Tanveer A. Butt and Alpha O. Kergna

This report is preliminary as it uses experimental data on PSSA technologies. While data on actual farm level application of the PSSA technologies is awaited, this report provides an overall view of the variety of impacts that the technologies might have on the Malian agriculture sector.

1. Introduction

The Special Program for Food Security (PSSA) is an FAO initiative in countries with low income and low food production. The objective of this initiative is to increase production of food crops by introducing new technology with the ultimate objective of improving food security conditions in these countries. The other complementing objectives of PSSA program are to improve access to rural credit, marketing and processing of agricultural produce, and enhancing capital investment in the agriculture sector.

2. Objective

The objective of this study is to provide a preliminary but a countrywide assessment of the economic impact of new crop technologies for rice and maize in Mali. The analysis focuses on economic impact assessment of these new technologies considered by the PSSA initiative. Two reflections of the impact assessment have been attempted by this study. One, it considers consumer and producer surplus which are established measures of economic welfare in any economic analysis. Second, it provides detailed regional results on prices and production of food crops rendering us great insight into the effects of new technology on food security - the availability of food at adequate prices.

Three new rice varieties are considered for Mopti, one new maize variety in Kayes and two in Koulikoro. Table 1 provides a comparison between yields of existing technology of these crops and the new technologies being considered in the study;

As Table 1 shows, there is a substantial increase in yield for both the crops and for all of their varieties considered.

3. Methodology

The analytical framework is provided by Mali Agriculture Sector Model (MASM), a mathematical programming model that simulates the behavior of key economic agents such as consumers, producers and possessors in response to prices, technology, and policy changes. The simulation is carried out in a structure that explicitly considers objectives of economic agents such as individual welfare maximization, profit maximization etc. and it explicitly recognizes system constraints such as maximum amount of a resources, e.g. land and labor, that can be used for production and the available production technologies. Thus production is carried out by using alternative technologies that combine limited resources and purchased inputs for profit maximization. The commodities thus produced are offered for sale in the market, once requirements for consumption of farm families have been met, to the urban consumers in cities and towns. These consumers make purchases of commodities produced at prices determined by supply-demand conditions. Thus MASM simulates the market equilibrium by determining a price at which supply and demand are balanced for each commodity in each region.

The simulation of agricultural production/consumption is carried out countrywide and accounts for regional delineation. Hence, production, consumption, and prices are simulated at regional level and then aggregated up to national level. Features have been added to MASM that consider inter-regional dependence on food production by allowing food to be transported from surplus regions to deficit regions. Besides domestic production and consumption, MASM also simulates trade of major food commodities. The output of MASM contains estimates of agricultural commodity prices and quantities, input use, land use and crop mixes, export/import, consumer and producer surpluses etc.

Since risk is a key feature of agriculture in Mali, MASM considers five different states of natures, each reflecting varying weather pattern and the associated yield loss/gain. MASM simulates producer’s risk aversion by penalizing those production alternatives that increase variance in revenue. In stochastic modeling jargon, this approach is comparable to standard E-V model. Thus MASM considers the trade-off between ‘high return-high risk’ and vice-versa.

For the purpose of this study, budgets for the new varieties for rice and millet in Mopti, Kayes and Koulikoro were obtained from PSSP study. The existing MASM budgets were replaced with these new budgets. Mainly these new technologies were high yielding varieties (Table 1) requiring intensive input use. However, no information was available about the risk characteristic of these new varieties. As a result, expected value maximization version of MASM, rather than risk averse, was used for the analysis.

4. The Base Model

The base model simulates demand and supply conditions of 1996 and its solution corresponds very well to the observed prices and production of primary commodities as is shown in Table 2.

For millet, price and production of MASM base solution deviated from the observed 1997 values by 9.47 and 5.87 percent, respectively. Whereas, for rice the deviations for price and quantity were 3.27 and -11.16 percent, respectively. Deviations for other crops are also on the low side. Thus MASM simulates 1996 production and prices for crops very well which lends great confidence for using the model for impact assessment studies such as the one at hand.

5. Results

Increase in yields due to new crop variety is expected to change crop mix, increase production, reduce prices, shift comparative advantage across regions for production of different crops etc. Given the supply and demand parameters, these changes can be used to calculate various indicators of economic impact of new technology. In this section, we summarize national level results on important economic indicators while detailed results giving regional level impact can be found in the appendix.

A review of standard supply-demand analysis framework would reveal that improved technology shifts the supply curve to the right causing prices to fall and production to increase. As a result, consumers always gain while gains to producers are uncertain and depend on how sensitive demand is to falling prices. In case of food commodities, it is generally known that even a substantial fall in prices may only moderately increase in quantity demanded. Thus producer’s revenue may fall in case of food commodities as prices fall more than the quantity demanded would increase. These are the results that we found in our analysis as well.

5.1 Prices, Area, and Production

Table 3 summarizes national level results on prices, area and production for major crops in Mali when new technologies are introduced. A glance at table 2 reveals that there is an overall decline in prices and increase in production, and a shift in area allocation among competing crops.

Table 3. Prices, Area and Production

There is a substantial fall in prices of all the commodities. The prices of maize and rice declined by 60.64 and 88.22 percent. The range of fall in prices for all other commodities is from 11 to 25 percent. It is important to mention here that the MASM run that considers new varieties for maize and rice is based on the assumption of full adoption of technology for these two crops. Where full adoption means that the new varieties completely replace the existing varieties. This is not a tenable assumption as in reality the adoption of new varieties is not instantaneous. A 25 percent adoption of these technologies in 3 to 5 years time may even be considered an optimistic case. Whereas full adoption may be achieved in a couple of decades. Though important, adoption is beyond the scope of this preliminary assessment of these technologies.

In response to substantially lower prices compared to base MASM run, area under maize and rice decreased by 30.53 and 35.94 percent, respectively. Whereas area under other crops increased. Area under cotton increased by 3.22 percent while under sorghum it increased by 21.01 percent. This change in crop mix reflects changing price and yield effects that MASM simulates with the objective of maximizing production efficiency in terms of maximum obtainable producers and consumer surpluses.

Despite the decline in area for maize and rice, production increased for both the crops as new technologies have substantially higher yields compared to base yields. Production of maize increased by 15 percent while for rice it more than doubled. Production of sorghum increased by 16.22 percent due to increase in area under the crop. Moderate increases were observed for other crops as well due to increase in their area.

As expected, the production increase was much less than the fall in prices, for example, price of maize fell by 60.64 percent whereas its production increased only by 15 percent. This is because in order to balance supply and demand in the market, MASM restricts production to the maximum amount that can be sold in the market.

Though the above table adequately captures the overall impact of technology on area, production and prices, it certainly masks important shifts in interregional comparative advantage. For example, in Segou production of rice and maize declined and increased for sorghum and millet suggesting that comparative advantage of producing maize and rice shifted to Mopti, Kayes and Koulikoro and that of sorghum and millet shifted to Segou. Similarly, production of groundnuts decreased by 67 percent in Kayes which was replaced by its higher production in Mopti. Interregional shipments also provide more information to the changing comparative advantage across regions. For example, despite the decline in production in rice, maize and cotton in Segou, prices for these crops fell in that region due to increased volume of shipments from other production regions of Mali. The tables in the appendix provide insight into regional level impact of the new technologies considered in this study.

A caution has to be exercised while interpreting the results of the impact assessment of new technology especially on prices. There are three major reasons for drastically low prices. First, MASM simulates demand conditions that existed in 1996. Introducing new technology with yield potential as high as four times the existing yields without any demand expansion would produce market glut. Second, we assumed instantaneous adoption meaning that producers do not face any socio-economic conditions that may stand barrier to the adoption of new technology. In other words, all the producers adopt the technology as and when it is available. Third, farming at research stations is under ideal conditions and hence the yields at research stations are always higher than those at farmers’ field. In this study, we assumed that farmers would be able to have the same yields as is on the research station. Thus the last two conditions when combined with the first one would result in surplus production leading to drastically low prices. One must address technology adoption and changes in demand and its composition together e.g. addressing the question ‘how much adoption do we expect for these varieties of maize and rice in say 5 years and what would be the overall food demand its composition’. We believe that once adoption and demand issues are considered, the nature of the results will not change but the quantitative estimates will.

5.2 Gross Revenue From Crop Production

In this section we present gross revenue to producers of undertaking crop production activity. The impact on producers’ revenue is provided at two price levels as is shown in table 4.

Table 4 Gross Revenue to Producers (Billions CFA)

At constant prices revenue from production of all crops have increased showing the effect of increased production on producers’ revenue. On the other hand, at variable prices, revenues are substantially lower for maize and rice and moderately lower for all other crops as price have fallen considerably without an equivalent increase in quantity demanded.

5.3 The State of Undernourishment

According to the FAO State of Food Insecurity report (SOFI 2000), there are 826 million undernourished people in the world, of which 792 million reside in developing countries. The condition is most prevalent in Sub-Saharan Africa where 34 percent of the overall population is undernourished. According to the same report, the incidence of undernourishment increased in Mali from 24 percent to 32 percent between 1990-92 to 1996-98. This highlights the importance developing new crop technologies that can increase the availability of food in Mali.

FAO has established methodologies for developing food insecurity estimates that factor in overall food production, post-harvest losses, export/import of food etc and accounts for total food available in a country. Estimates are then derived for measuring Dietary Energy Supplement (DES) available in kilocalories per person per year contained in the food available. The DES measure is on average basis. It ignores the fact that people have unequal access to food. For example, people with higher incomes have better access to food than those with lower incomes. Therefore, the methodologies for developing food insecurity estimates combine a measure of inequality in access to food with the average DES available and the minimum DES required. The estimates of food insecurity are reported for percentage of population that falls below the minimum required DES and is, therefore, undernourished. Since ASM covers seven major crops that supply over 85 percent of the DES available in the country, we can use the ASM results in conjunction with the FAO methodology to develop estimates of alterations of food insecurity under new technology. In particular, adjusting for the small proportion of DES not covered by ASM commodities, the base level undernourishment predicted by base ASM at the country level was found to be 31.6 percent which is close to the FAO estimate of 34 percent. The ASM sub-regional detail permits us to make DES estimates regionally and hence identifying ‘hot spots’ of undernourishment. Table 5 provides an estimate of DES in Mali and the proportion and number of undernourished people in the pre and post technology scenario.

Table 5 State of Undernourishment in Mali

Table 5 shows that the adoption of new technologies discussed in this report is likely to produce dramatic results in reducing the incidence of undernourishment. The overall country level undernourishment reduced from 31.6 percent (2.95 million people) in Mali to only 8.81 percent (0.95 million people) with the adoption of new technologies. The results show that since regional markets are integrated, all regions of Mali gain from new maize and rice technologies introduced in Kayes, Koulikoro and Mopti. The regions that gain the most from these technologies, from nutrition standpoint, are Segou and Mopti with a reduction of 86 and 83 percent, respectively, in undernourishment. The smallest effect is in Gao but still enabling the region to reduce its undernourishment by 48 percent. The results show that the adoption of these technologies could contribute substantially towards meeting the World Food Summit goal of reducing world hunger by half by 2015.

5.4 Aggregate Measure of Economic Welfare

As described earlier, besides prices, production, crop mix etc. the output of MASM also provides an estimate of measures of economic value mainly producers and consumers surplus. Where producer surplus is a surrogate of producers profit while consumer surplus is a measure of the difference between the maximum amount a consumer is willing to pay and what is actually paid (i.e. the difference between the maximum willingness to pay and the market price for a given level of consumption). Since, a substantial portion of crop production in Mali is kept by the farm families for their home consumption needs, MASM also considers another surplus measure and that is benefits of lower food expenditures of the farm families.

Table 6 provides results on the measures of economic welfare of the agents involved, namely producers, and consumers. The benefits to producers composed of producer surplus and a fall in expenditures for home consumption.

Table 6. Surplus Measures For Producers and Consumers (Billion CFA)

5.4.1 Consumer Surplus

It is interesting to note that even though the new technology was considered for adoption in Kayes, Mopti and Koulikoro, consumer surplus has increased in all the regions of Mali due to lower prices for all crops. The maximum increase of about CFA 10 billion was observed in Segou. The aggregate consumer surplus in Mali from consumption of all the crops considered increased by CFA 57 billion, an increase of 16 percent over the base.

5.4.2 Producer Surplus

Producer surplus decreased in all regions due to the substantial fall in prices without any increase in demand of an equivalent scale. This result is analogous to the classic case of increase in supply caused by technology when the demand is less sensitive to price changes. As result, a considerable fall in prices may increase quantity demanded only marginally, thereby eroding the producers’ surplus. As can be seen from table 5, producer surplus declined in all regions of Mali as prices fell in all regions. In aggregate, producer surplus declined by CFA 71 billion.

5.4.3 Benefits to Farm Families of Lower Food Expenditures

Home consumption expenditure is the amount of food expenditure that farm families make in a given year. A fall in home consumption expenditure is a surrogate economic surplus to farm families as the opportunity cost of their food consumption falls. Due to decline in prices, food expenditures fell in all the regions due to technology introduction. At the country level, farm families in Mali gained CFA 35.58 billion. The net loss to farm families, loss of producer surplus and gain from lower expenditure, comes to a net amount of CFA 35 billion.

5.4.4 Aggregate Welfare

Most of the regions of Mali registered a net gain in aggregate welfare (sum of three surplus measures discussed above). The overall economic value of new technology to Mali is equivalent to the increase in aggregated surplus, which comes out to be CFA 22 billion.

5.4.5 Incorporating Demand Expansion and Adoption Issues

As discussed earlier, full adoption of new technologies caused substantial increase in agricultural production. Without any expansion in demand, this lead to a steep decline in prices (Table 3) and producers had a net loss of CFA 35 billion. In this section we consider the adoption issue by assuming that the new varieties will be fully adopted by year 2015. Due to increase in population, demand for agricultural commodities, as parameterized in ASM, needed to be augmented accordingly. Population projections for made using the historical trends in population growth for rural and urban areas of different regions in Mali. Table 6 shows the population projections;

Table 6. Rural and Urban Population in Mali

Thus using the above population projections, we projected the demand for agricultural commodities both in urban markets and rural areas. MASM was run under a third scenario which super imposed demand projections for 2015 on the new technology scenario. Table 7 provides results for prices, production and area under different crops in Mali,

Table 7. Prices, Area and Production

A comparison of table 3 and 7 shows that prices of most of the crops have increased when we super imposed the demand increase to technology scenario. For example, the maize price went up from 23.62 to 37.66 CFA/ Kg, for groundnuts it increased from 192.76 to 275.83 CFA /Kg. Price of rice stayed the same even after imposing demand expansion to technology scenario as the area under crop increased further from 225.69 to 320.88 thousand hectares leading to a further increase in production of rice from 1200 tons to 2000 tons. The combination of expansion in demand and price increase resulted in increase in producers’ benefits. Table 8 shows the results of changes in welfare of major economic agents. Results of two scenarios are presented. PSSA column refers to technology improvement scenario as we have seen in Table 5, whereas PSSA2 imposes demand expansion to technology improvement. As we can see the producers benefits increased substantially when we considered the demand increase. There is almost 10-fold increase in producer’s benefits, which come from an increase in prices and quantities sold in the market.

Table 8. Surplus Measures For Producers and Consumers (Billion CFA)

Compared to base conditions i.e. with existing technology and demand conditions, producers’ surplus increased from CFA 117.75 to 452.37 when technology and demand expansion were considered simultaneously. Expenditures of farm families increased from CFA 77.35 to CFA 731.42 billion leading to a net welfare gain to farm families (increase in producer surplus — decrease in home consumption expenditure) of CFA 294.07 billion.

6. Summary

In this report we considered the economic and food security implications of adoption of new technologies for maize in Kayes and Koulikoro and for rice in Mopti. For this purpose, we used Mali Agriculture Sector Model which is a multi-commodity and multi-region model that simulates the agriculture sector of Mali. The results of our preliminary analysis show that the new technologies have a great potential of reducing the incidence of malnutrition in Mali. In overall, undernourishment in Mali reduced from 31.6 percent (2.95 million people) in Mali to only 8.81 percent (0.95 million people) with the adoption of new technologies. The economic benefits to producers and consumers of the adoption of the new technologies considered were also estimated using the standard economic measures of welfare, viz. consumer surplus and producer surplus. We estimated a total of CFA 1028.67 billion worth of economic benefits to the society of Mali, an amount that would be in far excess of the cost of technology development. There are also several secondary benefits of these technologies. For example, availability of food at low prices will leave more resources at hand of an average household that can now be used for expenditures elsewhere such as health, education etc. with positive implications on the overall quality of life in Mali. Lower prices would also reduce inflation easing out pressures of economy’s macro-economic adjustments that are notorious for bringing hardships to the poor. Benefits could also be considered in terms of import substitution and increase in export earnings thereby improving the balance of trade and foreign exchange reserve situation for the country.

7. Recommendations for Detailed Assessment of Technologies

As described earlier, a major assumption has been made in this technology assessment. The new technologies considered are assumed to be fully adopted by the farmers. We considered two adoption scenarios, instantaneous full adoption and full adoption by year 2015. In reality, slow technology adoption is the major hindrance for sluggish agricultural growth and low food production in the developing countries. Thus we must consider an assessment of the potential rate of adoption of the technology in greater detail than considered in this study. Also, in demand projections, demand was linearly adjusted for population in 2015. We must also consider changes in composition of demand as reflected in changes in per capita consumption of different food items due to changes in income over time. We also recommend considering the risk properties of the new crop varieties used in this assessment. We must also consider less than ideal conditions at farmer’s fields causing lower attainable yields than what is observed at research farms. The impact of new technologies on state of undernourishment can be further improved by including livestock commodities and refining estimates of inequality in access to food and by providing estimates of other indicators of food insecurity such as Depth of Hunger. The results reported in this preliminary assessment are based on close trade. In reality when prices decline by the order reported in this assessment, there is a strong possibility of increase in exports. This will have three major impacts. First, the producers income will increase through higher export revenues as the prices in the neighboring countries would make exports more profitable compared to the base scenario. Second, due to increase in export the urban consumers will face higher prices compared to post technology scenario of closed trade done in this assessment. This will dampen the estimates of economic welfare of the urban consumers. Thirdly, the estimates of improvement in DES will change in the open trade case, as a part of the increase in food production will leave the domestic market. Hence, we would expect a smaller improvement in the state of undernourishment in Mali.

Appendix: Tables Providing Detailed Results of MASM

Table A1. Production in 000 tons

Table A2. Area Under Different Crops (000 Hectares)

Table A3. Prices in CFA /Kg