology for nitrogen. This portable device, using no subsidiary equipment, measures the chlorophyll content in the leaves of cereal and potato plants to monitor the need for nitrogen. N-tester is being piloted with high-value, nitrogen-demanding crops in a range of countries throughout northern Europe, southern Africa, and North America.
|Source: Yara International ASA 2004.|
The tools used for nitrogen-sensor technology have similar challenges to those of digital soil technology. Databases and information support systems have been established to raise awareness and disseminate information to smallholders, but widespread access is limited by the extent of network infrastructure and costs. Increasing the opportunity for communication among various stakeholders involved in farming (such as input dealers and extension agents) could improve the spread of information.
Soil Carbon Sequestration in Agriculture
The amount of organic carbon present in soil depends on water availability, soil type, and other features. A primary factor affecting the soil’s carbon content is agriculture. Soil carbon in forests, crop land, or grazing pastures increases or decreases depending on inputs that are applied, rates of deforestation, and farming practices. In recent decades, producers’ poor land management practices have reduced soil carbon content. When soils are tilled, organic matter previously protected from microbial action decomposes rapidly and accelerates erosion and degradation. Improved farming practices like leaving crop residues in the field after harvest and no-till (where seed is planted without plowing) maintain soil carbon at higher levels (Lal et al. 2004),3 but these practices are not widespread. No-till is practiced on only 5 percent of the globe’s cultivated land (Derpsch and Benites 2003). The overwhelming majority of vulnerable regions are those with lower organic carbon pools (figure 5.2). Click here for Figure 5.2 .
High levels of soil organic carbon are crucial to agricultural productivity and environmental conservation. Studies found that increasing the pool of soil organic carbon by 1 x 109 picograms of carbon per hectare can boost yields 20–70 kilograms per hectare in wheat, 10–50 kilograms per hectare in rice crops, and 10–20 kilograms per hectare in bean crops (Lal 2010). Despite rapid depletion of soil organic carbon, projections show that carbon can be restored to about 60–70 percent of natural levels. A calculation relevant to developing countries and poor producers is that they could grow up to 40 million tons of additional food grain if they increased soil carbon by only 1 ton per hectare. This productivity increase would be complemented by reductions in climate change and GHG emissions (World Bank 2010a).
For these reasons, increasing soil carbon in farmers’ fields, especially smallholders’ fields, is integral to agricultural sustainability and productivity. Soil carbon sequestration, or the process of transferring carbon dioxide from the atmosphere into the soil through crop residues and other organic solids (like mulch), is one technique to restore carbon levels in soils. This transfer helps offset emissions from fossil fuel combustion and other carbon-emitting activities while enhancing soil quality, water-holding capacity, and long-term agronomic productivity (World Bank 2010a). Carbon sequestration can be accomplished through farming practices and land management systems that add high amounts of biomass to soil while enhancing soil fauna activity.
Various technologies have been developed in recent years to measure, monitor, and verify carbon content and sequestration in agricultural land. The variability of sequestration is huge: observed rates of sequestration range from 0 to 150 kilograms of carbon per hectare in dry climates and 100 to 1,000 kilograms of carbon per hectare in humid areas (Lal 2004). This immense variability implies that monitoring and verification technologies are essential to carbon sequestration efforts, especially those that result in financial exchanges, like carbon markets. ICTs are currently used to measure soil carbon sequestration for large land spans. Digital soil maps are created (either through remote sensors, satellite images, or models) to measure and monitor changes in carbon content. In-field assessment methods, neutron-scattering techniques, and satellite normalized difference vegetation indexes (which use different tools to measure carbon pools from afar), as well as microwave sensors like JERS or ERSSAR, can measure soil carbon and other chemical components in the soil. Computer-based models are also employed to predict soil carbon content (Lal 2010). Most of these methods and technologies, along with free satellite data (such as that available through Landsat), are not detailed enough for small-farm monitoring.
Despite the growth in sensor and information technologies for carbon sequestration and restoration, significant barriers prevent smallholders from being included in efforts to monitor and increase carbon sequestration. They include the poor development of carbon markets to date, especially in agriculture, and the continuing problem of developing methods that smallholders can truly access and afford. See the discussion below.
Poor Carbon Market Development, Especially in Agriculture
Carbon markets were designed to provide incentives for carbon sequestration and good farming practices. Since 2002, developed countries and firms (primarily in Europe) have traded carbon credits (Lal 2004). Trading carbon credits can encourage firms and farmers to increase soil carbon content and switch to more environmentally conservative systems. Despite major strides in carbon market development, serious challenges remain. A variety of economic and scientific factors make it difficult to set prices for carbon credits, and assessing the biological and ecological relationship between carbon storage and climate change is even more daunting (Lal 2010; World Bank 2010a). Even more important, agriculture and livestock are not included routinely in global carbon emissions treaties, which reduce even large firms’ incentives to participate in carbon sequestration. The Clean Development Mechanism of the Kyoto Protocol does not include land management, which prohibits carbon in agricultural soils from being traded in the Kyoto compliance market (World Bank 2010a). Current efforts to include agriculture in carbon trade institutions and policies will create financial incentives for governments, firms, and farmers in developing countries to use soil carbon sequestration technologies.
Accessibility and Affordability of the Technology for the Poor
Beyond poorly functioning carbon markets, other technical and social barriers prevent smallholders from adopting practices that increase soil carbon levels. As noted, the ICTs used to monitor, report, plan, and verify the amount of carbon sequestered are not appropriate for small farms. Monitoring sequestration is easiest when the potential is large, or around 100,000 carbon tons (Bajtes 2001). This limitation is a major challenge to carbon sequestration, given that “90 percent of the potential for carbon capture can be found in the developing world, where land managers are largely poor farmers on small plots of land” (Smukler and Palm 2009:1).
Most available ICT not only inhibits smallholders from participating in carbon markets (or their development) but reduces their ability to participate in simple soil restoration and conservation techniques. Recent World Bank projects have shown that robust, clear, and cost-effective accounting methods that outline how carbon is measured and quantified are essential if projects designed for smallholders are to function well, as is transparency in monitoring to assure farmers’ participation (World Bank 2010a). In the future, development institutions can focus attention on reducing costs of ICT for soil carbon (using coarse-to-medium resolution satellite imagery)(Smukler and Palm 2009), improving land rights and enforceability (which will help regulate carbon trade), and determining how financial incentives might be created (for example, through local carbon markets or payment for ecosystem services) to ensure that smallholders can participate (box 5.5) (World Bank 2010a).
Box 5.5: Rewarding Farmers for Carbon Sequestration in Kenya
The Kenya Agricultural Carbon Project is one of the first examples of a soil carbon project that not only addresses issues like food security and climate change but also provides financial assistance to rural dwellers. Kenya is a prime candidate for carbon sequestration. Agriculture contributes to over 50 percent of gross domestic product and one-third of the country’s population lives on ecologically fragile arid land.
Funded by the World Bank and designed by the Swedish Cooperative Center-Vi Agroforestry, the project, located in Western and Nyanza Provinces, addresses most issues faced on arid land. On approximately 45,000 hectares of land, farmers adopt good practices that result in carbon sequestration. These practices are expected to generate 60,000 tons of carbon dioxide each year, increase yields, and allow smallholder farmers to access the carbon market and achieve supplemental income through payment of environmental services. Extension agents disseminate technical knowledge, monitor and account for carbon sequestered, and build capacity in farmers’ organizations.
Once carbon is sequestered, the credits will be sold to the World Bank’s BioCarbon Fund. Project developers expect that improved practices will result in an additional US$ 350,000 in 2011 for the communities involved. The project also promotes improved carbon management policies and strategies that improve agriculture productivity and sustainability at the national level.
Source: World Bank Ghana Office 2010; World Bank 2010d.
Perfecting the Farm through Precision Agriculture
Site-specific information that allows producers to make management decisions about discrete areas of the field is called precision farming or precision agriculture. Determining soil and crop conditions to improve whole-farm efficiency—while minimizing impacts on wildlife and the environment—is the crux of precision farming. It has been used successfully in many developed countries and has the potential to change agriculture dramatically in this century.
A variety of tools can be used in precision agriculture. GPS, satellites, sensors, and aerial images can help to assess variation in a given field. Farmers can match input applications and agronomic practices with information received from these ICTs. Precision agriculture has been applied to many types of agricultural produce (hay, pasture, fruit, and cereals, for example) and to fisheries under many different climatic conditions. Many of these efforts have been limited to large-scale farming because of the significant investment required, but applications under smallholders’ conditions are gaining visibility. Remote sensors, sonar-based technology, and other ICTs can also improve aquaculture and livestock production.
Essentially precision farming provides a framework of information for farmers to make management and production decisions. It can answer questions pertaining to land preparation (including tillage depth and type, residue management and organic matter, and reductions in soil compaction); seed (planting date and rotation, density and planting depth, cultivar selection); fertilizer (nitrogen, phosphorous, potassium, and other nutrients, as well as pH additives, application methods, and seasonal conditions); harvest (dates, moisture content, and crop quality); and animals and fisheries (pasture management, animal tracking, and school identification).
Precision Farming through Wireless Sensor Networks
Consistent advances in microsensing, smaller devices, and wireless communication (Kabashi et al. 2009) have resulted in new comprehensive technologies that offer even more consistent and reliable systems for smallholders and policy makers alike. Wireless sensor networks (WSNs), which combine many kinds of sensory data in one location, are some of the most innovative technologies available for farming and agricultural planning. With the right components, these networks can form knowledge management systems, research databases, and response systems that can guide local communities and governments in agricultural development.
A WSN is a group of small sensing devices, or nodes, that capture data in a given location.These nodes then send the raw data to a base station in the network, which transmits the data to a central computer that performs analysis and extracts meaningful information. The base station acts as a door to the Internet (typically a local area network), providing operators with remote access to the WSN’s data (Dargie and Zimmerling 2007). Because the networks can have multiple sensory devices, the data can contain information on soil, climate, chemicals, and other relevant subjects. The wide application of WSNs allows them to be used not only in managing agriculture but in testing water quality, managing disasters, detecting volcanic activity, and conducting environmental evaluations.
|Source: Kabashi et al. 2009. Note: DRK = Distributed Resource Kits GPRS = General packet radio service; KMS = Knowledge Management System|
These networks have several key features. First, WSNs have both active and passive sensors. Active sensors release a signal to detect a physical phenomenon like seismic activity and radar. Passive sensors, which transform a physical phenomenon into electrical energy, can detect a vast array of phenomena, including temperature, humidity, light, oxygen, and chemicals (Dargie and Zimmerling 2007). Once sensors (for example, temperature and soil moisture) are selected, node locations are needed. Node density in developing countries should be scarce to better guarantee network connectivity for each node, reduce maintenance, and improve the network’s reliability (though it will limit field-mapping techniques). In addition, because low-income countries often experience poor network and telecommunications connectivity, nodes will often require a “buffer,” where data can be rerouted or stored in another node if connection to the base station fails. If an active node fails to transmit data to the base, the network will “wake up” the closest neighboring buffer node (Kabashi et al. 2009), providing a “multihop transmission” (see figure 5.3 for a basic illustration of the process).
The design and implementation of WSNs requires a number of important features. The nodes should monitor the field(s) continuously and for a significant period—it is best if maintenance is not required for at least one cropping season (or 4–6 months). The nodes should cover a wide area, be small to prevent animal and human interference (like stealing), and tolerate harsh environmental conditions like monsoons and extreme heat. Self-organization is also important: The network should automatically detect removed or newly arrived nodes and adapt the messaging route (Depienne 2007).
WSNs offer extensive benefits to farmers producing plants and animals. Agriculturalists can detect problems at an early stage and use more precise applications of fertilizer, water, and pesticide. Pastoralists can use WSN to monitor grazing land productivity. Placing wireless nodes in pastures allows farmers to move animals according to environmental indicators like soil moisture (see image 5.2 and IPS “Monitoring Livestock to Prevent Pasture Damage” in Topic Note 5.1). WSNs can also be used to manage irrigation and even to measure water quality.
Governments and development partners also benefit financially from WSNs. The technology is fairly cheap; some units cost as little as US$ 100 (Dargie and Zimmerling 2007). Developing countries often experience power deficiencies, but nodes that operate on batteries and alternative energy sources do not need electricity. Data are collected more easily. Whereas traditional methods of collecting agricultural data for national planning rely on occasional data logging by human operators, WSNs can collect continuous data with minimal human interaction. Even though some ICTs like mobile phones or transceivers can collect information faster in the field, they often have trouble cooperating with other software or Internet servers (Fukatsu et al. 2004). WSNs integrate the Internet into the software, making the data more user friendly and accessible.
Data organization is vital to the output of WSN as well as other remote technologies. If countries want to use WSN data to construct yield models or predict climate shifts, making sense of the data is pertinent to the design. The data produced can be used to improve crop management strategies and even develop knowledge management systems where best practices, crop disease identification, and planting techniques can be disseminated to smallholders. It is important to note, however, that although battery-operated nodes can function in areas with low power connections, changing batteries in remote areas may prove difficult. Sleep settings and well-designed energy-conserving hardware can help prevent frequent battery changes (Dargie and Zimmerling 2007).
|Source: Curt Carnemark, World Bank.|
Wireless sensors can also be used in aquaculture. Though concentrated in developed countries, the use of underwater wireless sensors has great major potential for developing countries. Real-time information is crucial to effective and profitable aquaculture. Akvasmart (see website), a Norwegian firm specializing in commercial fish farming, uses a wide variety of ICT tools, including sensors. Sensor systems can monitor oxygen, tidal current, temperature levels, fish behaviors, and water conditions. Interestingly, Doppler pellet sensors with a built-in camera can detect uneaten food in fish cages (figure 5.5). With this information, signals from the sensors can stop the feeding, allowing for more specific care and feed purchase. The sensors can also adapt to the accurate feeding rate of the fish over time.
Wireless sensors in water, just like those on land, can be coupled with other cameras for more precise readings. Akvasmart offers a video image system called the Vicass Biomass Estimator that measures the height and length of the fish in the pond. These figures can be used to estimate the weight of the fish. Other camera systems can be placed at the surface or underwater. Monochrome cameras monitor the feeding process by “looking up” from the bottom. Color cameras can monitor feeding and inspect the pond or cages and surrounding environment. Remote access cameras can tilt, zoom, and pan according to the interest of the fish farmer. Each of these camera and wireless sensor systems can be accessed from a personal computer and in some cases the Internet, where the data are collected.
|Figure 5.5: Akvasmart Doppler Pellet Sensor Network|
|Source: Akvasmart (http://www.akvasmart.com/index.cfm/?id=205636 Note: CSU=Cage Sensor Unit|
Precision Farming through Satellite Technologies
Precision farming through satellite technology utilizes three technologies: GPS (which can position a tractor within a few feet in the field), GIS (which can capture, manage, and analyze spatial data relating to crop productivity and field inputs), and variable rate technology (which provides site-specific, “on-the-fly” estimates of field inputs for site-specific application). The three ICTs combined provide information that allows producers to apply inputs, such as fertilizer and insecticide, precisely where they are needed (figure 5.6).
|Figure 5.6: Precision Farming through Satellite Technologies|
|Source: GIS Development Net|
Agricultural information is typically captured spatially, making it more convenient to handle on a regional scale.GIS technology is promising because it allows for a more specific focus. Variable rate technology has helped to identify weed infestations and water stress in areas where crop pest levels are high, which improves targeting of chemical applications and reduces waste associated with conventional blanket spraying (Munyua 2007). In addition to the potential productivity gains and cost savings, precision farming through satellite technology enables governments to study how agricultural practices affect the ecosystem and develop better regulations.
Once data are collected through GIS, scientists can interpret the images and analyze the soil and crop conditions to achieve better results. Although satellite imagery cannot detect soil quality directly like sensors can, it can record soil properties like light reflections and color. As crops start growing, precise pictures of the crops are captured more efficiently. The condition of the fully grown plants can then provide a clearer picture of the quality of the crops and what they require for successful harvest.
Based on soil and crop conditions, farmers can estimate the precise amounts of seed, pesticide, and fertilizer they need, organize the distribution of inputs, plan which crops to plant in which areas, and make new investments.Knowing the size and shape of fields can also help rural communities plan for future developments and investments like mechanization. Small, fragmented, or awkwardly shaped fields are difficult to work with a tractor or even animals. Above a certain minimum field size, it becomes cost-effective to use a tractor. Precision farming provided through satellite imagery can determine this threshold before a community invests in new equipment. If an area is suitable for mechanization, the benefits can be extensive. A GPS system that controlled tractor steering in Sudan cut planting time on the farm by 60 percent (Munyua 2007).
Precision farming must also rely on an information dissemination process. Many rural areas in developing countries are isolated from sources of new agricultural information; not surprisingly, farmers in these areas use few modern technologies. ICT is beginning to play an important role in providing advisory services in real time to farmers, which helps them plan and manage production, postharvest activities, and marketing more efficiently (see Module 9). Online information, consultation, and land suitability maps with web-based systems can play an important role in improving and updating knowledge for producer organizations.
Management and information-sharing tools are also necessary for effective precision farming based on satellite technologies. RiceCheck and the online knowledge bank at the International Rice Research Institute (IRRI) (http://irri.org/knowledge/training/knowledge-bank) are two of the most advanced knowledge management tools in rice production today. Collecting, analyzing, and sharing information on individual plots has been difficult, but through RiceCheck, farmers can now monitor crops, have an online group meetings (often with agronomists), and compare their yields to regional benchmarks for high yields (for a description of these benefits in Malaysia, see box 5.6) Through IRRI’s site, connected farmers can also make a checklist for their daily activities and review plans for the entire growing season.
Box 5.6: Web-Based GIS for Paddy Precision Farming, Malaysia
In Malaysia, an interactive, web-based GIS provides information for precision farming and mapping in the Sawah Sempadan rice-growing area in Tanjung Karang, Selangor (Che’Ya et al. 2009). The system allows farmers to access information about rice cultivation in their area. Because it uses open-source software, the system is cost-effective for users. Farmers can print variable rate fertilizer application maps and historical data about yield per paddy lot in previous seasons. This helps farmers analyze and reflect on the best strategy for the coming growing season. Farmers can share information, especially on fertilizer recommendations. A web presence also allows policy makers to access rice information.
This Topic Note primarily reviews soil and land productivity, particularly for the planning and preplanting stages of the production cycle. Correcting past damages and ensuring future yields, however, will require farmers, governments, and development partners to mitigate the effects of climate change and environmental degradation on soils. With the expanding reach of ICTs, achieving this goal is more likely in both developed and developing countries, but challenges remain in using ICTs to improving soil and land health. They are discussed in the following paragraphs, along with some means of preventing or overcoming them.
To begin with, these technologies are relatively new even in developed countries, and their potential is just being realized in developing countries. National awareness of the importance and benefits of soil fertility takes time to develop. As with carbon sequestration, ICTs to improve and maintain the fertility and productivity of land will require new legislation and policies outlining their use and providing incentives to achieve their benefits. Appropriate legal and regulatory frameworks, monitoring and verification systems, and liability, access, and property rights laws and regulations, such as regulations on carbon limits in some countries, are necessary to make significant, national progress. Though not all technologies require such stringent legal frameworks, government involvement—specifically at the national policy level—often raises the visibility and adoption rates for new ICTs.
Testing methods for soils vary and are still in development. For this reason, results are not always reliable and may be difficult to harmonize. Continued research—particularly in poor countries where research is typically limited—will help to address these challenges. Developing countries also lack the financial footing and human capital to use expensive technologies that require reliable operation and maintenance, even more so in harsh conditions. Strategic and long-term investments are needed to sustain improvements in soil and land productivity, especially if they are used in rural areas, where farmers who may be required to maintain the ICTs have little time to do so.
Farmers may not have a contemporary perspective on the environment because they have received little new information. They may not have access to the country’s environmental regulations (for example, prohibiting the burning of charcoal) or export requirements (such as limits on pesticide residues). Extension education and campaigns through ICTs like radio will help farmers to make decisions related to environmental policies and strategies.
Despite the benefits of soil technologies, smallholders have limited access to credit to use them. Even if they have access to soil maps or nitrogen estimates, their adoption or adjustment rates might be low. The inputs required to change practices are often out of reach in poor rural areas. New credit insurance schemes or financial rewards (like carbon markets) may reduce these monetary concerns.
Soil ICTs are not only new but complex. Farmers will require training and education to learn how to use them. Electronic education (e-learning) is an option, but infrastructure must be considered. In some cases, technologies function well with low bandwidth (WSNs are one example), but in others they do not (the RiceCheck web interface is an example). The productivity goals and the technologies used to meet them must match the IT capacity in the focus location.
Finally, the lack of institutional capacity poses other challenges for increasing soil and land productivity. Governments that want to incorporate the use of carbon markets or digital soil maps into agricultural policy will have to make major adjustments and investments in human resource capacity. Development partners like the World Bank can support some of these efforts.
INNOVATIVE PRACTICE SUMMARY
Seeing-Is-Believing Project Improves Precision Farming
Small-scale farmers in West Africa are experiencing unpredictable changes in their agricultural land. Soils are infertile in many areas, reducing agricultural productivity and spurring fear and uncertainty about future livelihoods among farmers. In the past few years, many West African farmers have abandoned their land, which had been in their families for generations.
It is imperative that smallholders obtain the knowledge about changing soil and crop patterns that can help them manage their farms. The Seeing-is-Believing West Africa (SIBWA) Project has been assisting farmers with accurate satellite information and imagery of their farm fields to help them improve their agricultural practices.
In June 2009, SIBWA started working with six farming communities in this region—three in Mali and one each in Ghana, Burkina Faso, and Niger. SIBWA is funded by the Bill and Melinda Gates Foundation through AGCommons, with supplementary funding from the United States Agency for Internal Development and Germany’s Federal Ministry for Economic Cooperation and Development (CODE-WA project).
Led by scientists at the International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), the SIBWA team provided farmers with very high resolution satellite images (such as those on displayed on Google Earth) of their land. To get a more precise picture of soil fertility, scientists can analyze the images when the crops are at their peak growth stage. When the ICRISAT team acquires a very high resolution image (VHRI), they use computer software to enhance it, adding extra layers of information, and analyze data that would be useful to farmers, such as variations in soil fertility, land size, and shape. Although a single VHRI image costs US$ 1,000–1,500, this method of analysis is often still cheaper than visiting every individual farmer’s field to collect samples. Partnering with local NGOs and extension officers, the SIBWA team visits the project sites to verify their findings with the farmers. ICRISAT further analyzes the images using feedback from field research to build a database that they can use to develop an accurate map of each farm.
SIBWA partners translate the soil and image information into local languages and take the detailed maps back to individual farmers, who can use them to plan and manage their crops for the coming season (image 5.3). The maps show areas of low or high fertility inside each field. With an overview of soil and crop conditions, farmers can organize the distribution of fertilizer throughout their fields and estimate which crops will produce the highest yields. The SIBWA team works with the farmers to determine the area of each field, making it easier for farmers to calculate the amounts of seed, pesticide, and fertilizer required for each field.
Another advantage of VHRI is that it shows the direction of furrows on the field and areas where farmers can plow along the contour lines of the land. Using this imagery, farmersmonitor whether they were following the contour lines accurately and efficiently to reduce soil erosion. SIBWA also involved local NGOs specialized in technology and extension services in each community to help farmers make use of the data.
|Source: Work funded by AgCommons a program executed by the CGIAR|
Data from projects like SIBWA can be used to develop growth and yield models by various means. Some rely on computer simulation and include weather-related variables; others are statistical estimation models based on multiple regression equations. While no single model has proven satisfactory in all conditions, both low- and high-resolution imagery have benefits extending beyond the decisions of individual farmers. Low-resolution yield prediction can benefit food importers and exporters as well as international and government agencies concerned with global markets and prices. In this regard, data collected from imagery in localized projects will be useful in years to come. Although it remains too early to analyze the impacts of SIBWA, the team expects that the farmers will use the data when planning for the new growing season (Traoré 2010; ICRISAT 2010).
INNOVATIVE PRACTICE SUMMARY
Improving Nitrogen Fertilization in Mexico
The International Maize and Wheat Improvement Center (CIMMYT) recently piloted a nitrogen sensor on 174 wheat plots in Mexico’s Yaqui Valley, in collaboration with the State of Sonora, Oklahoma State University, and Stanford University (image 5.4). A handheld device with an infrared sensor captures light to measure biomass and red wavelengths to measure chlorophyll content. These two measures determine how much nitrogen a plant requires and thus the appropriate amount of fertilizer to apply (CIMMYT 2005).
In Sonora, farmer-advisors purchase the sensors for US$ 5,000 and charge 7 pesos per hectare to diagnose farmers’ crops (I. Ortiz-Monasterio, personal communication). Though the cost of the diagnosis is expensive for smallholders, they needed significantly less fertilizer to maintain yields. Farmers who did not use the sensor applied 219 kilograms of nitrogen per hectare for yields of 6.92 tons per hectare; those who used the sensor applied as little as 158 kilograms of nitrogen per hectare for yields of 6.91 tons per hectare. For a 100-hectare farm, these savings add up to approximately US$ 7,500 per harvest (CIMMYT 2007).
Image 5.4: Infrared Sensor Technology Increases the Cost-Efficiency of Nitrogen Fertilizer Applications in Yaqui Valley
|Source: Iván Ortiz-Monasterio, CIMMYT.|
The technology not only reduces costs but reduces environmental damage: Nitrogen that washes into the ocean or local streams can harm ecosystems. CIMMYT is now working on a prototype pocket sensor that costs US$ 100–200, which would facilitate more affordable nitrogen testing services for farmers in developing countries (I. Ortiz-Monasterio, personal communication).
INNOVATIVE PRACTICE SUMMARY
Monitoring Livestock to Prevent Pasture Damage
Animal production in Australia traditionally required animals to be restrained to a particular location. The cost of installing fences and maintaining them constitutes around 30 percent of the cost of rearing one animal. Controlling animal location implies that farmers need to know about pasture conditions, because overgrazing leads to land erosion and nutrient depletion. With this in mind, researchers implemented a static and mobile node and camera network to remotely monitor the condition of grass throughout a field.4 Using solar panels, which generate much higher energy outputs compared to what is needed, the team observed soil moisture, greenness level, grass height, and grass coverage.
Consisting of an Atmega 128 microcontroller at 8 MHz, a Nordic NRF903 radio transceiver with a bit rate of 76.8 kilobits per second, a temperature sensor, and a soil moisture sensor, the commercially available static node (ECH20 capacitance-based) takes readings every minute with a ±2 percent error rate. Pictures of the pasture, troughs, and gates help to guide herdsmen in cattle movement. Additional mobile nodes connect directly to the cattle (around their necks). These nodes measure the livestock’s speed and turning rate, which improves tracking capacity.
With these two technologies, scientists can build generic models of herd movement so that herdsmen can better manage resources in smaller pastures. Though the technology is focused on developed countries, these ICTs hold great potential for developing countries.
Trends and Issues
ICTs can help to prevent and reduce losses in crops through well-planned investments and disaster warnings or time-sensitive alerts. Water management and disease or pest prevention are crucial to increased productivity. Advances in ICTs such as GPS, GIS, mediation software, mobile phones, and satellite imagery have improved smallholders’ ability to adjust farm strategies and reduce risk. At the same time, these advances allow governments and development partners to better monitor farm productivity, make more accurate projections, and plan better for the future.
Water is a primary topic in this thematic note. Although water is scarce and is becoming more so due to climate change, many water resources in developing countries are simply not exploited. In fact, the vulnerability facing agriculturalists in most of Africa is not the result of more variable rainfall but of failure to access the water that is available. Only 2–3 percent of Africa’s water is used (Woodhouse 2009). Despite current efforts to tap water resources and adapt to climate change, competition for water for household and industrial use will steer water away from agriculture over the next few years in almost 60 percent of the world’s most vulnerable countries (Ruttan 2002). Weather data, along with improved irrigation management and system engineering, are more important than ever.
This note also discusses disease and pest control. Pests and pathogens continually evolve, making it particularly difficult for small-scale farmers to increase productivity. Without inputs like pesticides and the knowledge to use them correctly, pests and diseases reduce global harvests by upwards of 30 percent for maize, rice, and potatoes (Oerke 2006). ICTs like mobile phones and radio frequency identification technology are making it easier for farmers to know which diseases or pests to watch for and how to handle them if they are found. Pest eradication takes national and collective efforts. With ICTs, governments find it easier to reduce crop losses from flies or rodents and livestock losses from disease like bovine spongiform encephalopathy (less formally known as “mad-cow disease”).
Preventing Disease and Pest Damage
Plant protection is important to save crops from diseases and pests. Increasingly, ICTs are used to help farmers reduce or more efficiently use the total amount of pesticides employed in crop protection. Farmers often are unaware of or cannot accurately assess plant diseases, which may reduce agricultural productivity and raise costs if pesticides are overused. Concerns for animal health are similar. Herdsmen and fishermen spend resources and time treating sick animals or identifying disease outbreaks. Using a variety of ICTs, producers can better identify, track, and protect their crops, animals, and livelihoods.
One example involves fishing communities, which face major challenges in both wild and managed fisheries. They can use ICTs to prevent fish diseases and protect local fishing grounds from unwanted visitors. Illegal, unregulated, and unreported fishing poses serious obstacles to sustaining fish production. Tools like GPS and mobile phones help fishers and governments locate poachers and report abuse (image 5.5). The South Pacific Forum Fisheries Agency, for example, now has a vessel monitoring system, which observes fishing grounds throughout the area, identifying and fining illegal fishers. The Sustainable Fisheries Livelihoods Program has helped Guinean fishing communities perform similar policing: local fisherman used hand-held GPSs to calculate the position of poachers and then radio them to the coastguard. Benefits of these technologies improve productivity indirectly by protecting the fish population. In Guinea, for example, incursions by industrial criminal vessels went down from 450 to 81 after just two years (FAO 2007).
Protecting farm animals from disease and other ailments also improves through ICT (see IPS “Radio Frequency Identification to Prevent and Treat Cattle Disease in Botswana” in Topic Note 5.2). Sensors and other remote technologies can be implanted in an animal, providing herdsman with the exact location, health, and situation for livestock like cows, pigs, or sheep. In addition to enabling easier identification and tracking, in the future, some instruments may offer animal response systems.
|Image 5.5: Mobile Applications Help to Monitor and Protect Fishers|
|Source: Edwin Huffman, World Bank.|
ICT is now being used in integrated pest management systems to improve farm management in a variety of ways. The Low Frequency Array Project (http://www.lofar.org) piloted in the Netherlands uses sensors to monitor and treat potato crops at risk for the fungus Phytophthora infestans,which causes late blight. Because the development of late blight depends heavily on climatic conditions (OECD 2009), capturing climatic conditions like humidity and leaf temperature can help farmers prevent onset of the disease by optimizing fungicide applications when climatic conditions warrant it. The project used three instruments: sensor nodes, a server, and a decision support system. One hundred and fifty sensor nodes, called TNodes, send soil information every 10 minutes through a TinyOS operating system to the server where data are stored (Baggio 2004). Users can access this information directly, or receive texts or emails from the linking decision support system (LOFAR n.d.). The decision support system gathers information from the server along with other meteorological data from weather stations to produce maps of the temperature distribution within fields. The system sends alerts to the farmer that identify the patches of land most susceptible to the fungus.
Information technologies are vital for disseminating crop protection advice, but “crowdsourcing,” (using ICTs to leverage widespread collaboration) can prevent diseases from spreading in the first place. If sufficient numbers of farmers can text information on potential crop disease symptoms to researchers and receive appropriate disease control advice, researchers can also track and potentially forestall epidemics. If farmers or cooperatives have access to the Internet, online bulletin boards or mailing lists can spread information on disease incidence quickly. Online decision support systems5 that link data to possible action, such as the one used in the Low Frequency Array Agro Project, are becoming more popular because clients require minimal software, which reduces management and distribution costs.
Additionally, it is useful to link weather information to pest or disease development over time. The Pacific Northwest Integrated Pest Management website through Oregon State University (visit here) collects temperature and precipitation data from 380 weather stations and links it to pest phenology models for 22 insects, 2 diseases, and 2 crop species (Bajwa and Kogan n.d.). Pest alerts and control techniques are announced and shared through social media like Twitter and email subscriptions. Similar alerts can be carried out through SMS in developing countries (box 5.7).
Box 5.7: Crowdsourcing Prevents Cassava Losses in Tanzania
In Tanzania’s Lake Zone, farmers from 10 districts who participate in the Digital Early Warning Network have been trained to recognize symptoms of cassava mosaic disease and cassava brown streak disease. Armed with mobile phones, farmers “crowdsource” or send out monthly text messages to researchers about disease incidence and receive disease control advice in return. When more than 10 percent of the members of a group spot a disease that was not present previously or has increased in prevalence, the project team visits the area to verify the information and advise farmers what to do. Each group of farmers—60 overall—is given a topped-up phone card to text researchers. They meet monthly to discuss observations and send the text messages. The network is part of the Great Lakes Cassava Initiative, which aims to improve the livelihoods of more than a million farmers in six countries of the Great Lakes region by tackling issues that affect cassava yields.
Source: Ogodo 2009.
Since 2000, new ICTs have given farmers and partners better opportunities to manage climate risk. WSNs and satellite images capture raw data that can be transformed into information useful for agriculturalists, helping them optimize decisions related to choosing crops (based on water requirements), planting (timing and planting density), buying inputs, and applying fertilizer. Climate information can also improve insurance markets.
Remote sensors are presently the chief source of climate data. FAO’s Global Information and Early Warning System on Food and Agriculture tracks data and trends related to food security, price risks, and natural disasters. FAO analysts monitor climate conditions and changes around the world using four satellites—FAO’s ARTEMIS (Africa Real Time Environmental Monitoring Information System), Europe’s METEOSAT, the United States’ NOAA (National Oceanic and Atmospheric Administration), and Japan’s GMS (Geostationary Meteorological Satellite).
Every 10 days, ARTEMIS and METEOSTAT provide images that help to estimate rainfall for Africa. FAO maintains a database of these images from the past two decades, which provides an opportunity to monitor significant changes in weather over time (image 5.6). GMS produces similar information for Southeast Asia as well as information on crop densities at the subnational level (FAO 2010b). Beyond reflecting past trends and predicting future ones, these satellites and others can provide up-to-date forecasts for farmers. These satellite images and others are free on FAO’s website. (Click here to view Image 5.6 ).
This proliferation of weather information has made mediation software extremely relevant to the productivity discussion. For example, MetBroker (http://www.agmodel.org/projects/metbroker.html), software that pulls weather data from various sources and “hides” the differences between them, is run on a computer permanently connected to the Internet. From 5,000 stations from 14 databases in 7 countries, MetBroker averages forecasting data and makes it consistent (Laurenson, Otuka, and Ninomiya 2001). This approach has two benefits: Researchers and modelers can access data from various harmonized sources for growth prediction models, and farmers can receive accurate real-time weather information to make farming decisions. Clients—whether farmers or modelers—can request a wide array of climate-related information from MetBroker, including rainfall prediction, air temperature, solar radiation, soil temperature, and leaf wetness (Laurenson, Otuka, and Ninomiya 2001). Some mobile technologies permit farmers to access MetBroker and request information on weather conditions for a certain region, specific stations, and for a restricted period, even with low bandwidth. MetBroker provides an option for summarizing data as well; users can opt to receive daily temperatures instead of hourly ones or receive expert summaries of weather information instead of complete data sets.
Another weather forecasting service, this one in Turkey, relies on simple SMS information to help farmers prevent losses to frost and pests in their orchards. Prior to the project, producers could not obtain weather information on time to cope with conditions that might harm their orchards. See the IPS, “Weather Forecasting Reduces Agricultural Risk in Turkey,” in Topic Note 3.1 in Module 3.
Mediation software was also essential for modeling groundnut yields in India (box 5.8). Among other things, the models can help identify the best times to plant to evade drought.
Box 5.8: Modeling India’s Groundnut Yield through Climate Information
In India, rainfed agriculture supports more than 60 percent of the population. In the semiarid Anantapur region, rain typically falls from May to November, yet it varies significantly from week to week, resulting in frequent wet and dry spells. If a dry spell occurs at a critical planting stage, groundnut yields decrease significantly.
Attempting to identify the most promising planting times, researchers used the PNUTGRO model to simulate groundnut growth and yield. The model included vegetative and reproductive development, carbon balance, nitrogen balance, and water balance. The team collected climate data from the Anantapur Agriculture Research Station, which has maintained records since 1962. Using maximum and minimum temperatures, radiation, and rainfall data over three decades, they found that the period between July 15 and August 10 is associated with very high yields. Even more important, planting in two additional periods was also associated with high productivity, suggesting that missing the earlier planting time does not mean that yields will be low for the entire season.
Like all models, this one is limited: it cannot be used to assess the profits or risks associated with management strategies in times of crisis (like the El Niño weather pattern). Nonetheless, analysis of yields associated with different climatic conditions can help to improve farming strategies for specific seasons and raise red flags for potential weather disasters after investments have been made.
Source: Gadgil, Seshagiri Rao, and Narahari Rao 2002.
Other forms of electronic weather information have potential to increase productivity, primarily by reducing risk. Many of these systems are being tested in OECD countries. eWarning (http://www.landbrugsinfo.dk/Planteavl/Sider/pl_11_543.aspx) was created through PlanteInfo (www.planteinfo.dk), a Danish initiative supporting decision making in national plant production. eWarning provides farmers with real-time weather information sourced by the AgriMeteorological Information System and Danish Meteorological Institute. In this particular system, weather information, including precipitation and temperature, is divided into 10-square-kilometer plots to provide farmers with specific climatic details on specific plots.
In eWarning and other systems, farmers request information through SMS in two forms. Push-type messages are regular, automatic updates obtained through a user subscription. Pull-type messages are sent only when a user requests them. When the user sends a letter (like “P”) in a message, the eWarning system will respond with information on precipitation for the user’s geographical location.Surveys show that the push-type message is most popular, providing farmers with an hourly forecast up to four times per day (Jensen and Thysen 2003).
A Yakima software firm, in partnership with Washington State University, is customizing a weather website for specific locations to provide weather alerts to farmers in the United States. These alerts include frost warnings, wind speed with recommendations for pesticide spraying, and information on disease outbreaks. After a farmer has registered for the service online, he or she can request information and specify the method to receive it (via text, email, or recorded voice message). Eventually, the service will offer climatic information in Spanish, making it easier for native Spanish speakers to make interpretations and decisions (Lester 2010). In the future, similar ICTs can be used in rural areas of developing countries.
Major water resource constraints and climate change make it increasingly important for developing countries to develop sound water-use policies and well-functioning, well-managed irrigation systems. Innovative water management systems and ICTs are helping to improve water use and expand intensive irrigation facilities. Though the number of technologies for irrigation is vast, this section focuses on remote sensors, satellite imagery, and GPS cameras. Each of these technologies helps to connect the farmers to irrigation infrastructure and guide governments in designing and implementing irrigation strategies.
ICTs help address some of the challenges inherent in creating and sustaining irrigation systems in rural areas. The function of water-user associations and their productivity improve through ICTs like mobile phones and personal device applications PDAs),whichincrease the quality and frequency of producers’ communication and interaction. Sharing information about emergency maintenance problems, entitlement rights, and management schedules is facilitated through ICT, which allows real-time responses even between users from distant communities.
Digital orthophoto quads (DOQs), a feature of GIS, are digital maps that combine the geometric information of a regular map with the detail of an aerial photograph (Neale 2003) (Click here to view image 5.7). DOQs provide spatial illustration of terrain, including elevation and property boundaries, which can help delineate irrigation canals and drainage systems. Given the high and increasing value of rural land, it is worth noting that the resolution and georeferencing possibilities of most satellite remote sensing systems are not yet adequate to demarcate property accurately. Nonetheless, achieving greater accuracy and confidence in property boundaries is essential to limit the land disputes that ensure when new irrigation schemes are designed and built. DOQs can help to achieve this higher level of resolution, but sometimes at higher costs than other high-resolution imagery. See IPS “Digital Orthophoto Quads Form a Database for the Dominican Republic” in Topic Note 5.2.
LiDAR (laser scanning) is a new technology for obtaining a highly detailed digital terrain model or, if equipped with an aerial camera, for topographic mapping. A digital terrain model is basically a digital representation of an area’s terrain on a GIS that provides accurate position and elevation coordinates. It is compatible with other digital spatial data, is more accurate, and has a higher resolution than satellite images. Elevations can be accurate within 5 centimeters, but accuracy typically is closer to 10 or 20 centimeters. In comparison, digital aerial cameras only provide only about a 20-centimeter horizontal resolution.
Because of its detailed imagery, a digital terrain model can be used for meticulous engineering designs such as those for roads, drainage, gravity-fed irrigation works, and detention reservoirs. These models can also be used more broadly to manage land and water (for example, in flood control). When combined through GIS with other data such as soil types, these models can help to identify areas with potential slope instability and erosion, which are important for reducing soil degradation and its negative impact on soil fertility. At the field level, digital terrain models can monitor and improve areas affected by waterlogging or flooding. Overall laser scanning has considerable potential for planning irrigation schemes, designing infrastructure, managing irrigation operations, and modeling. Laser scanning is most useful for large areas because the aerial operation is expensive. The cost of laser scanning also depends on the accuracy of the data required, location of the area of interest, and level of the data products (such as GIS layers).
Satellite data can also prove useful in managing irrigation schemes, such as the enormous Office du Niger scheme in Mali (see IPS “Using Landsat to Assess Irrigation Systems in Mali” in Topic Note 5.2). This irrigation scheme, one of the largest is West Africa, produces 40 percent of Mali’s rice crop and is key to national food security.
An equally intriguing ICT for irrigation management, specifically for monitoring the construction of irrigation systems, is GPS cameras. The cameras are relatively cheap and user friendly; when a project worker photographs infrastructure, the camera records the date, time, longitude, and latitude automatically.
Afghanistan’s national Emergency Irrigation Rehabilitation Project (funded by the World Bank) was delayed owing to increases in conflict in certain regions, but now GPS cameras provide “remote supervision.” As the irrigation project unfolds, water users can photograph the construction process to make contractors more accountable and prevent financial resources form being wasted. Users can report infrastructure problems to the government without needing to travel through potentially dangerous regions.
Project workers have photographed over 650 locations where irrigation construction projects are being implemented. These photos, which are emailed or delivered by hand to ministry offices, serve as the baseline for progress (World Bank 2010b). A crucial point is that the technology also enhances the participatory process, which may improve user associations’ productivity once the irrigation system is complete.
This note has described the many ways that ICTs enable real-time adjustments in agricultural practices to prevent losses after investments have been made. These technologies also have considerable potential to help small-scale producers use scarce resources—water, nutrients, and others. Greater certainty about the weather, access to water, and disease outbreaks can lead to better decisions and higher productivity. These ICTs also face important challenges, however, and a number of considerations are important in improving their effectiveness, especially for smallholders.
Strategies to improve agricultural practices change dramatically over time, just as strategies to manage irrigation have evolved from a nationally operated to user-operated model. ICTs aimed at preventing crop or livestock losses must adapt in line with these strategies so that users receive current information, communicated in the most cost-effective way.
Local knowledge is critical to improving smallholders’ productivity. ICT not only creates opportunities to disseminate information but offers ways of capturing local expertise. Vast differences in ecological and agronomic conditions make farmers’ knowledge indispensable. ICTs should be used to form two-way communication networks, ensuring that local knowledge is acquired and utilized.
The collective action problem is quite apparent in relation to the technologies described here. Water management and disease control require hundreds or even thousands of farmers to perform the same tasks in unison. By strengthening information sharing, ICTs like mobile phones will increase the potential for collective action. Self-policing may also be crucial to the technology’s success.
ICTs to disseminate information like weather forecasts must match capacity in the focus area. Some phones handle complex messaging; others do not. Local ICTs may need to improve before some preventive technologies can work in developing countries. Taking stock of the technical capacity in rural areas will clarify infrastructure needs.
Gender is an important consideration when using ICTs to prevent crop loss. Women are often already involved in maintaining water resources (for domestic and agricultural use) in their families. Involving them in water management or pest control projects increases their time to attend to other important activities like education and generating income. It also often results in more effective management.
Timing is a major concern in weather, water, disease, or pest ICT. If information is sent too late, farmers may not have time to adjust their farming strategy. If information arrives too early, farmers may make changes that prove unnecessary or even damaging.
Information must be relevant and clear. Too much text or scientific data can conceal the message and cause confusion. Only the most appropriate and contextually-based information (like forecasts) and updates should be provided. By continually interacting with farmers and monitoring their responses to information, project managers can clarify which information needs to be sent and which does not.
Keeping information current is expensive. Collaborating with various agencies and creating common systems and technologies can help achieve economies of scale to reduce costs (IICD 2006).
Just as they can be overwhelmed with too much new information, farmers can be overwhelmed with new technology and become reluctant to use it. Advances in ICT are best suited to helping farmers improve their management of one or two farm components at a time. Development partners and governments need to prioritize which yield technologies or agricultural strategies they would like to introduce and use ICTs to disseminate them to a broad population.
Limited financial resources are also a potential limitation to using these technologies. Large agricultural firms and smallholders alike need to control agricultural water, diseases, or pests. Incentives for the private sector to partner with government in large-scale ICT projects may enable the investment to reach smallholders as well.
INNOVATIVE PRACTICE SUMMARY
Radio Frequency Identification to Prevent and Treat Cattle Disease in Botswana
Implemented by Inala Identification Control (IIC) in South Africa, the Livestock Identification Trace-Back System in Botswana is one of the largest and more innovative forms of ICT for animal husbandry, involving over 300 million cattle.6 The system, which uses radio-frequency identification (RFID), serves many purposes, including meeting beef import requirements for the European Union (EU), the destination for 80–90 percent of Botswana’s beef exports. The system also improves veterinary services and livestock health.
A bolus with a unique ID number and a transponder is inserted into each animal’s rumen. In the field, 300 fixed readers scan cattle ID numbers and relay information to databases in 46 district offices. The bolus collects information that allows both herdsmen and the government to monitor new registrations, look for possible disease outbreaks, identify lost or stolen cattle, track weight gain, and plan for animal treatments. The database also provides the opportunity to monitor trends over time.
Technology like this offers many benefits. The bolus is safe for animals, protected from criminal tampering, and can be recycled, which keeps costs low. The bolus also saves time: Ear-tags, the traditional form of identification, required herdsmen or veterinarians to handpick cows through a lengthy process. This system speeds up the identification process. Herdsmen can optimize feeding schedules, select certain bulls for breeding programs, and keep updated health records, which improves productivity directly by reducing susceptibility to disease and planning for yields.
INNOVATIVE PRACTICE SUMMARY
Digital Orthophoto Quads Form a Database for the Dominican Republic
Digital orthophoto quads (DOQs) can do much more than provide digital maps. By tracking the photos, it is possible to create water databases that are crucial to the success of irrigation. The databases can provide real-time information on heavily and sparsely irrigated locations, statistics on water use (and subsequently water users), drainage problems, and even salinity issues.
This kind of database featured in a program to improve users’ management of irrigation systems (PROMASIR) in Dominican Republic in partnership with the Inter-American Development Bank and Utah State University.7 By combining DOQs with other information (such as information on property ownership), the database enables water users to search for other water users, observe property boundaries, review monthly crop and water statistics, or obtain estimates of irrigation water demand in certain areas. Users have access to more accurate information to use when updating their infrastructure as well as more insight into potential maintenance problems (such as a system breakdown upstream). Assigning water rights and water fees are also easier with databases. In areas with greater demand, prices can be expected to rise. Finally, a system like this can also prevents conflicts over water, because all users have access to the same factual information, such as price information and plot size.
An important point, however, is that smallholders who typically use agricultural water to meet their own needs for sustenance may not be accustomed to the kinds of collective action needed to develop and sustain large water management networks. They may maintain an individual farm mentality even when technologies like DOQ databases are available.
INNOVATIVE PRACTICE SUMMARY
Using Landsat to Assess Irrigation Systems in Mali
The Office du Niger, a vast irrigation scheme dating to the 1920s in Mali, delivers water from the Niger River to approximately 80,000 hectares of rice fields The irrigation scheme is divided into five administrative zones, each responsible its own water management. The scheme’s senior staff use data from Landsat (which uses sensors to record reflected and emitted energy from Earth) and other sensory data (including air temperature and humidity) to analyze cropping intensity, asses water productivity, and monitor equity in water distribution.8 The data are also used to compare the productivity of fields at the head (beginning) of the water source with the productivity of the fields at the tail (the most distant point from the water source).
Landsat has the ability to “see” a variety of colors as well as near-infrared, mid-infrared, and thermal infrared light, which helps to distinguish differences between land plots or water sources. Initial results from Landsat images revealed critical similarities and differences between administrative zones that irrigation managers can use to determine and address the causes of yield variation (for example, low yields in fields near the tail). To gain even greater clarity on why irrigation may succeed or fail in a given location, remote sensing and GIS images such as those used in Mali can be coupled with other statistics like administrative boundaries, crop data, and poverty levels in GIS maps.
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- 1 This is not a comprehensive list of all of technologies discussed in the module; nor is it a comprehensive list of all ICTs used to increase agricultural productivity. The technologies reviewed here are the best known and most applicable to all yield technologies or agricultural strategies.
- 2 For a diverse set of soil maps and data, see FAO, http://www.fao.org/climatechange/54273/en/).
- 3 These practices incur some costs, especially in the short term. More fertilizer may be needed before soil organic carbon increases. Similarly, crop residues that are used for fuel or feed will no longer be available (Lal et al. 2004).
- 4 This section draws on Wark et al. (2007).
- 5 See http://www.dssresources.com.
- 6 This section draws on Burger (2004).
- 7 This section draws on Neale (2003) and World Bank (2006). PROMASIR is the Programa de Mejoramiento y Administración de Sistemas de Riego por los usuarios.
- 8 This section draws on World Bank (2010c) and NASA (n.d.).
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