Rainfall modelling can predict future yields
|Rainwater harvesting and management|
PARCHED THIRST (PT) is a decision-support tool that addresses the challenges of low and unreliable crop and livestock production in semi-arid areas. Unlike most other models, PT includes the effect of the weather, water management and soil variability on cereal crop yields. Planners can therefore use it to estimate food deficits or surplus, and so anticipate their import or export strategies. The model is currently used by the Early Warning Department of the Ministry of Agriculture and by agricultural extension offices and training institutes in several areas of Tanzania. It is also used for research and teaching in Uganda, Ethiopia, South Africa, Nigeria, India, Pakistan, Greece, the UK and the USA. It is available for download from websites in Tanzania, UK and Belgium.
Project Ref: NRSP13:
Natural Resources Systems Programme and the Government of Tanzania
Relevant Research Projects:
Collaborating institutions and partners included:
The RNRRS output or clusters of outputs are Rainwater harvesting and Scaling up through uptake promotion. The outputs were:
PARCHED THIRST (PT) model v2.1 was further developed (v2.2, v2.3 and v2.4), validated and promoted to improve integrated management of rainwater. PARCHED-THIRST, which stands for Predicting Arable Resource Capture in Hostile Environments during the Harvesting of Incident Rainfall in Semi-arid Tropics, is a process-based model, which combines the simulation of hydrology with growth and yield of a crop. The PT model is a decision support tool that addresses challenges of low and unreliable crop and livestock production in semi arid areas. This is caused by low availability of soil moisture as a result of erratic distribution of rainfall and high losses through run-off and evaporation.
The project facilitated the availability and use of the PT model to potential users. Effective communication and knowledge management strategies for assisting the scaling-up of PT Model and related research outputs in rainwater harvesting (RWH) that would benefit the poor in semi-arid areas were developed and promoted. This was achieved through use of interactive methods and reader-friendly written communication methods and media to create awareness, providing advice and help to target users. The communication products included virtual laboratory experiments (for agricultural colleges and universities), and Knowledge Sharing Products (booklets, posters, manual). Case studies using PT model to address specific problems for districts, MAFS and Tanzania Meteorological Agency (TMA) were developed and documented as part of the validation process. These activities were conducted between 2002 and 2006.
The output focused on the following main commodities: Maize, rice, lablab, vegetables (onions, tomatoes, water melons, cabbage) and livestock. These outputs could also be applied to other commodities such as tree planting, domestic water use, aquaculture, brick making and construction.
Value could be added to the project outputs by clustering with outputs from the following RNRRS projects: R6621, R7987, R8115 and R8116. For example, outputs from RNRRS project R6621, dealt with the development of a hillsides system soil and water model used to study different soil conservation measures and climatic regimes, could supplement those from the PT model to come up with more elaborate decisions on soil water conservation. Also, outputs from project R7987 could be used to increase awareness/appreciation to extension staff of the potential of computer applications/models in agricultural and livestock planning. The R7987 project developed a computer-based decision support system known as 'Tsetse Plan' to help planners design and implement community-based interventions against tsetse fly infestation in Eastern African countries.
Project R8116 identified institutional, regulatory and tenure systems requiring improvement of the management of common pool resources (CPR) and project R8115 produced communication products on improved strategies for integrated soil and plant nutrient management in RWH. Both projects addressed issues in NRM the same as R8088. Therefore clustering with these outputs will have multiplier effects on poverty reduction through improved capacity of the policy makers, extension specialists to deliver services and smallholder farmers to better manage their resources.
Furthermore, the project outputs could also be clustered with non-RNRRS projects P185 and P113, funded by Water Research Fund for Southern Africa (WARFSA). Project P185 focused on the development and validation of a macro-catchment RWH module to be included into the PT model. This could increase the scope of application of the model. Project P113 developed a GIS-based decision support system for identifying potential sites for rainwater harvesting. This could assist in identifying potential locations/areas for rainwater harvesting and hence enable the identification of areas where the PARCHED-THIRST model could be applied successfully.
How the outputs were validated:
Preliminary validation of PARCH, which is the first component of PARCHED THIRST model, was done by various research institutes. The validation of PARCH involved experiments conducted by J.L. Monteith in 1988/89 at ICRISAT center to study the interactions of water and nutrients on the growth of sorghum, under irrigated and drought conditions; simulation of cultivar DK 55 at the Katherine research station in the Northern Territory (Australia) using PARCH (Hammer & Muchow, 1992); validation of the soil-water balance sub-model of PARCH conducted in Botswana by Land and Water Management Project (L&WMP), with one variety of sorghum (MV) and one maize variety (KGN); and experiments conducted at the Tropical Crop Research Institute Unit at the University of Nottingham to validate PARCH simulation of sorghum (cv. 65D); Kenyi (1991).
Comparisons of actual growth data against PARCH predictions have been analysed. Simulated grain and dry matter have been plotted against the actual recorded values to produce 1:1 comparison graphs and linear regression analysis conducted on the pooled data gives an r2 of 0.97 with 70 DF, resulting in a probability of P<0.001 which is a very positive result.
Further validation of various components of the PARCHED THIRST model was performed by researchers from SWMRG-SUA since year 2000 in collaboration with various stakeholders. Data sets on soils and crop yields from Kigonigoni site in Mwanga district (Kilimanjaro-Tanzania) and from Magadu site in Morogoro district were collected and a comparison with simulated values of maize (TMV1) yields under both rainfed and RWH systems was done. Results showed good agreement between simulated and measured yields under both RWH and rainfed conditions. To improve the performance of the model in the design of RWH system, a new sub-routine that allows macro-catchment RWH was developed, validated and incorporated into the model (Mzirai,O.B, 2006).
To promote the use and adoption of PT, SWMRG established the 'PT Help Office', with the intention to collect feedback from and to provide support to the model users. Training and feedback workshops have been organised for target stakeholders including district agricultural extension officers, agricultural tutors from various agricultural training institutes, researchers and lecturers from the Sokoine University of Agriculture (SUA) (in the field of agriculture, soil science and irrigation engineering), staff from the MAFSC and from the TMA. Through these training sessions, district agricultural officers were able to identify areas where the model can be applied to solve problems at their work stations, and thence developed a number of relevant case studies. For example, agricultural extension officers from Kilosa district used the model to study and compare the effects of different soil and water conservation techniques on maize crop yields in Vidunda village; this enabled them to advice farmers on best options regarding planting dates. Agricultural tutors and lecturers from SUA were also involved in the development of a teaching manual of virtual experiments in agriculture using the PT model. The manual is meant to assist in teaching some topics in agriculture to undergraduate and postgraduate students at SUA and to diploma students in Agricultural Training Institutes. Feedback from different stakeholders has made it possible to upgrade the model from version 2.1 to version 3.0 (through versions 2.2, 2.3 and 2.4).
Where the Outputs were Validated:
PARCHED-THIRST outputs have been validated in various locations in semi arid tropics. Validation of the PARCH component was done since in the late 1980's and continued in the beginning of 1990's in Australia and Botswana. Since year 2000, the RWH component of the model as well as the new macro-catchment RWH module, incorporated into the model recently, have been validated and the results are now applied at different sites, in semi-arid areas of Tanzania. District agricultural extension officers in Morogoro, Kilosa and Same districts validated the model by applying it to their environments by developing case studies in 2005. The same year (2005), instructors at SUA and other colleges and researchers at Uyole and Ilonga agricultural research and training institutes validated the model by developing virtual experiments. At national level, Ministry of Agriculture, Food Security and Cooperatives and Tanzania Meteorological Agency, using PT model, developed case studies relating to food security.
Who are the Users?
Currently, agricultural extension officers in Morogoro, Kilosa, Mwanga and Same districts are using the model to simulate various agricultural practices, from which they are able to deduce useful information they need to advise farmers on different crop management aspects; such as optimal planting window, appropriate soil and water conservation techniques and rainwater harvesting systems design. SWMRG provided computers to the four districts agricultural offices to enable them use the model.
PT is also being used by tutors at Ilonga and Uyole agricultural training Institutes, and SUA lecturers, whereby conventional experiments in agriculture are complemented by virtual experiments using the model.
At national level, the Early Warning Department of the Ministry of Agriculture, Food Security and Cooperatives anticipates using PT and seasonal climate forecasts to predict crop yield.
At continent level, PT model is used for research and teaching in the following African institutions: Department of Soil Science, Makerere University, Uganda; Department of Geography and Environmental Studies, Addis Ababa University, Ethiopia; University of Kwazulu-Natal and University of the Witwatersrand, School of Civil and Environmental Engineering; Department of Soil, Crop and Climate Sciences, University of Free State, South Africa; Department of Agricultural Engineering, Obafemi Awolowo University, Ile-Ife, Nigeria.
Use of PT model outside Africa, again, for research and teaching is at the following institutions: Cornell University, USA; Centre for Ecology and Hydrology, UK; Water Management, Pakistan; Columbia University, USA; Agricultural University of Athens, Greece; HR Wallingford, UK; Agricultural Engineering College and Research Institute, India; Katholieke Universiteit Leuven, Belgium and University of Newcastle upon Tyne, UK.
Where the outputs have been used:
Outputs of the project are being used in Morogoro, Kilosa and Same districts in Tanzania. The outputs are also used by Sokoine University of Agriculture and Ilonga agricultural training Institute located in Morogoro, Uyole and Igurusi agricultural training institutes located in Mbeya region in Tanzania. Furthermore, PT Help Office continues to receive and to respond to requests about the model from within and outside Tanzania.
Use in Africa:
Use outside Africa:
Scale of Current Use:
The scale of use in this context was measured by the number of software copies distributed or downloaded by clients. By 2002, more than 200 copies were distributed to various clients. They include 9 district agricultural and extension officers from targets areas; ;8 Irrigation engineers; 8; personnels working with NGOs in the targets areas; 9 government departments working on agriculture and water resources; 14 agricultural research institutions; 14 rainwater harvesting projects and organizations; 13 lecturers from SUA and University of Dar-es-Salaam; 28 scientists/researchers working on different SWMnet/ASARECA network and countries; 5 scientists and researchers in international organization and networks,; 10 participants who attended Statistics in Agricultural Climatology (SIAC) courses; 22 participants who attended PT launching workshop; 4 postgraduate students at SUA.
Websites in Tanzania, UK and Belgium house the model. The sites in Tanzania and UK do not have means to keep track of the number of downloads while that in Belgium does. From the Belgium website the number of downloads, between November and December 2005, were 7 and between May and July 2006 were 14. This finding implies that the scale of use is quite high given the 21 downloads recorded in only five-month period in Belgium alone.
Through a feedback workshop organized by the SWMRG in August 2006, it was revealed that there is a significant and continuous increase of the number of model users and aspirant users. Participants drawn from different parts of the country included water and irrigation specialists, agronomists, extension specialists and instructors.
Policy and Institutional Structures, and Key Components for Success:
The following programmes, platforms, policy and institutional structures have assisted in the promotion and adoption of the outputs:
Promotion of Outputs:
Currently, limited promotion of the model is going on in Tanzania, led by the SWMRG. Of recent, A workshop was organized in August 2006 to train and receive feedback from stakeholders. The participants included agricultural officers from Hai and Rombo districts in Kilimanjaro Region; tutors from agricultural training Institutes-Igurusi and Kilimanjaro Agricultural Training College; hydrologists from Pangani and Rufiji Basin Water Offices; zonal irrigation officers from Morogoro and Kilimanjaro region and staff from the Mixed Farming Improvement project (MIFIPRO) operating in Kilimanjaro region. Brochures for short courses have been produced and are planned to be distributed to all line ministries and district councils. The model is freely available over the Internet through the three websites in Tanzania (Sokoine University of Agriculture), UK (NRSP website, University of Newcastle upon Tyne) and Belgium (Katholieke Universiteit Leuven). This has allowed a global promotion of the model and some of the downloads which have been recorded were from countries like India, Pakistan, Malawi, Iran, South Africa, Cuba, USA, Australia, Ethiopia and Uganda.
Potential Barriers Preventing Adoption of Outputs:
The current barriers preventing the adoption of outputs include little understanding of the importance of crop models among stakeholders. For example, the majority of agricultural extension officers in the country were not exposed to the ideas of crop models during their college education. In addition, most of them have little knowledge in computers, and/or they have limited or completely no access to computers at their work places. In addition, politicians and policy makers have little awareness and knowledge on the role of models in assisting policy formulation and decision-making.
For colleges, the main factor limiting adoption is limited teaching facilities such as computers as well as resource persons knowledgeable with models. The majority of those appointed, by their institutions, to attend PT training have been male scientists (extension specialists and researchers), while there is a substantial number of qualified female scientists. In addition, PT Help-Office is currently constrained in-terms of facilities, connectivity and specialized manpower even though is handling all queries from users.
There is also a problem in the acquisition of the meteorological data leading to its limited availability. This is caused by lack of conducive policies on data acquisition and sharing. On the other hand, there is no policy of creating databases on soil and crop parameters, this is despite the fact that researchers have been generating data, which is scattered and sometimes lost.
How to Overcome Barriers to Adoption of Outputs:
Poverty Impact Studies:
An impact study for this CLUSTER titled "Benefits of RWH in Poverty Reduction in Tanzania" was conducted in Maswa and Same districts under the auspices of project R8116.There is a strong link betweenR8116 and R8088 because Project R8116 identified institutional, regulatory and tenure systems requiring improvement of the management of common pool resources (CPR), which were linked with R8088. The impact study involved yield monitoring of paddy and maize crops to establish evidence of benefits of RWH in poverty reduction. The study showed that:
However, for investments in RWH to have an impact on poverty reduction, increased linkages to profitable markets is critical.
How the Poor have Benefited (including gender and other poverty groups):
The following positive impacts on livelihood have been recorded:
Direct and Indirect Environmental Benefits:
Some of the environmental benefits related to this output include runoff management, soil and water conservation and optimum water abstraction. The model is capable of determining the required catchment areas and cropping areas based on the rainfall pattern and amount of runoff generated for a given location. This allows the runoff water to be captured and applied to the cropping area and therefore reducing possibility of unnecessary flooding and soil erosion. The model can also be used to simulate the effect of soil and water conservation measures by constructing virtual terraces on the software. The long-term effect of lack of soil conservation measures is erosion of the agriculture land, which leads to lower productivity. This effect is normally not easy for farmers to understand or perceive. However, the model is capable of illustrating the effects through simulation.
Furthermore, optimum water abstraction can be achieved with PT model. This is important because unnecessary water abstraction can lead to negative environmental impact downstream or conflicts with downstream users. The model can be used to estimate the amount of water required over time and therefore abstraction amounts and schedules.
Adverse Environmental Impacts:
None. Mainly positive impacts are expected because the outputs aimed at addressing proper water harvesting for livestock and crop production and, soil and water conservation.
Coping with the Effects of Climate Change, or Risk from Natural Disasters:
The outputs could play an important role in increasing people's capacity to cope with the effects of climate change. For example, by using the model with climate forecasts, it is possible to tell which could be the appropriate planting dates for a particular location, and what combination of agricultural inputs/ resources and management practices will result into optimum yields. Therefore, people could be able to use the available resources rationally and also to prepare for prevention or at least to reduce the impact of disasters that could result from climate change.
Relevant Research Projects,
with links to the
Geographical regions included:
Target Audiences for this content:
Crop farmers, Livestock farmers, Fishers, Forest-dependent poor,