Seasonal Climate Information for Ensuring Agricultural Sustainability and Food Security of Small Holder Rainfed Farmers: Experience from India Madhavan Manjula & Raj Rengalakshmi (M. S. Swaminathan Research Foundation) Abstract Inter‐seasonal and inter‐annual climate variability is the specific climate related production risk facing small holder rainfed farmers in India. The impact of climate variability on agricultural productivity is manifested as crop loss due to excessive/inadequate rainfall or sporadic pest & disease outbreak due to variability in temperature and relative humidity during the growing season. For small holder rainfed farmers, access to reliable extended range and seasonal climate forecasts (SCF) along with existing short and medium range weather forecasts disseminated could induce a set of adaptive risk reduction measures. The paper is an attempt to capture the process and outcome of a pilot research study in a semi‐arid rainfed agro‐ecosystem in Tamil Nadu, India to understand the utility and value of seasonal climate forecast in generating responsive risk reducing decisions by players across the value chain. The results show that to realise the desired societal benefit of SCF which is, ‐ensuring agricultural sustainability and food security through risk reducing decisions‐In addition to forecasts with improved predictive skills, factors of prime concern are the climate variable predicted, effectiveness of communication process, social equity in access to climate information, and ability and flexibility to adapt of the end user. Key Words: Seasonal climate forecast, risk management, climate change adaptation, sustainable agriculture, food security. Introduction: Shifting rainfall patterns, changing temperature regimes and increasing climate variability are some of the outcomes of climate change. While all the factors pose threat to agriculture, climate variability during the crop season is a major source of agriculture production risk (Fraisse, et al 2006, Legler et al. 1999). The impact of climate variability on agricultural productivity is manifested through crop loss due to floods or drought induced by excessive rains or a complete lack of rains during the crop season. Variability in temperature and relative humidity during the growing season triggers sporadic pest and disease outbreak which the farmer is unprepared to handle and thus resulting in massive crop losses. The impact of climate variability on the farming system has a multiplier effect on the other players across the agricultural value chain ultimately posing a challenge to the local agricultural economy and food security. Knowledge of seasonal climate variability prior to crop season and its incorporation in management decisions across the value chain is touted as the key adaptation strategy for intra‐seasonal climate variability. In addition to the existing short and medium range weather forecasts disseminated by the Indian Meteorological Department, access to reliable intra‐seasonal and seasonal climate forecasts (month to multi‐month time frames) could induce a set of adaptive responses that might help to reduce production risks posed by climate variability (Meinke et.al 2006). Sivakumar (2006) reported that access to forecasts of meteorological risks and timely agrometeorological advisories can assist farmers to take appropriate decisions to cope with changing climate by taking both strategic and tactical decisions for sustaining agricultural production and reducing production losses. Several attempts have been made across the globe to generate and disseminate seasonal climate forecast. Research in the regions of South America, South Asia, and Africa pointed out that El
Niño Southern Oscillation based climate forecasts have the potential to enhance agricultural production (Hammer et al. 2001; Hansen 2002; Sivakumar and Hansen 2006). CLIMAG‐West Africa, APSRU and the Queensland Centre for Climate Applications in Australia, Southeast Climate Consortium of Florida University in Argentina, Climate Forecasting for Agricultural Resources (CFAR) targeting smallholder farmers in Burkina Faso, CLIMAG‐Asia in India, Pakistan and Indonesia are the attempts at the ground level to test the technology (Pulwarty 2007). But many of these efforts have not yielded the desired results due to various reasons (Mjelde et.al 1998; Stern and Easterling, 1999; Agrawala et.al., 2002; Patt and Gwata, 2002). The reasons range from low predictive skill of the forecasts at finer scales required by the users, lack of easy access to SCF, inability on the part of end user in understanding SCF and lack of capacity of end user in taking up adaptive responses based on seasonal climate information along with attitude and psychology to take risks. The study of McCrea R et al., (2005) among grain growing men farmers of Australia showed that more use of SCFs can be expected when farmers have a good understanding and a favourable attitude towards the seasonal climate information. The experimental studies in Africa indicated that though reliable climate forecast information was provided to farmers, they often not in a position to modify their management strategies in response to climate risk information due to resource and institutional constraints like land, labour, seeds, inputs, credit, tenure security, market access and viable prices and technical information (O’Brien et al. 2000; Vogel 2000; Ingram et al. 2002; Phillips 2003, Vogel and O’Brien, 2007). So far attempts were made to provide forecast information to producers and gained experiences in communicating probability based climate forecast information with them, and learnt that participatory approach is essential for engaging dialogue with farmers while passing the information. The empirical initiatives in few African countries and India indicated that the participatory approach is essential to communicate the SCF information to farmers (Phillips and Orlove 2004; Ziervogel 2004, Meinke et al. 2006; Hansen et al. 2007). There are two critical elements need to be taken care while communicating the SCF; i. understanding the specific SCF information need of the actors as well as plan to communicate the SCF to them. The review showed that there are many seasonal climate forecast information application studies indicate the risk of a deterministic interpretation of forecast information and many empirical approaches have been tried to explain its probability nature ( Patt and Gwata 2002, Suarez and Patt, 2004, Phillips and Orlove, 2004 and Hansen et al. 2007).As mentioned earlier, research studies suggested the use of participatory approach to explain the probability form of SCF information, additional attempts were made show case the risk in terms of economic terms for easy understanding (Hayman 2011), use of visuals and exercises during workshops may enable participants to relate forecast information to everyday life (Luseno et al., 2003; Phillips and Orlove 2004), question and answer session adopting traditional way of oral form of knowledge transmission (Ingram et al. 2002; Patt et al., 2005), and group interaction and discussion (Patt et al., 2005; Orlove et al., 2007, Peterson et al., 2006, Marx et al., 2007). Also, the seasonal climate information needs of different actors involved in the crop value chains are different and hence to improve the relevance of climate forecasts, it is imperative to identify the decision‐relevant attributes of forecast information for specific activities and actors and simultaneously encourage forecasters to develop and give relevant information with those attributes (Stern et al, 1999). Hence in order to improve the utility of SCF to ensure food security, SCF information should be shared with all actors in the crop value chain and depending upon the actors need, locale specific seasonal climate forecast information needs to be generated and shared for better use. Study Background: Studies on usefulness of seasonal climate forecast in South Asia in general and India in specific (Meinke et.al.2006b, Singh et.al 2007, Hansen et.al., 2007) has shown some utility of SCF in reducing risk. But the studies have also highlighted the declining predictive skill of the indicators used to generate those forecasts due to climate change and reduced utility of SCF in the absence of an effective communication and dissemination channel. Developing better skill in forecasts by basing the models on indicators that effectively capture climate change, assessing relevant climate variables according to the user needs, having effective forecast dissemination channels and building the capacity of the end user in understanding the forecast and addressing the barriers to translation of forecast understanding to field level adaptive actions are ways of facilitating use of SCF in strategic decisions aimed at risk reduction due to climate variability (Coelho et al. 2010). The current study is part of a multi‐country project that aims to investigate the use of SCF in enhancing food security in South Asia and develop a blueprint for improved seasonal climate information across case study regions in the Indian Ocean Rim. The project focuses on 1) improving capacity to deliver and use seasonal climate risk information, 2) development of enhanced and tailored intra‐seasonal to seasonal climate forecasts which incorporate factors that drive climate variations and climate trends, and 3) improve the access to, understanding of and capacity to use seasonal climate information across different elements of society from policy to business to the farming community. The study was implemented across 6 case study sites spread across India and SriLanka. This paper details the outcomes of the study conducted in one of the case study sites ‐ Kanniwadi region of Dindugul distirct of Tamil Nadu State. Description of Study Area: The study takes a value chain approach and focuses on the agricultural value chain in Dindugul district of Tamil Nadu. Dindugul district of Tamil Nadu State is located in the 10 05’ latitude and 77 30’ longitudes. The district has a geographical area of 6266.64 Sq.Km. Agriculture and allied activities are the major economic activity in the district. The major crops are millets and other cereals, pulses, cotton, oilseeds, paddy and sugarcane. Average size of holding in the district is 1.1 hectare. Gross irrigated area in the district is about 1.12 lakh hectares and this accounts for 43% of the gross cultivated area. On‐farm value chains were focused in Kanniwadi region of Dindugul district. Kannivadi region in Reddiarchatram block, is a semiarid region located in Dindigul district of Tamil Nadu state. Kannivadi region receives a mean annual rainfall of 845.6 mm. Geographically it is located in the 10 21’ latitude and 77 50’ longitude. The rainfall pattern in the region is traditionally classified as kanamalai (Sep‐ Oct), adaimalai (Nov‐Dec) and kodaimalai (April‐ May). Among the four standard seasons, the area benefits more from northeast monsoon (53%) and the maximum rainfall is between October‐ December. January and February are the months, which receive minimum (49.6mm) rainfall. The coefficient variation in the inter annual period is around 30 percent and the farmers in this region have a strong perception of climate variability including more frequent water‐deficit years, premature end of rains, late onset of rains, and uneven distribution. Agriculture is predominantly rain‐fed and the main agriculture season is from October‐December. Depending on availability of sub‐surface water for irrigation, in certain areas, 2‐3 crops are taken up in a year. M.S.Swaminathan Research Foundation has been working in the region for the past 15 years and hence the local men and women farmers are sensitized to sustainable development initiatives and mobilized and organised in to groups/apex organizations. Methodology: The study employed quantitative and qualitative methods to elicit information from the different players across the agricultural value chain in Dindugul District. Detailed structured interview schedule was used to collect quantitative information from on‐farm respondents. Stratified sampling technique was adopted to draw samples for baseline survey. A total of 242 farmers, 117 in Konur o
Village and 125 in Pudupatty village were surveyed. More than 50% of the farmers surveyed were women. The farmers surveyed in Konur village belong to the marginal and small category while that of Pudupatty were all marginal farmers. Inter‐sectoral approach was taken in understanding the climate risk management strategies and crop decision making across the respondents in the case study villages. Participatory rural appraisal tools like resource mapping and social mapping, seasonal calendar, trend analysis, time use: daily activities, gender roles and responsibilities with decision making profiles, access and control over resources, venn diagram, capacity and vulnerability analysis matrix were used to elicit qualitative information from both men and women farmers. While collecting the information using the tools simultaneous triangulation was done to cross check and confirm the information and statements. Adequate care was taken to select a suitable site for the discussion and time to enable the participation of women and other elder members in the community. The off‐farm players were engaged in the study through stakeholder workshops, SCF communication workshops, and one‐to‐one interviews. During the SCF communication workshops in order to convey the risk factors in terms of cost factor for easy understanding especially among farmers; decision tree analysis, decision graph method and wonder bean tool were used (Hayman et.al.2013). Results and Discussion Climate Risks and Key Decision points Impacted by Climate across Stakeholders: Rainfall was the major climate risk articulated by all the players across the value chain in the region as has been the experience in several other studies across the globe (Phillips et.al 2001). But slight variations existed between stakeholders in terms of the aspects of rainfall that were considered crucial for their respective business. Extreme weather events like droughts and floods were cited as climate risks that were impacting at a regional level. Extreme drought occurred on an average of one in ten years during the last four decades in the region. The specific rainfall related aspects of significance to farming related to late/early onset of monsoon, unequal distribution of rainfall in the season, extended dry spell after sowing, excess rainfall in peak flowering or harvesting season, untimely rains and inadequate amount of rainfall during the season. Farmers reported 100% crop loss in the event of late/early onset of monsoon and excess rainfall during peak flowering and harvesting season. For farmers and Extension Agencies like the Agriculture Department who provide support services in terms of crop management advisories, key decision points impacted by climate information are as given in Table 1. Table 1. Key Decision Points Impacted by Climate Information Key Decision Points Key Climate Variable that Informs the Decisions Time of sowing Choosing of crops/crop variety Irrigation management – in terms of timing of irrigation and quantity of water to be applied Resource Use Allocation – both labour and finance Fertiliser application – the quantity and type of fertiliser as well as the timing of application of fertilisers on crops Onset of monsoon Total rainfall and its intra‐seasonal distribution Total rainfall and its intra‐seasonal distribution Total rainfall and its intra‐seasonal distribution Distribution of rainfall across the crop growth stages Wind direction, wind speed and distribution of rainfall across the crop growth stages Distribution of rainfall during the crop maturation Time of Harvest stages Aspects like total amount of rainfall during the season, onset of monsoon and intra‐seasonal distribution of rainfall are said to influence the business decisions of input dealers. The major business decisions affected by climate/weather parameters for an input dealer are as given below: o Stocking of inputs (seeds/fertilisers/pesticides) – quantity, type and time o Transport of inputs (seeds/fertilisers/pesticides) ‐ time o Supply of Inputs (seeds/fertilisers/pesticides) – quantity, type and time Current Source of Climate Information across Stakeholders: The current climate forecasts that the stakeholders have access to are nowcast, short range, and medium range forecasts given by the Indian Meteorological Department. The forecasts given by Indian Meteorological Department are received through television, radio, news papers and mobile phones. In addition to these public sources, farmers in Kanniwadi block of Dindugul district receive medium range weather forecast information through Village Knowledge Centre and Farmer Producer Company network. Officials of Agricultural Research Stations and the Agriculture/Horticulture Department receive daily forecast information from the automatic weather stations operated and managed by Tamil Nadu Agriculture University based in Coimbatore Majority of the farmers also rely on traditional knowledge for climate related information. These include proverbs and folk songs referring to climate parameters like rainfall, wind direction and wind speed. An earlier study conducted in the region had also documented the extent of use of traditional knowledge in weather and climate related information (Rengalakshmi 2006), refer Box. Box: Traditional Knowledge on Seasonal Climate Predictions in the Study Area: In seasonal climate prediction farmers use metrological indicators: westerly wind during Adi (June ‐ July) bring rain in iyappsi (Oct‐ Nov) and if there is no rain in the summer and wind in Adi (June ‐ July) they prefer short duration crops like cowpea and they reduce their farm investment and some will invest on livestock’s especially goat. Farmers have evolved contingency cropping system as a risk aversive strategy from the climate fluctuations especially for the rainfed systems. The following example shows their crop selection skills according to the variation under rainfed agro‐ecosystem. If rain set during June‐July ‐ lablab, sorghum, redgram, groundnut, vegetable cowpea If it is late by 15 days – cowpea, fodder sorghum If it is late further by 15 days ‐ green gram and blackgram If it delays further by 15 day – Minormillets/short duration sorghum. Other decisions are mobilizing seed, fertilizer and application, decisions on sowing (early or late), land and bed preparations, mid season corrections such as reducing population /providing irrigation. Source: Rengalakshmi (2006) The lead time of these proverbs ranges from 10 minutes (short range) to 3 months (seasonal). The proverb which gives the 3 month lead time says ‘if it is windy during July‐Aug then it is sure to rain during Oct‐Nov. The proverb with the shortest lead time says, ‘if it is cloudy near the Kannivadi hills then it will rain in 10 minutes’. The farm decisions based on the proverbs is timing of land preparation, sowing, fertiliser application and harvesting. The major source of proverbs related to Timing of pesticide application climate information is people belonging to the older generation and progressive farmers. The other source of traditional knowledge cited by respondents is the almanac and the local astrologers. Utility and Reliability of the Current Climate Information: Medium range forecasts given by Indian Meteorological Department for Dindugul district is based on the readings from Agro Meteorological Field Unit (AMFU) located within Reddyarchathram Block in the district, and hence the forecasts and the advisories based on these forecasts come with 70‐80 percent accuracy. Hence they are useful and reliable for decision making. But these forecasts were found to be more representative of the plains of Dindugul while in hilly parts of the district the forecasts emanating from this AMFU were not always found to be representative. The climate information accessed through the automatic weather stations are also locale specific and hence were reported to be more accurate and reliable. Forecasts given through the media for the larger public are at a higher spatial scale – at a regional or at best district level‐ hence these forecasts are only 50 percent reliable for decision making at the micro level. Low reach and delay in communicating climate information was also cited as a major issue for non‐utility of the existing climate information for decision making. Lacks of skill in interpreting the forecasts and paucity of resources (financial as well as human) with the end user are other major challenges hindering utilization of the forecast information for climate smart management decisions by the end users. The farmers also relied on traditional knowledge for climate information. These are proverbs and folk songs referring to climate parameters like rainfall, wind direction and wind speed. The lead time of these proverbs ranges from 10 minutes to 3 months. The probable decisions that they would take based on the proverbs is timing of land preparation, sowing, fertiliser application and harvesting. The major source of proverbs related to climate information is old people in the village and other progressive farmers. The other source of traditional knowledge cited by respondents is the almanac and the local astrologers. But traditional knowledge was also said to be losing its relevance in a changing climate. Decreasing level of confidence of people on weather/climate related traditional knowledge was reported in general. Strengthening Reach of Existing Climate Information: At present, weather/climate information is being communicated through mass media like television, radio and newspaper. Amongst these channels, television has the largest reach amongst both men and women. The weather bulletins given through the television is usually given at the end of the news bulletin. The positioning of the weather bulletin was articulated by many stakeholders as a key challenge in emphasising its importance to the general public and also in terms of sustaining interest in the bulletin amongst the viewers. Airing the weather bulletins at the beginning of the news bulletin, having a dedicated weather channel and scrolling of district specific climate information at frequent intervals throughout the day were articulated as strategies to increase the reach of weather/climate information through television. Communicating weather/climate information through mobiles was suggested as one of the best mode of communication, since mobile technology has a large coverage. It was suggested that the government should make it mandatory for all service providers to disseminate weather information through mobiles and this need to be made a criterion for granting licence to the service providers. Seasonal Forecast Requirement (Parameters and Lead Times) across Stakeholders: Different stakeholders articulated different requirements for the seasonal forecast information. The climatic parameters demanded by farmers are total rainfall for the season, onset and distribution of total rainfall across the season. Climate forecasts with a maximum of 1 month lead time was felt to help in strategic on‐farm decisions. Seasonal climate forecasts with longer (2‐6 months) lead times was said to have no relevance for farmers in the region. The off‐farm players like input dealers, insurance agents and credit institutions required information with 3‐6 months lead time to make strategic decisions. Input companies and district level wholesale dealers of inputs saw a lot of potential for strategic business planning and risk reduction if forecasts are given 6 months before the start of the season. The sub‐dealers at the block level demanded forecast information with a lead time of 1 to 2 months. Seasonal forecasts specific to a region was found to be of less significance for players beyond farm gate like the district level traders in agricultural commodities. Their scale of operation was much beyond the region and hence weather/climate variability in one region is not going to affect their business. But regional level climate variability will have an impact on small level traders whose scale of operation was within the region. These include the local village level traders. These traders most of the time double as input dealers and supply seeds and fertilisers to the farmers and enter into a buy‐back arrangement with farmers. The local level traders required information on total rainfall and its distribution with a 1 month lead time. Communicating Seasonal Climate Forecasts: Role of Decision Analysis: Communicating SCF is a challenge since the forecast is probabilistic in nature. The short and medium range forecasts given by Indian Meteorological Department are deterministic in nature and are easier to communicate as they quantify the weather parameters. Moreover they are given for short periods of time. The probabilistic forecasts give chances of occurrence of climate events over long period of time. What is important in communicating seasonal forecast is making potential users understand the risk factor. In SCF when one says there will be 40% chance of a normal rainfall season, one need to understand that there is 60% chance of this not happening. This aspect of SCF is crucial and need to be communicated very clearly to the end users. Another issue with seasonal climate forecast is that if they are misunderstood as deterministic forecasts and is adjusted to one outcome in the mind of the decision maker it can lead to poorer risk management decisions. This is a worse off scenario than one in which the farmer has no climate information at all. In the absence of a forecast, a farmer may plan for a wide range of outcomes (Hayman 2011). Conventional methods of forecast communication like weather bulletins through mass media is of less use in communicating forecasts of probabilistic nature. In the pilot study we adopted a decision analysis framework to communicate seasonal climate forecast. Decision analysis framework helps communicate forecasts of probabilistic nature and also serve as decision support tools in assessing the value of seasonal climate forecasts against multiple criteria. The analysis framework is useful in working out trade‐offs between competing objectives and helps compare relative profitability of the probable decisions/choices that the respondents make based on the forecasts (Carberry et.al.2000). Decision support tools like decision trees, decision graphs and wonder‐bean were used to elicit utility of seasonal climate forecast from different stakeholders. Decision Tree: Process and Outcomes Decision tree is a decision support tool that uses a tree‐like model or graph to map decisions and the possible decision outcomes. This helps the end user to envisage the consequences of each decision, the resource cost that go into it and the consequence of decision in economic terms. The decision tree also allows for adding complexities to the decision making process. The exercise was conducted at two levels, 1) on‐farm and 2) off‐farm. The on‐farm exercise was conducted with farmers and the off‐farm was done with members of Farmer Producer Company in the region. The farmers were asked to articulate the crops that were taken by majority of farmers during the current agricultural season. The crops that were taken by a large majority of small and marginal farmers were cotton and maize. The crop choice was dependent on whether they had access to irrigation or not. The previous year the region received poor rainfall and farmers under rainfed farming systems had sown maize while those who had access to irrigation cultivated cotton. There were farmers who had planted vegetables, and these were also farmers with access to irrigation to an extent. The farmers were asked to articulate the crops that were taken by majority of farmers during the current agricultural season. The crops that were taken by a large majority of small and marginal farmers were cotton and maize. The crop choice was dependent on whether they had access to irrigation or not. The previous year the region received poor rainfall and farmers under rainfed farming systems had sown maize while those who had access to irrigation cultivated cotton. There were farmers who had planted vegetables, and these were also farmers with access to irrigation to an extent. Based on this discussion, the crop choices taken up for the Decision Analysis exercise were cotton, maize and ladies finger (the major vegetable crop). In the subsequent exercise taken for varietal choices within crops, cotton and maize were the crops that were considered. Ladies finger was dropped as it came out from the first exercise that though this is a popular and profitable crop it was not generally preferred by the farmers since it require more labour (for plucking the pods and marketing) and needs assured irrigation. Plucking of the pod is a labourious process and should be done at the early hours and farmers with access to family labour only prefer this crop. Given these constraints, vegetables could be taken on only very limited area. Once the crop choice was made, the farmers and the members of the producer company were asked to articulate the most climatically risky decisions that they make with respect to the above mentioned crops. Table 2 and 3 gives details of the climatically risky decision taken by farmers and the Producer Company. Table 2: Climatically Risky Farm Level Decisions Climatically Risky Decisions Decision Maker
Timing of Decision
Land allocation – Crop Choice Farmer 15 days to 1 month before the beginning of season Variety Choice Farmer 15 days to 1 month before the beginning of season Top dressing of fertilizer Farmer
During the crop season
Harvesting Farmer Towards the end of the crop season Mobilizing labour for different Farmer Throughout the season activities Table 3: Climatically Risky Off‐Farm Decisions
Climatically Risky Decisions Decision Maker
Timing of Decision Advance booking of seeds – Crop Input Dealer/Farmer Producer 6 months before the beginning of crop season – Input Dealer Choice‐ Deciding the percentage Company 3 months before the beginning of crop weightage to be given to each of the season – Farmer Producer Company crop Advance Booking ‐ Varietal Choice within crops Input Dealer/Farmer Producer Company 6 months before the beginning of crop season – Input Dealer 3 months before the beginning of crop season – Farmer Producer Company
Rationing of supply to dealers – in terms of quantity distributed and the frequency Lifting the inputs for distribution – (quantity as well as frequency) Mode of Distribution – for Cash/Credit Input Dealer
3 months before the beginning of crop season – Input Dealer Farmer Producer Company
1 to 1.5 months before the beginning of crop season Input Dealer/Farmer Producer Company 3 months before the beginning of crop season – Input dealer 1 to 1.5 months before the beginning of crop season Following this exercise, the concept of seasonal climate forecast was explained to the participants. The difference between the short, medium, extended range forecasts and seasonal climate forecasts were explained to the participants. The differences in deterministic and probability forecasts were also explained. Capacity of the participants on deciphering probabilistic seasonal climate forecasts was built through representing different probabilities of rainfall for the season starting from climatology scenario of equal chances of normal, dry and wet season and varying the probabilities to a great degree among the different options. In order to define the normal season farmers were asked to share their perceptions based on their field and practical experiences. According to them it was 15 plough rainfall (1 plough rainfall is 25 mm). Later it was compared with the average monthly rainfall of last 60 years. Based on the comparison, 375 mm was considered as normal seasonal rain for the season between September/October to December. This helped the farmers to understand what is meant by normal rain while discussing the seasonal climate forecast. After this, the excel sheet with the decision tree framework was run through for the benefit of the participants. They were given probability forecast of 60% normal season, 20% dry season and 20% wet season. Given this scenario the farmers were asked to articulate their crop choice decisions as a first step and the varietal choice within the crop choices as an added complexity in the second step. The producer company members were asked to articulate their business plan, given the probability forecasts. The outcomes of the exercise with farmers are given in Table 6a, 6b (given in Annexure); Chart 1&2. Outcome of the exercise with Producer Company members is given in Table 7. Table 4 gives the biophysical assumptions made by the participants for the decision. Table 5a & 5b gives the economic assumptions used for maize and cotton respectively in the decision tree. Table 4: Biophysical information Season Below Normal Normal Above normal Season Maize (tons/acre) Kaveri‐
Hyshell Super 244 3 0.2 3 2
3.5 3 Cotton (tons/acre) Kaveri‐Jackpot Kaveri‐ATM (Irrigated) 1.5 0.8 1.5
1.5 1.5 1.8 Table 5(a): Economic Assumptions – Maize
Rainfed Irrigated Maize Irrigated Maize ‐ Maize‐Hyshell Price ‐ Kaveri‐
Kaveri‐Super 244 Super 244 growing cost+ harvest Price (Rs/ton) (Rs/ton) cost) (Rs/ha) Rainfed Maize‐
Hyshell‐ growing cost+ harvest cost) (Rs/ha) Below normal Normal Above normal 13000
13000
13000
22000
22000
22000
13000 13000 13000 19000
21000
22000
Table 5(b). Economic Assumptions ‐ Cotton Rainfed Cotton‐
Irrigated Cotton Irrigated Cotton ‐
Hyshell Price Price ‐ Kaveri‐
Kaveri‐Jackpot Jackpot (Rs/ton) (growing cost+ harvest cost) (Rs/ha (Rs/ton) Rainfed Cotton‐
Hyshell‐ (growing cost+ harvest cost) (Rs/ha) Season Below normal 40000
25000
40000 16000
Normal 40000
25000
40000 20000
Above normal 40000
25000
40000 20000
Outcomes of the decision for the different branches of the decision tree: Farm Level (Refer Table 6a & 6b given in Annexure) In the first stage of the exercise, 3 crops that were ranked major crops during the 2012‐13 agricultural season in the region was taken up for analysis. The crops ranked were maize, cotton and vegetables. Among vegetables ladies finger the most commonly cultivated vegetable in the region was considered for the analysis. The scenario given to the farmers was 60 % chance of normal rainfall, 20 % chance of above normal and 20% chance of below normal rainfall. Table 6a gives the outcomes of the decisions for the different scenarios. Ladies finger comes out as the best crop choice in terms of gross margin to the farmer. But surprisingly not many farmers opt for ladies finger in the region, the factors for this being purely non‐climatic. The labour requirement for ladies finger in terms of weeding and harvesting operations are high. Farmers commented that it can be taken only on very small acreage and with family labour. Ladies finger is not a viable option if the farmers have to depend on wage labour for its operations. This ultimately leaves cotton and maize as the ideal crop choice for the region for the majority of small and marginal farmers under rainfed situations. And between cotton and maize, cotton fetches a higher gross margin than maize under the different rainfall scenarios considered for analysis. But the advantage of maize is that since the crop is of short duration, the farmer can take more than one crop during the season with the residual moisture in the soil and have access to limited irrigation through wells. Farmers who can provide critical irrigation for the crop can take it during the second and third season Table 6b gives the decision on varietal choice within the crops under irrigated and rainfed farming systems. The exercise was done for the two major crops in the region namely, cotton and maize. Farmers articulated varietal choices under irrigated and rainfed farming conditions for maize and cotton. Kaveri‐Jackpot, the irrigated cotton comes out as the best choice in this region. This is followed by rainfed cotton, Kaveri‐ATM, Irrigated maize‐Kaveri Super 244. The least profitable option is rainfed maize the probability weighted average of which is just 13% of the best option among the choices considered for analysis. Maize is a water sensitive crop and hence maize yield will be severely affected under above normal rainfall condition in both rainfed and irrigated condition due to water stagnation as well as rain during the flowering season which affects the pollination and seed setting. Normally higher rainfall especially in the flowering period leads to small size grains which results in less test weight. Such grains have low market value and fetches lesser price. Chart 1 plots the gross margins of the different varieties across the different rainfall scenarios. Chart 1 clearly brings out the fact that irrigated cotton performs best across normal, above normal and below normal rainfall conditions. Irrigated cotton receives the same amount of gross margins irrespective of the rainfall conditions. This is because the total growing cost of irrigated cotton doesn’t change with excess or below normal rainfall situation. There is no yield variation under different rainfall scenarios and the price of cotton remains almost the same irrespective of the rainfall conditions. On the other hand, rainfed cotton performs best during above normal rainfall condition. During the above normal rainfall condition, the gross margin realized by rainfed cotton is only Rs.3000 less than irrigated cotton. Irrigated maize performs well during normal and below normal rainfall conditions, since farmers are able to provide critical irrigations for irrigated maize even during below normal rainfall conditions. Rainfed maize performs best during normal condition and is affected during below normal and above normal conditions. Chart 2 brings out the percentage share of the gross margin of the different varieties to the gross margin of the best option. The best option among the varieties considered is irrigated cotton‐ Kaveri Jackpot. Rainfed cotton is 75% of the best option, irrigated maize is 47% of the best option and rainfed maize is 13% of the best option. Outcomes of the decision for the different branches of the decision tree: Off‐Farm Level The off‐farm actor considered here is the Reddiyarchathram Sustainable Agriculture Producer Company Limited (RESAPCOL) – a farmer producer company based in Kannivadi. Input sourcing and supply for farmers is one of the activities of the producer company. The climatically risky decision for Producer Company is managing the seed supply chain in the region. Table 6 gives the outcomes of the decision analysis exercise taken up with the representatives of Farmer Producer Company. The demand side decision that needs to be taken as input sourcing agents by the producer company is advance booking of seeds for the season. Quantity of the different crops and the varieties within the crops that should be procured, phasing out the quantity to be distributed across the period, and the nature of transaction (cash/credit) to be followed in seed supply are the key supply side decisions that should be taken by the producer company. During a normal season, the producer company would make an advance booking for 5 tons of seeds. Of this, 60% would be maize seeds, 30% cotton seeds and 10% ladies finger. Actual amount that would be purchased before a normal season is 3 tons, 60 % of the total advance booking amount. The producer company would sell seeds and other inputs on credit to the farmers. Rainfall Scenario Below normal Normal Above Normal Table 7. Outcome of Decision Analysis Exercise with Farmer Producer Company Quantity Advance purchased Quantity booking: 2 months distributed Mode and seed Contingency arrangement before the to sub time of quantity Probability season dealers payment (ton) (percentage) 20 3.5 60 5 20 7 50% maize 60% maize 55% maize 45% cotton 30% cotton 35% cotton 5% ladies finger 10% ladies finger 10% ladies finger 0.9 3 5 25 % of demand 60% of demand 70% of demand Cash on delivery Credit Credit During a below normal season, advance booking made will be only for 3.5 tons, 70 % of the quantity that would be booked during a normal season. Of this the distribution of the different seeds would be 50% maize, 45% cotton and 5% ladies finger. The amount that would be purchased 2 months before season is 25% of the total booking, roughly about 0.9 tons. During a below normal season no credit would be extended to farmers for buying seeds. Seeds will be supplied by the company only on immediate cash remittance by the farmers. During an above normal season, 7 tons of seeds would be booked in advance, 140% more than the quantity that would be booked during normal season. Of this, 55% would be maize, 35% cotton, and 10% ladies finger. 70% of the advance booking, which is 5 tons of the seeds, will be lifted by the producer company 2 months before the season. The company would be willing to distribute the seeds on credit to the farmers. Thus the probable decisions that the farmers would base on SCF is crop choice and crop variety choice. For the farmers of Dindugul region it was a choice between cotton and maize. Decisions related to crop management during the crop cycle were based on the day to day weather conditions. For the company dealing in inputs, SCF for a region helps in placing their products as well as in deciding the quota of advance booking for the company dealers in that region. For the district level wholesale dealer of inputs, SCF was more useful in terms of determining the stock of seeds to be maintained before the beginning of the season. It helps them decide crop choices, crop varietal choices, and the combination of the different crops to be maintained. It also helps them decide the mode of payment to be engaged in with the sub‐dealers. If SCF forecasts a good season, they would extend inputs on credit to the sub‐dealers, and if a bad season is forecasted they would prefer to engage in cash‐and‐carry method. Depending on the forecast they would also decide the volume of transaction with sub‐dealers. The credit institutions would use the information to decide the target for crop loans for the region as well as deciding on the loan amount to be extended for each crop. The credit institutions will use the information for withholding or pushing the crop loans depending on the season. Similar experiences have been reported from Brazil Lemos et.al. 2002) and Zimbabwe (Phillips et.al. 2002. Similarly the insurance agents will also use the information to plan their targets and reimbursement plans. Relevance of SCF Communication Tool and SCF for Strategic Planning across Stakeholders: The importance of non‐climatic factors on strategic decisions was emphasized by most of the players. In the case of farmers, it was the resourcefulness of the individual farmer and their coping capacity that were key drivers of crop choice or crop varietal choice decisions. The farmers articulated that the production decisions and outcomes are determined by climate variables as well as agronomic and economic factors such as labour, animal traction, credit etc. The communication tool cum planning tool used does not account for the non‐climatic factors. Hence the decisions/outcomes of these exercises may not be pertinent to and more often than not is disconnected from the real life agricultural decisions. While facilitating the session, it was observed the farmers felt difficulty in articulating their strategic decisions with respect to committed and non‐committed costs. This was because farmers normally based their committed and non‐committed costs decisions on observation and assessment of actual climatic occurrences in the field. Moreover the climate information given to them (probability of a normal/below normal/above normal rainfall year) did not tell them anything about the intraseasonal distribution and variation. Several of the non‐committed cost decisions like intercultivation, plant protection and top dressing are decisions that are taken on the basis of the intraseasonal distribution and variation of rainfall. Further from the discussion it emerges that that majority of key farm decisions are taken in very short lead times. Irrespective of any forecast (scientific or traditional) farmers prepare the land in anticipation of the season. Given the edaphic conditions choice of the crop as well as variety is based on the onset of the rainfall prior to sowing. With respect to crop choices under rainfed situation, they take up either cotton or maize the deciding factors being market price of the previous year/season and availability of water to give one or two irrigation at critical stages of the crop (in case of cotton). With respect to sowing decisions, if the onset is normal, i.e receiving atleast 50 mm of rainfall within 3‐5 days of interval, farmers take initiative to purchase seeds, mostly hybrid seeds. Such seeds are available in the market from different sources for immediate cash payment as well as credit. Planning for intercultural operations, top dressing and harvesting are purely based on how the season progresses. For Example this year expecting a normal rainfall they have sown the seeds, but due to long dry spell after sowing, decisions related to intercultural and fertilizer application are not followed. Such decisions does not require prior preparation and are taken based on actual occurrences. For the off‐farm player, the Farmer Producer Company and big multinational input companies, strategic planning was done on the basis of their historic sales data, the standard acreage under each crop in the region, the ground level data supplied by their field assistants and data on the existing market share of their competitors. The wholesale dealers will not incur heavy losses even if the forecast information goes wrong because before the beginning of the season only bookings and commitments are taken up and real transaction in commodities happen after the commencement of the season or just about when it starts. Value of SCF for End Users1: In the study attempt was also made to capture the value of seasonal climate forecast for off‐farm players across the value chain. A mixed‐methods approach was utilised in order to explore in detail the end users perceptions of SCF, its benefits and limitations in use. Table 8. Illustration of SCF A verbal explanation of SCF was made for each Probabilities Climate Scenario respondent, and in order to explain and assist 60% Below Normal Rainfall respondents in understanding what seasonal climate 20% Normal Rainfall forecasting is like, an illustration was provided 20% Above Normal Rainfall alongside verbal explanations. The probabilities and scenarios are varied to encourage respondents to consider the information critically; this enabled more detailed discussion of the utility of SCF. Our study shows that that 16 of the 18 participants did not fully understand seasonal climate forecasting in the manner it was presented and explained during interviews (Refer Table 9 given in Annexure). There were also cases of confusion, where a participant would initially grasp the concept of probability, but revert back to discussing decisions on the basis of volume of rainfall. The two participants who clearly understood seasonal climate forecasts were one’s who have been associated in the project right from the launch of the project. One of them felt that probabilities of rain were not useful enough to base decisions on. Interestingly enough, 11 out of the 16 who did not understand the information, stated that they consider seasonal climate forecasting useful. This also shows how the information can be wrongly interpreted if the information is not presented after a full explanation of the concept of probabilities and risks. The table shows the probable decisions that will be based on SCF by the different players. Table 10. Probable Decisions based on SCF for Different Players, Dindugul District Off‐farm Player and Stage Probable Decision based on SCF Input Wholesalers Stocking Rate (Managing Inventory) Producer (Farmer) Land allocation & Crop Choice Produce Aggregator (Cotton) Plan/influence produce turnover volumes Produce Aggregator Forecast Volume of Incoming Stock (Vegetables) Processor (Cotton) Help predict cotton quality Financial Institution (Banks)
Decide on lending ceilings for different crops Wholesale Marketing Not useful Limitation in use of SCF for Decision Making: One of the key limitations of SCF as articulated by some respondents is that it is based on likelihood, which according to them is an assumption. The other limitation articulated is that SCF does not provide details like location of rains, the timing, lead times, duration and rainfall volumes which are according to the respondents key variables that are required to make decisions. Combined with this, the difficulty in understanding the meaning of percentage likelihood of an event occurring, makes it difficult for the respondents to gauge how they would use SCF for decision‐making. 1
This section is based on a study conducted by project partners as part of the project to understand the value of seasonal climate forecast for players across the value chain with a focus on off‐farm value chain. The outcomes of the study form part of a separate report which is yet to be published. Suggestions for Making SCF Relevant for stakeholders: Concerted efforts in the nature of capacity building exercises need to be taken up to build the ability of the end user in interpreting and using SCF for strategic decision making. There should be flexibility in lead time in which SCF is released. SCF with 6 months lead time need to be followed up with SCF with shorter lead times ranging from 1 to 3 months. This will cater to the requirement of the different stakeholders and increase the utility of forecast information for planning and management decisions. To increase stakeholder confidence in the seasonal climate forecasts the scale of forecast need to be at the block level irrespective of the time scales or the climate parameters considered. SCF need to be a seamless forecast that combines with already existing short, medium and extended range weather forecast. Forecast models which give SCF should also take into account the dynamics of the microclimate of the region as well as any striking changes in climate variables that happens after a major extreme climate event like tsunami. SCF forecasts should combine traditional knowledge of the region with climate science. SCF should be given along with advisories that build mitigation aspects directed at climate risk reduction. Conclusion: Seasonal climate forecasts can to a certain extent help in risk reducing decisions across the agricultural value chain in the study region. And seasonal climate information is always considered as one of a suite of information on climate and weather that players across the value chain might use in order to make decisions. But incorporation of SCF in decision making depends a lot on the capacity of the end users in accessing and understanding the forecasts. Further, field level adaptation action based on these decisions is dependent on the resource capacity of the end users and the presence of an enabling environment (Glantz 1977, Hansen, 2002). In order to enhance the usefulness of climate forecast information and advisories it is essential to identify the decision‐
relevant attributes of forecast information for particular activities and associated actors in the value chain and to encourage forecasters to provide information with those attributes. Apart from generating appropriate forecast information, communication is another crucial element decides its use and hence user appropriate participatory approach should be selected. Thus SCF generated and disseminated for the region need to be flexible in terms of lead times and complement the existing short and medium range weather forecasts. Efforts at building the capacity of the end user in understanding, interpreting and using the forecast information for decision making need to be taken up for realizing better utility of SCF in the region. The flip side of SCF in the region is that it might sometimes undermine food security in the region by adding on the vulnerability of the primary producers. The forecast information is being put to different purposes by players across the value chain. Some of these decisions may be complementary to the primary producer while some can be counterproductive to the primary producer. For example the credit agencies decision to limit crop lending in anticipation of a forecasted bad season can be very detrimental to the primary producers. Experience in Brazil (Lemos et.al. 2002) and Zimbabwe (Phillips et.al. 2002) financial institutions extending agricultural credit tightened their credit in response to a forecast for increased probability of drought. Similarly, the decision to not promote crop insurance in anticipation of a forecasted bad season by an insurance company can be counterproductive to the primary producers. Hence efforts at strengthening climate resilience at a regional level need to factor in all these complementary and competitive engagements amongst the different stakeholder to ensure a win‐win situation for all the players across the value chain. Annexures Table 6a. Outcome of Decision Analysis – Crop Choice Decision in Study Region Probable Weighted Average 33000 38200 % of best option Maize 48% Cotton 55% Probability
Rainfall Scenarios Normal Above Normal below normal Normal Above Normal below normal Probability 0.6
0.2
0.2
0.6
0.2
0.2
Growing Gross Probable Yield Income Cost/acre Margin Weighted (t/acre) Price Rs/ton (Rupees) (Rupees) (Rupees) Average 4
13000
52000
12000
40000
33000
3
13000
39000
13000
26000 2
15000
30000
11000
19000 1.5
40000
60000
16000
44000
38200
1.5
30000
45000
16000
29000 1
45000
45000
15000
30000 Ladies 69400 finger Normal 0.6
7
15000
105000
100% Above Normal 0.2
7
15000
105000
below normal 0.2
4
18000
72000
Note: The yield articulated in the case of maize and cotton is average yield (irrigated & rainfed) 30000
30000
25000
75000
75000 47000 69400
Table 6b. Outcome of Decision Analysis – Crop Variety Choice Decision under Irrigated and Rainfed Farming Systems in Tamil Nadu Case Study Region
Crop/ Probability % of Farming Varietal Rainfall best weighted Scenario
Option System Choice average Kaveri‐ Super 244 normal
16480 47% Maize above Irrigated normal
below normal
4680 13% Rainfed Hyshell normal
above normal
below normal
Cotton Kaveri‐
35000 100% Irrigated Jackpot normal
above normal
below normal
Kaveri‐
26400 75% Rainfed ATM normal
above normal
below normal
Committed cost (Fym, land preparation, seed, basal fertilizer and labour for all operations)
Non‐
committed cost (top dressing, intercultivati
on, plant protection)
Total growi
ng cost
Gross Margi
n
Probabili
ty Weighte
d Average
16480
Proba
bility
Yield (ton/ac
re)
Price (Rs/to
n)
Income (Rupees) 0.6
3
13000
39000 18000
4000
22000
17000
0.2
3.5
13000
45500 18000
4000
22000
14400
0.2
0.6
3
2
13000
13000
39000 26000 18000
17000
4000
4000
22000
21000
17000
7600
0.2
3
13000
39000 18000
4000
22000
4000
0.2
1.2
13000
15600 16000
3000
19000
‐3400
4680
0.6
1.5
40000
60000 15000
10000
25000
35000
0.2
1.5
40000
60000 15000
10000
25000
35000
0.2
1.5
40000
60000 15000
10000
25000
35000
0.6
1.5
40000
60000 12000
8000
20000
28000
0.2
1.8
40000
72000 12000
8000
20000
32000
0.2
0.8
40000
32000 10000
6000
16000
16000
35000 26400 Table 9. Response Across the Supply Chain to Level of Understanding and Perceived Potential Use of Seasonal Climate Information
Perception of Respondents articulation as to whether they Understood the Potential Use of the Climate Information climate Information given Activity Stages/Players across the Supply Chian Government ‐ input supply Agriculture advice & seedling supply
NO
YES
Government Advice & innovation demonstration NO
YES
YES
YES
conf2
YES
Support ‐ training Farm school & farming
& extension Support ‐ Banking Credit planning, development &regulation Input supply Seed company NO
NO
Input supply Seed wholesale distributor
NO
YES
Input supply ‐ wholesale Wholesaler, seeds, fertiliser & agro‐chemicals YES
YES
Input supply ‐ retail Seed, fertiliser & pesticide retailer YES
NO
Input supply & services Agri‐clinic ‐ seeds, pesticides, fertiliser NO
NO
Input supply Input marketing, organic fertilizers NO
YES
Farmer Cotton, corn, fodder NO
YES
Farmer Maize & other crops NO
NO
Farmer organisation As individuals, farmers; as a group, input suppliers & traders
Mixed (conf) 2
YES
Aggregator & seed supply Seed supply, farming & cotton trading NO
YES
Aggregator & farmer Maize & cotton trader, farmer
Conf2
YES
Wholesale Vegetable auction; commission mandy NO
NO
Processing Cotton trading & pre‐ginning
NO
NO
Trading Government regulated marketing ‐ commodities NO
NO
Total Yes 3
11
No 12
7
2
Confused 3
Total number of interviews
18
Source: Adapted from Lim‐Camacho et.al. (yet to be published) 18
2
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