Cassava Crop Model Improvement Team Introduction: Cassava Production today and in the future Cassava is a basic energy source of great importance in the tropics (Cock, 1982). Although traditionally thought of as a poor man s crop that grows on poor soils, it has great potential to meet the needs of a rapidly-developing world facing the challenges of climate change: it is a crop with important potential for intensification throughout the world. As countries develop there is a slow increase in consumption of starchy products until a critical income level is reached. Above this income level demand, for starchy crop products changes from that of traditional foods to become a major source of food, animal feed and industrial starch and demand takes off (Monke, 2000, Monke 1983). Large areas of tropical and subtropical Asia and the southern sub-tropics of South America are now reaching or have already passed the threshold income level and the cassava business is booming with cassava changing from a traditional food crop to be a giant export oriented industry, particularly in Vietnam and Thailand (Kawano, 2011) and also in Southern Brazil and Paraguay as an important source of starch. In parts of Africa, as food policies cease to favour imported food, cassava production is increasing rapidly, with Nigeria now the world s largest producer, albeit with low yields. Previous cassava booms were normally based on increased area planted to extensive cassava production: the latest cassava boom is based on both increased area planted and intensification in Southern China, Thailand, Cambodia and Nigeria, and primarily due to more intensive production in Southern India, Indonesia and Vietnam (Kawano, 2011). In the tropics, cassava root yields are frequently in the range 5 20 t/ha (Fermont et al., 2009; FAO, 2012). Nevertheless, root yields as high as 75 90 t/ha (fresh weight) and 25 30 t/ha (dry root weight) are possible under experimental conditions (El-Sharkawy, 2004). The large difference between experimental yields and farmers yields suggests that there is a large yield gap that can be closed by improved management. This is already occurring: during the 30 years to 2009, total cassava production increased by more than 100%, with an increase in average yield of 79% (Kawano, 2011). In Indonesia over the past 30 years production increased by 60% due to intensification as area planted to cassava declined by 18%. Cassava as a crop will grow and produce on exhausted soils and is often grown as the last crop in rotations (Cock, 1982). Traditionally cassava was grown with little use of purchased inputs (Purseglove, 1974; Cock, 1982; Cock and Howeler, 1978, El-Sharkawy and Cadavid, 2000; Howeler, 1991). Nevertheless, as Howeler (2001) pointed out in a seminal review, yield stagnation or decline in many cassava growing regions is partly due to displacement of cassava to more marginal areas, and partly due to the deterioration of the soil resources and poor management. However, this is now changing in Asia, with increased
2 yields due to improved varieties and cultural practices, including fertility management (Kawano, 2011). We suggest that there is a large, rapidly-expanding potential market for cassava products in Asia, Africa and parts of Latin America and that growers and policy makers are anxious to respond to this surging demand through both intensification of production and increased area planted to the crop. The intensification of production will be based on both new varieties that continue to be developed, coupled with improved management practices. Currently researchers, policy makers, private sectors investors, and growers have scarce trustworthy quantitative information on how improved cultivars will perform and how to manage them, however. Of particular importance in the tropical regions is climate change with the likely advent of temperature regimes that do not currently exist and greater uncertainty in weather conditions. Preliminary rather crude models (Ceballos et al., 2012; Jarvis et al., 2012) and expert opinion (J.H. Cock pers. comm.) suggest that cassava, with its tolerance of high temperatures and lack of critical growth phases, is likely to become of even greater importance as a basic energy source in the form of starch in the coming decades. Simulation models of cassava growth can provide information that will guide the future development of the cassava crop. Cassava will then play an ever more important role in ensuring food security and improving the welfare of the rural population in tropical regions where there are few viable alternatives. The cassava Crop Model Improvement Team (CMIT). A simulation model of cassava is needed to enhance understanding of its performance under current and future climates and to help evaluate improved genetic and management technologies across a wide range of global environments. Only a few cassava models exist at present, and these have had very limited testing. There is a critical need to evaluate and improve existing simulation models of cassava for use in guiding the development of improved production technology and evaluating potential benefits of new technologies globally. The models will have to take account of existing climate and soil conditions and under climate change scenarios. These models will be used to target research investments for different agro-ecological zones. They will provide information for policy makers to evaluate alternative policies, which will improve both food security and the demands for starchy products. They will also guide appropriate policies designed to reduce environmental degradation across a wide range of production and socio-economic environments. CIAT and CCAFS have formed a cassava Crop Model Improvement Team (CMIT) with crop modelers from various institutions developing and improving existing cassava models (currently DSSAT and APSIM). CIAT provides Team leadership in the initiative and is committed to improving and maintaining the model and its code over the long term. The Team comprises researchers experienced in cassava physiology and modeling (James Cock,
3 physiologist and first developer of a cassava model; Anthony Hunt, physiologist and plant breeder, co-developer of the GUMCAS cassava model (Matthews and Hunt, 1994), and developer of the most recent prototype cassava model (CROPSIM-cassava) in DSSAT, Gerrit Hoogenboom DSSAT coordinator, Myles Fisher ecophysiologist with broad experience in crop modeling; John Hargreaves CSIRO crop modeler and developer of the APSIM cassava model). The team works in close association with, and is backed up by the CIAT cassava programme. The Team also includes researchers from Chiang Mai University, the Khon Kaen Field Crop Research Center under the Department of Agriculture of the Thai Ministry of Agriculture and Cooperatives, and the CIAT cassava program with support from the Colombian Ministry of Agriculture and Rural Development. Field researchers who have existing cassava datasets or are actively involved in field research are providing minimum data sets for model development and evaluation of the model. As the programme gathers momentum other organizations and institutions are expected to join. The Team is working in conjunction with the DSSAT Foundation and the AgMIP crop modeling initiative (Gerrit Hoogenboom leads the DSSAT Foundation and is an AgMIP crop modeler). The Cassava-CMIT leader is responsible for leading the overall initiative, organizing meetings, assembling data, communicating advances to the team and external agencies in publications and reports, and documenting the model and its components. The Team will ensure that it operates in accordance with the principles of full availability to model code, full attribution of data, model components, publications, and publication of datasets (with full respect for data providers, including any needed delay in data posting due to publication or other proprietary issues). Goal: What if? The overall long term goal of the cassava CMIT is to provide the research community, policy makers, private sector business enterprises and cassava growers with the capability to ask what is likely to happen if they follow a particular course of action with cassava and to obtain a trustworthy answer. The strategy adopted to reach this goal is to develop a robust model that simulates accurately the development of the cassava crop under current known conditions with current varieties and that can provide the likely development of new cultivars and different management practices under today s and future conditions. CMIT Objectives The overall objective is to develop a model that accurately simulates cassava growth and yield under a wide range of climate, soil, genetic, and management conditions, some of which currently do not exist. An output from this effort will be a global public good that is freely available for use and modification by those interested in the cassava crop as an attractive option to meet their specific goals. The CMIT team has set short term, medium and long term objectives. These objectives are linked to both outputs and possible outcomes.
4 Short term objective, output and outcomes. The short term objective (until the end of 2013) is to develop a working model for cassava on two platforms, DSSAT and APSIM, simulating the growth and development of cassava in the lower-latitude tropics for a small number of widely-grown varieties. The model will incorporate the known capacity for cassava to tolerate drought and to produce well under low fertility conditions. The model will be tested and evaluated against existing data sets different from those used to develop it. The model will include the important effects of leaf to air vapor pressure deficit for the first time in an effective crop model, and which is an important contributor to cassava s ability to resist drought. The model will allow breeders to simulate the likely performance of new ideotypes for specific conditions and the response of the crop to different management practices. The model will provide the best available information on how cassava is likely to respond to the more extreme conditions of higher temperature, higher CO 2 concentrations, and drier weather that are likely to occur in the coming years. We expect breeders to use this improved model to design cultivars with improved traits and to make crosses directed to developing designer varieties well adapted to specific conditions and management. Policy makers are expected to use the model to explore the possibilities of increased production and productivity through improved land use and management, and to use this knowledge to determine more effective agricultural policies that improve food security and increase the incomes of small farmers. Agri-business and industry will use the model to determine how they can increase productivity and competiveness of their industry. Medium term objective, output and outcomes (2016). The medium term objective is to extend the range of the model to the more extreme subtropical latitudes incorporating the effects of photoperiod and to ensure that it can be used to extrapolate with confidence into climatic zones that simply do not exist today. The subtropical areas are already important for cassava production and are likely to become more important as winter temperatures rise and they become more suitable to grow cassava. The Team has already identified several gaps in our knowledge of the growth and development of cassava in the field and these will be incorporated into the model. The gaps include, inter alia: The response of cassava to individual fertilizers other than nitrogen, with particular emphasis on potassium which represents a high cost for small farmers; Response of leaf and stem development to very high temperatures; The dry matter content of roots which is of vital importance; and Parameters for a broader genetic base. In addition the team is currently exploring collaboration with disease and pest modeling groups with a view to coupling the growth and development model with their models. The Team will promote the use of non-destructive sampling methods for the generation of information to accurately describe and later evaluate the efficacy of the simulated growth and development processes as a major limitation to progress in the building of the model.
5 Figure 1. CMIT Team in a cassava breeding experiment on CIAT. The Team has already developed the conceptual basis for non-destructive monitoring and evaluation and will make this a routine procedure, which will then be used to evaluate continually the efficacy of the model in a process of continuous improvement. Within three to four years the Team will have a model, which will be available to those interested, and which provides trustworthy evaluations of the likely effects of: climate change on cassava growth and production; varietal traits associated with adaptation to variation in climatic and weather conditions; management practices geared to increased productivity and greater stability of dry matter content during the year. In addition non-destructive evaluation procedures for continuous improvement of the model will be available. As expected with the short-term objectives, the Team expects breeders to begin to test designer varieties adapted to specific conditions and management in the field. Policy makers are expected to implement policies, based on use of the model, geared to increased production and productivity through improved land use and management. The Team expects that decisions will be made by agri-business and industry that will begin to increase both the productivity and competiveness of their industry. Long term, objective, output and outcomes (2018). The long-term objective is to make available to all interested people a simulation model that accurately depicts the growth and development of cassava under a wide array of existing and potentially occurring environments and management conditions. As the model is developed the Team will incorporate gene effects. Complex multi-genic effects will be added considering the work in DNA sequencing, which is currently being done through sequencing data and SNPs (single-nucleotide polymorphism) associating genotype with phenotype. The long-term output will be the availability of a cassava model that allows the research community, policy makers, growers and agri-businesses to ask ex ante the question What happens if I take a certain action? and to receive a trustworthy response. Over the long term the outcome of the development of this dependable and reliable cassava model will be better informed production decisions, better adapted varieties available to growers, wiser policy decisions that will improve the livelihoods of the rural poor and guarantee food security and a more competitive agri-business sector.
6 Organization The Cassava CMIT has opted for an informal networking organizational structure, which will allow users to modify the model code for their own requirements. Users will be encouraged to inform the Team of any modifications they might make and why so that the Team can incorporate useful developments into the code for other users. The model will be improved and tested with further field data from research and development organizations such as CIAT, CSIRO, Universities, and National Agricultural Research Institutions. The cassava CMIT leader (CIAT) will coordinate activities of this loose organization and will search for funding of the essential activities required for the continuous improvement and maintenance of the model. In order to become a member of the CMIT, an individual or an organization must demonstrate that they can contribute knowledge, data, programming skills or other pertinent skills to the development of an effective cassava model. Figure 2. Cassava meeting, April 2013 Cali, Colombia.
7 16000 14000 12000 10000 8000 6000 4000 2000 0 50 100 150 200 250 300 350 400 Days after Planting Harvest wt (simulated) Harvest wt (observed) 16000 14000 12000 10000 8000 6000 4000 2000 0 50 100 150 200 250 300 350 400 Days after Planting Harvest wt (simulated) Harvest wt (observed) Figure 3. Simulation of harvest yield of cassava variety MCol-1684 with the original DSSAT sub-model (top) and the current version as revised in April, 2013 to take account of leaf size with photothermal time, crop branching, and nitrogen distribution (bottom). References Ceballos, H., J. Ramirez, A.C. Bellotti, A. Jarvis and E. Alvarez (2011). Adaptation of cassava to changing climates. In: S.S. Yadav, R.J. Redden, J.L. Hatfield, H. Lotze-Campen and A.E. Hall (eds.). Crop Adaptation to Climate Change. New York: Wiley. Pp. 411-425. Cock, J. H. (1982). Cassava: A basic energy source in the tropics. Science 218:755-762.
8 Cock, J. H. and R. H. Howeler (1978). The ability of cassava to grow on poor soils. In G.A. Jung, (ed.) Crop tolerance to suboptimal land conditions. Madison, Wisconsin, American Society of Agronomy, ASA Special Publication 32: 145-154. El-Sharkawy, M.A and L.F. Cadavid (2000). Genetic variation within cassava germplasm in response to potassium. Experimental Agriculture 36:323-334. El-Sharkawy, M.A. (2004). Cassava biology and physiology. Plant Molecular Biology 56:481 501. FAO (2012). FAO Statistical Yearbook. Rome: Food and Agriculture Organization of the United Nations. 369 p. Fermont, A.M., P.J.A. van Asten, P.A. Tittonell, M.T. van Wijk, K.E. Giller (2009). Closing the cassava yield gap: An analysis from smallholder farms in East Africa. Field Crops Research 112:24-36. Howeler, R.H. (1991). Long-term effect of cassava cultivation on soil productivity. Field Crops Research 26:1-18. Howeler, R.H. (2001). Cassava agronomy research in Asia: Has it benefited cassava farmers? In: R.H. Howeler and S.L. Tan (Eds.). Cassava s Potential in Asia in the 21st Century: Present Situation and Future Research and Development Needs. Proc. 6th Regional Workshop, Ho Chi Minh City, Vietnam. Pp. 345-382. Jarvis, A., J. Ramirez-Villegas, B.V. Herrera Campo, C. Navarro-Racines (2012). Tropical Plant Biology 5 (1): 9-29. Kawano, K. (2011). The Triumphant Cassava Chronicled by Foresight, Political Will and Accountability. Journal of Root Crops, 37(2): 101-110. Matthews, R.B. and L.A. Hunt. (1994). GUMCAS: a model describing the growth of cassava (Manihot esculenta L. Crantz). Field Crops Research, 36: 69-84 Monke, E.A. (1983) International grain Trade 1950-1980. Technical Bulletin No 247. Agricultural Experiment Station, University of Arizona, Tucson, Arizona, USA. Monke, E.A. (2000). The evolution of cereal and livestock supply and demand: policies to meet new challenges. Cereal and Livestock Supply and Demand. In: M.W. Rosegrant and P.B.R. Hazell (Eds.). Transforming the Rural Asian Economy: The Unfinished Revolution. Oxford UniversityPress. Pp. 161-189. Purseglove, J.W. (1974). Tropical crops: Dicotyledons. Longman. London.