"Useful discussion of technological advances in agriculture in the Huaral region in the early 20th century. Special emphasis is placed on the expansion of cotton culture in the region. However, it neglects to discuss the role of Japanese immigrants in expanding cotton agriculture in the period after 1930"--Handbook of Latin American Studies, v. 58.

Florunner peanut was grown after 1 and 2 years of Tifton 9 bahiagrass, corn, cotton, and continuous peanut as whole-plots. Pesticide treatments aldicarb (3.4 kg a.i./ha), flutolanil (1.7 kg a.i./ha), aldicarb + flutolanil, and untreated (control) were sub-plots. Numbers of Meloidogyne arenaria second-stage juveniles in the soil and root-gall indices of peanut at harvest were consistently lower in plots treated with aldicarb and aldicarb + flutolanil than in flutolanil-treated and untreated plots. Percentages of peanut leaflets damaged by thrips and leafhoppers were consistently greater in flutolanil-treated and untreated plots than in plots treated with aldicarb or aldicarb + flutolanil but not affected by cropping sequences. Incidence of southern stem rot was moderate to high for all chemical treatments except those that included flutolanil. Stem rot loci were low in peanut following 2 years of bahiagrass, intermediate following 2 years of corn or cotton, and highest in continuous peanut. Rhizoctonia limb rot was more severe in the peanut monoculture than in peanut following 2 years of bahiagrass, corn, or cotton. Flutolanil alone or combined with aldicarb suppressed limb rot compared with aldicarb-treated and untreated plots. Peanut pod yields were 4,186 kg/ha from aldicarb + flutolanil-treated plots, 3,627 kg/ha from aldicarb-treated plots, 3,426 kg/ha from flutolanil-treated plots, and 3,056 kg/ha from untreated plots. Yields of peanut following 2 years of bahiagrass, corn, and cotton were 29% to 33% higher than yield of monocultured peanut.

A total of 35 herds of cattle from 2 inistitutional farms, and 6 privately-owned farms which were semi-intensively managed, and 17 nomadic Fulani herds were studied between January 1984 and December 1993 to evaluate the age of first calving (AFC) in the three major indigenous breeds of the Savannah zone of Northern Nigeria: the White-fulani (bunaji), Sokoto-Gudali (Bokoloji) and red Fulani (Rahaji). The AFC of heifers at first calving ranged from 30 to 42 months (mean, 37±0.57 months) with no breed differences. The AFC of heifers from Semi-intensively managed herds (institutional and privately-owned farms) was 30 to 40 months ( mean, 36±1.05 months) and that from nomadic herds was 33 to 42 months (mean, 37±1.31 months). The much lower AFC range observed in this studycompared to the highervalues (33 – 60 months) reported by pervious investigators is due to the increased awareness of livestock owners for the need to improve feed supplementation for livestock in particular during the dry season. The nomadic herdsmen ensure a ready supply of cereal bran, cotton seed (meal or cake) crop residues such as groundnut and bean hulls, corn stalks in addition to salt (mineral) licks during the harsh dry season. Institutional and private herds are provided with legumes and grass silages and hay in addition to cereal bran, cotton seed meal and salt licks during the dry season. Nomadic herdowners now realize the importance of supplementary feeding and maintenance of good herd health in the prevention of malnutrition and diseases. This results in a consistent improvement in growth and productivity as evidenced by the lower AFC values observed in this study.

Mango, the most important fruit of India, is grown in 1.23 million ha with an annual production of 10.99 million tones, which accounts for 57.18 percent of the total world production. It is grown in tropical and subtropical regions of the country. Out of nearly 1000 mango cultivars existing in the country, only about 20 cultivars are grown commercially. New cultivars like Amrapali, Mallika, Arka Puneet, Arka Anmol, Ratna, sindhu, Mangeera and a regular bearing and high yielding clone Dashehari-51 have been developed and released for commercial cultivation. Inarching, veneer grafting and stone grafting are the popular methods of propagation. Random seedling rootstocks are used for multiplication of cultivars. Planting at a distance of 10-12 m continues to be common practice, but high density plantings have increased productivity. Though surface irrigation is commonly practiced, use of drip irrigation is increasing in the areas with limited water resources. Intercropping in mango is practiced in the pre-bearing stage. Leaf nutrient guides are being developed. Use of paclobutrazol has solved the problem of alternate bearing in commercial cultivars and is being used at commercial scale. Rejuvenation of old and unproductive trees by pruning of IV order branches has been found successful. Malformation and spongy tissue are the disorders reducing the yield and quality of fruits. Hoppers, mealy bug, inflorescence midge, shoot gall psylla, leaf webber, fruitfly and stone weevil are the key pests. Powdery mildew, anthracnose, dieback, sooty mould, phoma blight and bacterial canker are the widespread preharvest diseases, while anthracnose, stem end rot and black rot are important postharvest diseases. Control methods of these pests and diseases have been worked out. Generally, fruits are hand picked. Low cost harvesters have been developed in different parts of the country. Grading is done based on weight and size of fruits. Baskets and wooden boxes are used for packaging. Recently, CFB boxes have been developed from cotton stalks and sugarcane baggase. Fruits are mostly stored at room temperature for 6 to 14 days. Precooling and low temperature storage studies have given encouraging results. Truck has been adopted as the most convenient mode of transport from orchards to markets. A small quantity of fruits is also lifted by air for export. A large number of products are made from unripe and ripe fruits. However, only mango pulp, pickle, chutney and juice are the products exported from India.

IntroductionSustainability of agricultural production and food supply is threatened in many developing countries by human population growth. The increasing food requirement forces the population to extend the cultivated areas to less fertile areas, often without taking sufficient measures to maintain soil fertility, causing soil degradation and declining yields. Moreover, the ensuing competition for land and other resources increases differentiation between rich and poor. To change this course of development, appropriate measures have to be taken at the farm and at the policy level, requiring insight in the relevant agro-ecological and socio-economic processes and their interactions.However, there is no generally applicable definition of sustainability, hence criteria need to be defined in relation to the area studied. In this study, the criteria pertain to agro-ecological aspects such as soil fertility, crop and animal production, and to socio-economic aspects such as income distribution and cereal prices. As the concept of sustainability has a temporal dimension, a time frame needs to be defined. A period of 25 to 30 years is considered suitable, as the chance of uncertain events increases with time. An ecologically uniform region within a country is considered an appropriate level of analysis of sustainability issues, as this allows to take decision making at both regional and farm level into consideration.To obtain insight in the processes related to agricultural sustainability, it is useful to consider the problem situation as a system and to represent it as a quantitative model. It is thereby important to develop such models interactively with the stakeholders. These models should include agro-ecological as well as behavioural processes at the farm level and allow aggregation of these processes to the regional level.In the past decennia, different types of agro-ecological models have been developed. Most of these models address only a limited number of aspects, such as crop production or organic matter dynamics and are often limited to one growing season. Moreover, these models are usually very detailed and have high data requirements. For the purpose of this study, insight in the interactions between ecological processes, such as soil fertility and crop growth over a longer period is required. As the availability of data is often limited in developing countries and as it is not necessary to make precise predictions in a regional study with a long time frame, summary models are used to simulate ecological processes. These summary models allow integration of many processes over a longer period and require a limited amount of data.Farmer's behaviour can be modelled in several ways using econometric techniques, mathematical programming or decision rules. Econometric techniques are used to predict future behaviour based on historical data. Limitations of these techniques for this study are their limited suitability to deal with new phenomena and their extensive data requirements. Mathematical modelling is suitable for optimisation, but less appropriate to describe actual behaviour. The use of decision rules to represent human behaviour offers more flexibility and is less dependent on the availability of data.There have been several attempts to develop models that provide insight in the ways to achieve sustainable agriculture at the regional level. However, they are not very satisfactory for one or more of the following reasons:lack of integration between socio-economic and agro-ecological aspects;they consider the region or the village as a super farm, disregarding the behaviour of the (different categories of) farmers;they provide only a static picture of a sustainable agricultural system without indicating how this state might be attained, starting from the present situation;The current study has been undertaken to develop a modelling approach that is suitable to:represent the interactions of ecological processes over a period of several years;describe farmers' behaviour and their interactions at the regional level;serve as a tool for decision makers at the farm and the regional level to explore the effects of their decisions on the sustainability at these levels.The empirical setting of this study is the Koutiala region in SouthEast Mali. The major crops grown in this area are millet, sorghum, maize, cotton and groundnut. Cotton has appreciably increased the incomes of the farmers in the area and as a result, both the number of farmers and the cultivated area have increased. However, sustainability of this development is being threatened: the area under continuous cultivation is rapidly increasing, very often without taking sufficient measures to maintain soil fertility and to prevent erosion, leading to soil degradation. Due to lack of alternative investment possibilities, farmers spend their surplus income on the purchase of cattle, causing overgrazing of the common pastures.Two dynamic simulation models have been developed in this study:a farm model allowing exploration of different farm management strategies for different farm types;a regional model, allowing exploration of the effects of different policies on the sustainability of agricultural development.The farm modelThe farm model consists of one core model and four data sets, each representing a particular farm type. Four farm types (A, B, C and D) are distinguished, mainly based on herd size, area cultivated and level of equipment. By changing a number of parameters in the data sets, the effect of different management strategies on soil fertility, crop and livestock production, farm income and food availability can be simulated. As the farm is subdivided in a number of fields of 1 ha, the model permits also to examine the effects of various crop rotations.Soil fertility indicators, used in the model are organic matter, nitrogen, organic and inorganic phosphorus, pH and soil depth. The model simulates changes in these indicators caused by e.g. the application of fertiliser and manure, decomposition of organic matter, removal by crops, erosion, leaching etc., using time steps of one year. Soil moisture content is simulated on a monthly basis. Crop production is determined by uptake of nitrogen and phosphorus, water availability, effect of pests and diseases and effect of labour input.Animal production (growth rates, calving rates, death rates and milk production) is determined by the amount and quality of the available feed on a monthly basis. The feed consists of grass and browse from the common pastures, crop residues and some concentrate. The results show decreasing soil organic matter contents on all farms types. Phosphorus contents, however, are increasing except on D farms, as these farms do not apply fertiliser. Soil pH decreases due to the use of ammoniacal fertiliser.Millet yields decrease over time due to the decrease in soil organic matter, the most important source of nitrogen for this crop. Maize appears to be susceptible to drought, partly explaining the reluctance of the farmers to grow this crop. Nevertheless, cereal supply can be maintained above the minimum requirement for all farm types. Results of model experiments suggest that stable feeding of millet straw positively influences animal production, which is further enhanced by the introduction of dolichos as an intercrop in maize. Introduction of dolichos, however, reduces maize yield and hence incomes.Soil conservation measures such as ridging and tied ridging, increase maize yields in dry years by reducing run-off losses of water and fertiliser, and by increasing water infiltration. The increased labour requirement for the construction of tied ridges, however, renders this practice less attractive for the farmer than simple ridges.Determination of the number of animals per farm that maximizes income, results in very large herd sizes per farm but also in large areas of pasture land required to feed these herds. This explains the interest of farmers to increase their herds, but also shows the consequences of this practice for the environment. Model experiments suggest that taxation of the use of pasture land at a rate of FCFA 3000 per ha would reduce the interest of the farmers to continuously increase their herds.The regional modelThe regional model is based on the farm model. Soil processes, labour requirements, farm income and crop and animal production are modelled in the same way. The way crops are rotated over the different fields, however, has not been included in the regional model. In the regional model, the farmers are regarded as actors. This implies that their behaviour has become endogenous such as crop choice, purchase of fertiliser and purchase and sale of cattle.The model simulates agricultural development over the period 1980 - 2025. The development of the number of farms per farm type plays a central role in the regional model. The number of farms per farm type may change for several reasons:farmers may retire and be succeeded by their sons or not, depending on expected incomes as compared to off-farm incomes. It is also possible that more than one son wants to become farmer; others may migrate to town.new farmers may immigrate from other areas;farmers may change type if the changes in their herd size are such that the new herd size does not match the criteria of the current type. At the start of the simulation, the number of farms per farm type is known as well as their household sizes, the areas of sandy and loamy soil occupied, the number of animals per farm and the soil fertility per farm and soil type.Farmers determine the area per crop to be cultivated on the basis of their food requirement, expected yields, net revenues per crop, taste preferences, credit availability, input supply, etc. The application of animal manure depends on availability and on the crop. The use of fertiliser is determined by the crop and the fertiliser-crop price ratio. Cereal prices are endogenously determined on the basis of the surplus production and the demand of the non-farm population.Depending on his income, the farmer may use part of it to invest in cattle. If the average herd size of a particular farm type increases, part of the farms belonging to that type, move to a 'higher' type. On the other hand, if average herd size decreases, part of the farms move to a 'lower' type. When a farmer moves to another farm type, he takes his household, land and herd with him. These changes, along with the effects of population growth and migration, result in changes in the number of farms per farm type, average household size, herd size, area and soil fertility per farm type. Land, required for the new farms and for the expanding farms is withdrawn from the common pasture area.Model results show a continuous increase in cultivated area until all land is occupied, and decreasing levels of organic matter and, hence, of millet yields. Decreasing millet yields and increasing urban demand lead to higher cereal prices and, hence, to a larger share of cereals cultivated. Due to favourable incomes, farmers invest in cattle, resulting in an increase in the number of large (A) farms. However, as the common pasture area is shrinking, animal feed supply falls short of the requirement, increasing animal death rates and reducing the herd size and, hence, A farms become B farms.In the basic model, the behaviour of the different farm types is described by different sets of decision rules. Running the model over a number of years, however, results in changes that create a new situation, such as a structural shortage of feed and decreasing millet yields. It is likely that farmers will adapt their behaviour to the new circumstances, e.g. they may change their selling and investment strategy and improve feed supply by growing a fodder crop or start to apply fertiliser on their millet crop when yields drop below a certain level. Therefore, the model has been adapted by including such behaviour.Finally, a number of policy experiments has been carried out: changes in prices of fertiliser and cotton, introducing a tax on the use of pasture land and increased off-farm wages. Increasing cotton price and reducing the fertiliser price both by 20 %, increase the area of cotton, the use of fertiliser and income, stimulating farmers to increase herd size. The larger number of animals in the area results in lower animal growth rates, reducing herd sizes per farm and causing the number of A farms to decrease and the number of B farms to increase.Increasing off-farm wages reduces the number of farms, as young people leave the farms to find a remunerative job outside agriculture. As higher wages positively affect off-farm incomes of the farm households, enabling farmers to increase their herds, the number of A farms increases. Hence, the decrease in the total number of farms is compensated by the increasing share of A farms, maintaining cereal production and prices at approximately the same level. Experiments to explore the effects of taxation of the use of common pasture land at the regional level suggest that a taxation of the use of pasture land of FCFA 5000 per ha reduces total herd size and total number of farms, especially A farms, and improves feed availability.Evaluation of the approachThe regional model presented in this study integrates biophysical and socio-economic aspects: farm management decisions affect the resource base of the farmer and the changes in the resource base affect farm management. In addition to that, the model integrates the farm and the regional level: the behaviour of the farmer influences cereal prices and land availability, which influence the behaviour of the farmer in the subsequent period. The descriptive approach to the modelling of decision making provides flexibility in the simulation of farmer's behaviour and offers possibilities for sociological research, a discipline that ought to play a more important role in land use studies.Questions may be raised however regarding the predictive power of such models. Is it possible to make reliable predictions on agricultural development of a large region, comprising many farms of different types? This is further complicated by a lack of tested theories, relevant to the situation, and by a paucity of reliable longitudinal data that are required to construct and validate the model. Moreover, unpredictable events, such as droughts, devaluation, changes in world market prices of cotton and political changes constitute sources of uncertainty. The model should therefore be considered as a hypothesis, that may be applied for decision support, rather than an instrument that enables the decision maker to predict the future with some certainty.As a model cannot capture the real world and as the real world continuously changes, the model should be repeatedly subjected to a process of testing. In this process, model predictions are compared with real world data, followed by an adjustment of the model. This implies that the model should not be considered as a fixed but rather as an evolving representation of the real world. The validity of such models may be further enhanced if developed in continuous interaction with stakeholders, including farmers, researchers and policy makers. Hence, this approach emphasises the importance of processes in two ways: the processes that are part of the model and the processes related to the way the model is developed.Such models may be helpful in improving understanding of the dynamics of the system, allowing decision makers at farm and regional level to improve the quality of their decisions. The model may also help to discover discontinuities in behaviour when conditions change as shown above. Similarly, the model may be useful in discovering undesirable trends and permits exploration of effects of various policies or identification of topics for agricultural research that may contribute to avoid or remedy problems in the future.This approach might also be used in combination with approaches using mathematical programming, where the latter may be used to generate technically feasible options for sustainable land use, while the approach used in this study could serve to determine how to stimulate adoption of such land use systems, starting from the present situation.

<H3>Introduction</H3>Sustainability of agricultural production and food supply is threatened in many developing countries by human population growth. The increasing food requirement forces the population to extend the cultivated areas to less fertile areas, often without taking sufficient measures to maintain soil fertility, causing soil degradation and declining yields. Moreover, the ensuing competition for land and other resources increases differentiation between rich and poor. To change this course of development, appropriate measures have to be taken at the farm and at the policy level, requiring insight in the relevant agro-ecological and socio-economic processes and their interactions.However, there is no generally applicable definition of sustainability, hence criteria need to be defined in relation to the area studied. In this study, the criteria pertain to agro-ecological aspects such as soil fertility, crop and animal production, and to socio-economic aspects such as income distribution and cereal prices. As the concept of sustainability has a temporal dimension, a time frame needs to be defined. A period of 25 to 30 years is considered suitable, as the chance of uncertain events increases with time. An ecologically uniform region within a country is considered an appropriate level of analysis of sustainability issues, as this allows to take decision making at both regional and farm level into consideration.To obtain insight in the processes related to agricultural sustainability, it is useful to consider the problem situation as a system and to represent it as a quantitative model. It is thereby important to develop such models interactively with the stakeholders. These models should include agro-ecological as well as behavioural processes at the farm level and allow aggregation of these processes to the regional level.In the past decennia, different types of agro-ecological models have been developed. Most of these models address only a limited number of aspects, such as crop production or organic matter dynamics and are often limited to one growing season. Moreover, these models are usually very detailed and have high data requirements. For the purpose of this study, insight in the interactions between ecological processes, such as soil fertility and crop growth over a longer period is required. As the availability of data is often limited in developing countries and as it is not necessary to make precise predictions in a regional study with a long time frame, summary models are used to simulate ecological processes. These summary models allow integration of many processes over a longer period and require a limited amount of data.Farmer's behaviour can be modelled in several ways using econometric techniques, mathematical programming or decision rules. Econometric techniques are used to predict future behaviour based on historical data. Limitations of these techniques for this study are their limited suitability to deal with new phenomena and their extensive data requirements. Mathematical modelling is suitable for optimisation, but less appropriate to describe actual behaviour. The use of decision rules to represent human behaviour offers more flexibility and is less dependent on the availability of data.There have been several attempts to develop models that provide insight in the ways to achieve sustainable agriculture at the regional level. However, they are not very satisfactory for one or more of the following reasons:<UL><LI>lack of integration between socio-economic and agro-ecological aspects;<LI>they consider the region or the village as a super farm, disregarding the behaviour of the (different categories of) farmers;<LI>they provide only a static picture of a sustainable agricultural system without indicating how this state might be attained, starting from the present situation;</UL>The current study has been undertaken to develop a modelling approach that is suitable to:<UL><LI>represent the interactions of ecological processes over a period of several years;<LI>describe farmers' behaviour and their interactions at the regional level;<LI>serve as a tool for decision makers at the farm and the regional level to explore the effects of their decisions on the sustainability at these levels.</UL>The empirical setting of this study is the Koutiala region in SouthEast Mali. The major crops grown in this area are millet, sorghum, maize, cotton and groundnut. Cotton has appreciably increased the incomes of the farmers in the area and as a result, both the number of farmers and the cultivated area have increased. However, sustainability of this development is being threatened: the area under continuous cultivation is rapidly increasing, very often without taking sufficient measures to maintain soil fertility and to prevent erosion, leading to soil degradation. Due to lack of alternative investment possibilities, farmers spend their surplus income on the purchase of cattle, causing overgrazing of the common pastures.Two dynamic simulation models have been developed in this study:<OL><LI>a farm model allowing exploration of different farm management strategies for different farm types;<LI>a regional model, allowing exploration of the effects of different policies on the sustainability of agricultural development.</OL><H3>The farm model</H3>The farm model consists of one core model and four data sets, each representing a particular farm type. Four farm types (A, B, C and D) are distinguished, mainly based on herd size, area cultivated and level of equipment. By changing a number of parameters in the data sets, the effect of different management strategies on soil fertility, crop and livestock production, farm income and food availability can be simulated. As the farm is subdivided in a number of fields of 1 ha, the model permits also to examine the effects of various crop rotations.Soil fertility indicators, used in the model are organic matter, nitrogen, organic and inorganic phosphorus, pH and soil depth. The model simulates changes in these indicators caused by e.g. the application of fertiliser and manure, decomposition of organic matter, removal by crops, erosion, leaching etc., using time steps of one year. Soil moisture content is simulated on a monthly basis. Crop production is determined by uptake of nitrogen and phosphorus, water availability, effect of pests and diseases and effect of labour input.Animal production (growth rates, calving rates, death rates and milk production) is determined by the amount and quality of the available feed on a monthly basis. The feed consists of grass and browse from the common pastures, crop residues and some concentrate. The results show decreasing soil organic matter contents on all farms types. Phosphorus contents, however, are increasing except on D farms, as these farms do not apply fertiliser. Soil pH decreases due to the use of ammoniacal fertiliser.Millet yields decrease over time due to the decrease in soil organic matter, the most important source of nitrogen for this crop. Maize appears to be susceptible to drought, partly explaining the reluctance of the farmers to grow this crop. Nevertheless, cereal supply can be maintained above the minimum requirement for all farm types. Results of model experiments suggest that stable feeding of millet straw positively influences animal production, which is further enhanced by the introduction of dolichos as an intercrop in maize. Introduction of dolichos, however, reduces maize yield and hence incomes.Soil conservation measures such as ridging and tied ridging, increase maize yields in dry years by reducing run-off losses of water and fertiliser, and by increasing water infiltration. The increased labour requirement for the construction of tied ridges, however, renders this practice less attractive for the farmer than simple ridges.Determination of the number of animals per farm that maximizes income, results in very large herd sizes per farm but also in large areas of pasture land required to feed these herds. This explains the interest of farmers to increase their herds, but also shows the consequences of this practice for the environment. Model experiments suggest that taxation of the use of pasture land at a rate of FCFA 3000 per ha would reduce the interest of the farmers to continuously increase their herds.<H3>The regional model</H3>The regional model is based on the farm model. Soil processes, labour requirements, farm income and crop and animal production are modelled in the same way. The way crops are rotated over the different fields, however, has not been included in the regional model. In the regional model, the farmers are regarded as actors. This implies that their behaviour has become endogenous such as crop choice, purchase of fertiliser and purchase and sale of cattle.The model simulates agricultural development over the period 1980 - 2025. The development of the number of farms per farm type plays a central role in the regional model. The number of farms per farm type may change for several reasons:<UL><LI>farmers may retire and be succeeded by their sons or not, depending on expected incomes as compared to off-farm incomes. It is also possible that more than one son wants to become farmer; others may migrate to town.<LI>new farmers may immigrate from other areas;</UL>farmers may change type if the changes in their herd size are such that the new herd size does not match the criteria of the current type. At the start of the simulation, the number of farms per farm type is known as well as their household sizes, the areas of sandy and loamy soil occupied, the number of animals per farm and the soil fertility per farm and soil type.Farmers determine the area per crop to be cultivated on the basis of their food requirement, expected yields, net revenues per crop, taste preferences, credit availability, input supply, etc. The application of animal manure depends on availability and on the crop. The use of fertiliser is determined by the crop and the fertiliser-crop price ratio. Cereal prices are endogenously determined on the basis of the surplus production and the demand of the non-farm population.Depending on his income, the farmer may use part of it to invest in cattle. If the average herd size of a particular farm type increases, part of the farms belonging to that type, move to a 'higher' type. On the other hand, if average herd size decreases, part of the farms move to a 'lower' type. When a farmer moves to another farm type, he takes his household, land and herd with him. These changes, along with the effects of population growth and migration, result in changes in the number of farms per farm type, average household size, herd size, area and soil fertility per farm type. Land, required for the new farms and for the expanding farms is withdrawn from the common pasture area.Model results show a continuous increase in cultivated area until all land is occupied, and decreasing levels of organic matter and, hence, of millet yields. Decreasing millet yields and increasing urban demand lead to higher cereal prices and, hence, to a larger share of cereals cultivated. Due to favourable incomes, farmers invest in cattle, resulting in an increase in the number of large (A) farms. However, as the common pasture area is shrinking, animal feed supply falls short of the requirement, increasing animal death rates and reducing the herd size and, hence, A farms become B farms.In the basic model, the behaviour of the different farm types is described by different sets of decision rules. Running the model over a number of years, however, results in changes that create a new situation, such as a structural shortage of feed and decreasing millet yields. It is likely that farmers will adapt their behaviour to the new circumstances, e.g. they may change their selling and investment strategy and improve feed supply by growing a fodder crop or start to apply fertiliser on their millet crop when yields drop below a certain level. Therefore, the model has been adapted by including such behaviour.Finally, a number of policy experiments has been carried out: changes in prices of fertiliser and cotton, introducing a tax on the use of pasture land and increased off-farm wages. Increasing cotton price and reducing the fertiliser price both by 20 %, increase the area of cotton, the use of fertiliser and income, stimulating farmers to increase herd size. The larger number of animals in the area results in lower animal growth rates, reducing herd sizes per farm and causing the number of A farms to decrease and the number of B farms to increase.Increasing off-farm wages reduces the number of farms, as young people leave the farms to find a remunerative job outside agriculture. As higher wages positively affect off-farm incomes of the farm households, enabling farmers to increase their herds, the number of A farms increases. Hence, the decrease in the total number of farms is compensated by the increasing share of A farms, maintaining cereal production and prices at approximately the same level. Experiments to explore the effects of taxation of the use of common pasture land at the regional level suggest that a taxation of the use of pasture land of FCFA 5000 per ha reduces total herd size and total number of farms, especially A farms, and improves feed availability.<H3>Evaluation of the approach</H3>The regional model presented in this study integrates biophysical and socio-economic aspects: farm management decisions affect the resource base of the farmer and the changes in the resource base affect farm management. In addition to that, the model integrates the farm and the regional level: the behaviour of the farmer influences cereal prices and land availability, which influence the behaviour of the farmer in the subsequent period. The descriptive approach to the modelling of decision making provides flexibility in the simulation of farmer's behaviour and offers possibilities for sociological research, a discipline that ought to play a more important role in land use studies.Questions may be raised however regarding the predictive power of such models. Is it possible to make reliable predictions on agricultural development of a large region, comprising many farms of different types? This is further complicated by a lack of tested theories, relevant to the situation, and by a paucity of reliable longitudinal data that are required to construct and validate the model. Moreover, unpredictable events, such as droughts, devaluation, changes in world market prices of cotton and political changes constitute sources of uncertainty. The model should therefore be considered as a hypothesis, that may be applied for decision support, rather than an instrument that enables the decision maker to predict the future with some certainty.As a model cannot capture the real world and as the real world continuously changes, the model should be repeatedly subjected to a process of testing. In this process, model predictions are compared with real world data, followed by an adjustment of the model. This implies that the model should not be considered as a fixed but rather as an evolving representation of the real world. The validity of such models may be further enhanced if developed in continuous interaction with stakeholders, including farmers, researchers and policy makers. Hence, this approach emphasises the importance of processes in two ways: the processes that are part of the model and the processes related to the way the model is developed.Such models may be helpful in improving understanding of the dynamics of the system, allowing decision makers at farm and regional level to improve the quality of their decisions. The model may also help to discover discontinuities in behaviour when conditions change as shown above. Similarly, the model may be useful in discovering undesirable trends and permits exploration of effects of various policies or identification of topics for agricultural research that may contribute to avoid or remedy problems in the future.This approach might also be used in combination with approaches using mathematical programming, where the latter may be used to generate technically feasible options for sustainable land use, while the approach used in this study could serve to determine how to stimulate adoption of such land use systems, starting from the present situation.

This thesis investigates options for sustainable rice cultivation and general agricultural development in the Mwanza and Shinyanga regions in northwestern Tanzania, often called Sukumaland due to the predominance of Wasukuma people. Generally Sukumaland has a semi-arid climate; agriculture is constrained by unreliable and low rainfall. In the past fifty years the population density has doubled in most parts. This has triggered several changes in farming systems. One important change is a reduction of grasslands in the valleys, due to increased cultivation of rice. Rice cultivation systems in Sukumaland serve here as a case study that allows the investigation of the interplay of social, economic and biophysical sustainability factors at field, farm, watershed and regional/national levels and their importance to the development of sustainable agriculture. Because soil fertility management is currently more important to sustainable rice cultivation in Sukumaland than water use efficiency or pest and disease susceptibility, it is the focus of the investigation.Economic reform programmes known as structural adjustment programmes started in Tanzania around 1986, guided by the International Monetary Fund (IMF) and the World Bank. These programmes required drastic changes in Tanzanian national economic policies and had great impact on the marketing of agricultural outputs and inputs. Liberalized markets and private traders were expected to improve the agricultural sector via a much needed intensification of agriculture, involving higher consumption of mineral fertilizers, herbicides and pesticides. However, the `liberalization' of international agricultural trade provided only a limited increase in access to developed country (DC) markets for less developed countries (LDCs) like Tanzania, and included few restrictions on the dumping of agricultural products by DCs. Farmers in LDCs cannot compete with farmers in DCs, and this lack of market opportunities, in combination with low agricultural prices and the low purchasing power of LDC consumers, pose major constraints on LDC food security. Specific data for Tanzania show that for this country the per capita food production increased in the 1970s, stabilized in the 1980s and started to decline in the 1990s. From a national point of view this is obviously not sustainable agricultural development.The gradual process of soil nutrient depletion in many parts of sub-Saharan Africa (SSA) is thought to be a reason, though rather hidden, for slow agricultural growth in the face of high increases in population. An integrated nutrient management (INM) strategy, which combines the use of locally available resources with the use of external inputs and includes both management practices to save nutrients from being lost from the system and interventions to add nutrients from outside, has been advocated to increase production and develop sustainable agriculture in SSA. INM methods are one of the important strategies of low external input and sustainable agriculture (LEISA) policies. The LEISA approach is aimed at making optimal use of local available resources, adding limited external inputs and using them in the most efficient way. In managing soil fertility, low inputs of mineral fertilizers must be combined with applications of farmyard manure and, where applicable, green manures, compost, agroforestry and erosion control. The expected result is sustainable increases in production.The objective of this thesis is to evaluate whether it is feasible for farm households in the Sukumaland rice cultivation systems to adopt INM and LEISA, given the current economic climate fostered by structural adjustment programmes. More generally, whether farm households in SSA countries can adopt INM/LEISA as a way of generating sustainable agricultural development in the context of liberalization of international agricultural trade, structural adjustment programmes, and fast population growth, is examined. It must be noted that in many locations within Sukumaland, cassava, maize or sorghum are more important food crops than rice; cotton, maize or horticultural crops can be more important cash crops than rice. Just as elsewhere, sustainable agricultural development in Sukumaland depends on the performance of all cropping systems present, and on the interactions of these cropping systems with livestock and other subsystems at the farm level.When performance in the food and cash crop sectors of Tanzania and the availability and consumption of agricultural inputs in 1986-1996 are compared with periods prior to IMF/World Bank backed reform, the positive developments of the first five years of reform appear not to be sustainable. At present, rural productivity levels per capita for important food and cash crops are declining. High increases in fertilizer prices and input availability problems in the villages are related to liberalization of agricultural input supply and pricing. The removal of subsidies on agricultural inputs from 1991 onwards is crucial in explaining the decline in production of maize, the main food crop in Tanzania. Structural adjustment programmes usually include far reaching reductions in the role of government. However, adequate government involvement may be necessary to ensure greater use of agricultural inputs and thus improved performance of the agricultural sector in Tanzania.In Sukumaland, historical material makes it possible to put current conditions in an historical context. A description of farming systems dynamics in Sukumaland over the past 100 years shows that people were attracted to the area by its low incidence of human and animal diseases. Cultivation was first restricted to the sandy upper parts of slopes because they are easy to prepare by hand hoe. Increases in population density led to enlargements in farm size directed towards the lower parts of slopes. Rice cultivation in these areas reduced grazing space for livestock. Households with large herds migrated to new, tsetse free areas; large farms were made possible by the availability of ploughs and oxen. In the 1950s and 1960s there were strong increases in household income in Sukumaland due to extensive, financially rewarding cotton growing. In the 1970s and 1980s this became much less profitable, leading to diversification in cash crops in accord with agroecological variations and distance to Mwanza town. Ongoing increases in population density caused a decrease in arable land and livestock units per capita, plus shifts in crops grown. Less demanding (cassava) and higher yield (rice, maize, cassava, sweet potatoes) crops were substituted for the traditional crops (sorghum, bulrush millet). These developments varied across the Mwanza and Shinyanga regions, due to differences in population density and agroecological conditions. At present there are signs of agricultural intensification near Mwanza town, while extensive farming dominates in the remaining parts of Sukumaland.Recent agricultural surveys conducted in Sukumaland have drawn attention to the importance of rainfed, lowland rice in the farming systems studied. More than a third of rice produced in Tanzania comes from Sukumaland. Farmers increased their rice production quickly when rice cultivation became more profitable in comparison to cotton and other crops, as well as more popular as a food crop because it can produce high amounts of calories on small pieces of land. The strong increase in rice cultivation during the last 25 years is remarkable, given the low and highly unreliable rainfall in Sukumaland. Farmers have developed highly productive rainfed, lowland rice systems solely on the basis of their knowledge of soils, rainfall patterns and topography, and on their experiments with water management systems, cultivars, and planting and land preparation methods. Rice management practices closely follow differences in ecology and household characteristics. Selection of rice cultivars is largely determined by water conditions in the field. The cultivation of rice is more intensive in Mwanza region, where transplanting takes place; on the larger rice fields in Shinyanga region, broadcasting dominates. Households grow rice for both food and cash - mainly for food in Mwanza region and mainly for cash in Shinyanga region. Water and weeds are the major production constraints, but low soil fertility is also a problem on the sandier fields of Mwanza region. Yields have declined due to continuous cultivation, with almost no application of organic or mineral fertilizers. On the more clayey rice fields in Shinyanga region yields are, however, still satisfactory at present due to the relatively short period (10-20 years) of cultivation. However, in one out of three years farmers fail to get food and cash from rice due to insufficient, too late or too unreliable rainfall.In response to farmers' complaints about declining rice yields, on-farm soil fertility research was carried out in the rice fields of Sukumaland between 1990 and 1996, using a Farming Systems Research/Extension (FSRE) methodology. The decline was thought to be related to a decrease in soil fertility. On-farm research showed that broadcasting 30 kg N ha <sup>-1</SUP>in the form of urea in wet rice fields at the tillering stage increased rice grain yields by 500-900 kg ha <sup>-1</SUP>in almost every type of rice field cultivated in Sukumaland. Doses higher than 30 kg N ha <sup>-1</SUP>were less economical at 1996 prices for crops and fertilizers. The crop yield response to urea was better when rice plants were at the maximum tillering stage, when water depths were less than 15 cm at application, and when the sand content of fields was higher. The relatively small differences each year in response per field did not justify multiple extension messages. A single dose of 30 kg N ha <sup>-1</SUP>in the form of urea to rice at tillering was thus recommended for Sukumaland as a whole.Despite the relatively high average productivity index for a low dose of urea in rice, there was almost no adoption by farmers. The main factors were problems in the availability of urea in villages and decreasing profitability of the rice-urea technology, due to IMF/World Bank instigated reform measures. Non-adoption was also due to absence of real need to use urea on the more clayey rice fields; poor involvement of the extension service; confusing research messages related to rice soil fertility management; the high degree of uncertainty in Sukumaland farming systems; and low participation of farmers during priority setting for on-farm activities. Effective adoption of agricultural technologies generated by an FSRE methodology calls for strong, institutionalized links with the extension service, commodity research and policy makers. Better coordination of activities between donors and governments is an essential precondition to make such links work.Failures in the adoption of use of urea in rice encouraged researchers and farmers in Sukumaland to look for alternative ways to improve soil fertility in rice fields. Research has been done on the use of locally available resources such as kraal manure and rice husks, and the introduction of green manure and multipurpose trees as an alternative to urea. The performance of green manures and multipurpose trees was meagre due to limited potential for biomass production in the semi-arid climate. Half of the households in Sukumaland have no easy access to cattle manure, and in any case the quality of the available manure is low, due to open air collection and very low addition of crop residues. The relatively large amount of labour involved in transporting and incorporating bulky organic materials like kraal manure, green manure, rice husks and tree leaves in the relatively far and less easily accessible rice fields is also a serious problem.The increase in labour required per hectare is difficult to realize in the thinly populated Shinyanga region, and furthermore is not seen as desirable by households anywhere in Sukumaland, due to the expected decrease in labour productivity. Apart from that, farmers with clayey rice fields see no need to invest so much in soil fertility management. A nutrient balance calculation for the rainfed lowland rice cultivation systems in Sukumaland gave no serious depletion rates for major nutrients, which seems to support the farmers' attitude. Despite massive campaigns promoting the use of organic fertilizers in Sukumaland during the colonial period and recent attempts by the Tanzanian government, the adoption rate is still very low in almost all cropping systems. Only near Mwanza town are farmers applying kraal manure to horticultural crops and, to a lesser extent, maize/cassava fields. The quantities applied are, however, not sufficient to achieve positive nutrient balances on these sandy upland soils.A review of the literature suggests that INM/LEISA successes in SSA are characterized by relatively high intensity of land use, high population density, medium to high livestock density, good market access and presence of large urban markets (in particular for horticulture products) in the vicinity. Further, the active support of SSA governments for their agricultural sectors will be needed if sustainable agriculture is to be attained through INM/LEISA approaches, as well as active intervention to protect their agricultural sectors against competition from industrial countries. INM/LEISA approaches are not appropriate for SSA farming systems that lack these characteristics. Several examples from SSA and Asia show that severe dependence on labour-intensive methods may lead to decreases in labour productivity. LEISA advocates seem to be largely unaware that farming methods based primarily on labour-intensive techniques can lead to the impoverishment of farm households. For resource-poor farmers, sustainable agriculture must first of all be socio-economically viable. Insufficient use of external inputs can turn LEISA into a non-sustainable form of agriculture; therefore LEISA advocates should take a critical look at the impact of structural adjustment programmes in SSA.The main conclusion of this thesis is that INM/LEISA approaches are currently not an appropriate way to generate sustainable soil fertility management in the rice cultivation systems of Sukumaland. Farmers with rice fields located on fertile clayey soils are still satisfied with their grain yields, and are not yet motivated to invest labour and cash in soil fertility maintenance. However, especially in rice fields located on sandier soils, in the future farmers will have to invest considerably in soil fertility maintenance to achieve sustainable rice cultivation. The current situation in Sukumaland makes such investments highly unlikely. The huge increase in rice cultivation in Sukumaland is on the other hand a good example of farmer adaptation to increasing population densities, changes in market opportunities, and soil fertility advantages in the valleys. Negative nutrient balances furthermore do not always justify recommendations to farmers that involve the immediate use of mineral fertilizers and/or organic fertilizers. More farmer participation, especially in priority setting, is necessary to prevent misunderstandings regarding farmers' objectives.Any strategy for future sustainable rice cultivation and agriculture in Sukumaland, including INM or LEISA, must be based on a thorough analysis of biophysical, socio-economic and public policy factors and their linkages. Such a strategy requires a conducive economic and policy environment. In Sukumaland this will require improvements in infrastructure, increased government support to agriculture, reduced taxation in the cotton crop sector, reduced reliance on rice imports, higher population densities, intensive livestock keeping and a greater variety of off-farm employment. A good INM strategy in Sukumaland would then be to use urea in rice, and farmyard manure in the nearby cassava/maize and cotton fields. Without a conducive economic and policy environment, population growth in Sukumaland will lead to an intensification largely based on labour inputs. Instead of agricultural evolution, agricultural involution will be the result.This thesis can be orderd: KIT-publisher R.Gunm@kit.nl

This thesis investigates options for sustainable rice cultivation and general agricultural development in the Mwanza and Shinyanga regions in northwestern Tanzania, often called Sukumaland due to the predominance of Wasukuma people. Generally Sukumaland has a semi-arid climate; agriculture is constrained by unreliable and low rainfall. In the past fifty years the population density has doubled in most parts. This has triggered several changes in farming systems. One important change is a reduction of grasslands in the valleys, due to increased cultivation of rice. Rice cultivation systems in Sukumaland serve here as a case study that allows the investigation of the interplay of social, economic and biophysical sustainability factors at field, farm, watershed and regional/national levels and their importance to the development of sustainable agriculture. Because soil fertility management is currently more important to sustainable rice cultivation in Sukumaland than water use efficiency or pest and disease susceptibility, it is the focus of the investigation.Economic reform programmes known as structural adjustment programmes started in Tanzania around 1986, guided by the International Monetary Fund (IMF) and the World Bank. These programmes required drastic changes in Tanzanian national economic policies and had great impact on the marketing of agricultural outputs and inputs. Liberalized markets and private traders were expected to improve the agricultural sector via a much needed intensification of agriculture, involving higher consumption of mineral fertilizers, herbicides and pesticides. However, the `liberalization' of international agricultural trade provided only a limited increase in access to developed country (DC) markets for less developed countries (LDCs) like Tanzania, and included few restrictions on the dumping of agricultural products by DCs. Farmers in LDCs cannot compete with farmers in DCs, and this lack of market opportunities, in combination with low agricultural prices and the low purchasing power of LDC consumers, pose major constraints on LDC food security. Specific data for Tanzania show that for this country the per capita food production increased in the 1970s, stabilized in the 1980s and started to decline in the 1990s. From a national point of view this is obviously not sustainable agricultural development.The gradual process of soil nutrient depletion in many parts of sub-Saharan Africa (SSA) is thought to be a reason, though rather hidden, for slow agricultural growth in the face of high increases in population. An integrated nutrient management (INM) strategy, which combines the use of locally available resources with the use of external inputs and includes both management practices to save nutrients from being lost from the system and interventions to add nutrients from outside, has been advocated to increase production and develop sustainable agriculture in SSA. INM methods are one of the important strategies of low external input and sustainable agriculture (LEISA) policies. The LEISA approach is aimed at making optimal use of local available resources, adding limited external inputs and using them in the most efficient way. In managing soil fertility, low inputs of mineral fertilizers must be combined with applications of farmyard manure and, where applicable, green manures, compost, agroforestry and erosion control. The expected result is sustainable increases in production.The objective of this thesis is to evaluate whether it is feasible for farm households in the Sukumaland rice cultivation systems to adopt INM and LEISA, given the current economic climate fostered by structural adjustment programmes. More generally, whether farm households in SSA countries can adopt INM/LEISA as a way of generating sustainable agricultural development in the context of liberalization of international agricultural trade, structural adjustment programmes, and fast population growth, is examined. It must be noted that in many locations within Sukumaland, cassava, maize or sorghum are more important food crops than rice; cotton, maize or horticultural crops can be more important cash crops than rice. Just as elsewhere, sustainable agricultural development in Sukumaland depends on the performance of all cropping systems present, and on the interactions of these cropping systems with livestock and other subsystems at the farm level.When performance in the food and cash crop sectors of Tanzania and the availability and consumption of agricultural inputs in 1986-1996 are compared with periods prior to IMF/World Bank backed reform, the positive developments of the first five years of reform appear not to be sustainable. At present, rural productivity levels per capita for important food and cash crops are declining. High increases in fertilizer prices and input availability problems in the villages are related to liberalization of agricultural input supply and pricing. The removal of subsidies on agricultural inputs from 1991 onwards is crucial in explaining the decline in production of maize, the main food crop in Tanzania. Structural adjustment programmes usually include far reaching reductions in the role of government. However, adequate government involvement may be necessary to ensure greater use of agricultural inputs and thus improved performance of the agricultural sector in Tanzania.In Sukumaland, historical material makes it possible to put current conditions in an historical context. A description of farming systems dynamics in Sukumaland over the past 100 years shows that people were attracted to the area by its low incidence of human and animal diseases. Cultivation was first restricted to the sandy upper parts of slopes because they are easy to prepare by hand hoe. Increases in population density led to enlargements in farm size directed towards the lower parts of slopes. Rice cultivation in these areas reduced grazing space for livestock. Households with large herds migrated to new, tsetse free areas; large farms were made possible by the availability of ploughs and oxen. In the 1950s and 1960s there were strong increases in household income in Sukumaland due to extensive, financially rewarding cotton growing. In the 1970s and 1980s this became much less profitable, leading to diversification in cash crops in accord with agroecological variations and distance to Mwanza town. Ongoing increases in population density caused a decrease in arable land and livestock units per capita, plus shifts in crops grown. Less demanding (cassava) and higher yield (rice, maize, cassava, sweet potatoes) crops were substituted for the traditional crops (sorghum, bulrush millet). These developments varied across the Mwanza and Shinyanga regions, due to differences in population density and agroecological conditions. At present there are signs of agricultural intensification near Mwanza town, while extensive farming dominates in the remaining parts of Sukumaland.Recent agricultural surveys conducted in Sukumaland have drawn attention to the importance of rainfed, lowland rice in the farming systems studied. More than a third of rice produced in Tanzania comes from Sukumaland. Farmers increased their rice production quickly when rice cultivation became more profitable in comparison to cotton and other crops, as well as more popular as a food crop because it can produce high amounts of calories on small pieces of land. The strong increase in rice cultivation during the last 25 years is remarkable, given the low and highly unreliable rainfall in Sukumaland. Farmers have developed highly productive rainfed, lowland rice systems solely on the basis of their knowledge of soils, rainfall patterns and topography, and on their experiments with water management systems, cultivars, and planting and land preparation methods. Rice management practices closely follow differences in ecology and household characteristics. Selection of rice cultivars is largely determined by water conditions in the field. The cultivation of rice is more intensive in Mwanza region, where transplanting takes place; on the larger rice fields in Shinyanga region, broadcasting dominates. Households grow rice for both food and cash - mainly for food in Mwanza region and mainly for cash in Shinyanga region. Water and weeds are the major production constraints, but low soil fertility is also a problem on the sandier fields of Mwanza region. Yields have declined due to continuous cultivation, with almost no application of organic or mineral fertilizers. On the more clayey rice fields in Shinyanga region yields are, however, still satisfactory at present due to the relatively short period (10-20 years) of cultivation. However, in one out of three years farmers fail to get food and cash from rice due to insufficient, too late or too unreliable rainfall.In response to farmers' complaints about declining rice yields, on-farm soil fertility research was carried out in the rice fields of Sukumaland between 1990 and 1996, using a Farming Systems Research/Extension (FSRE) methodology. The decline was thought to be related to a decrease in soil fertility. On-farm research showed that broadcasting 30 kg N ha -1in the form of urea in wet rice fields at the tillering stage increased rice grain yields by 500-900 kg ha -1in almost every type of rice field cultivated in Sukumaland. Doses higher than 30 kg N ha -1were less economical at 1996 prices for crops and fertilizers. The crop yield response to urea was better when rice plants were at the maximum tillering stage, when water depths were less than 15 cm at application, and when the sand content of fields was higher. The relatively small differences each year in response per field did not justify multiple extension messages. A single dose of 30 kg N ha -1in the form of urea to rice at tillering was thus recommended for Sukumaland as a whole.Despite the relatively high average productivity index for a low dose of urea in rice, there was almost no adoption by farmers. The main factors were problems in the availability of urea in villages and decreasing profitability of the rice-urea technology, due to IMF/World Bank instigated reform measures. Non-adoption was also due to absence of real need to use urea on the more clayey rice fields; poor involvement of the extension service; confusing research messages related to rice soil fertility management; the high degree of uncertainty in Sukumaland farming systems; and low participation of farmers during priority setting for on-farm activities. Effective adoption of agricultural technologies generated by an FSRE methodology calls for strong, institutionalized links with the extension service, commodity research and policy makers. Better coordination of activities between donors and governments is an essential precondition to make such links work.Failures in the adoption of use of urea in rice encouraged researchers and farmers in Sukumaland to look for alternative ways to improve soil fertility in rice fields. Research has been done on the use of locally available resources such as kraal manure and rice husks, and the introduction of green manure and multipurpose trees as an alternative to urea. The performance of green manures and multipurpose trees was meagre due to limited potential for biomass production in the semi-arid climate. Half of the households in Sukumaland have no easy access to cattle manure, and in any case the quality of the available manure is low, due to open air collection and very low addition of crop residues. The relatively large amount of labour involved in transporting and incorporating bulky organic materials like kraal manure, green manure, rice husks and tree leaves in the relatively far and less easily accessible rice fields is also a serious problem.The increase in labour required per hectare is difficult to realize in the thinly populated Shinyanga region, and furthermore is not seen as desirable by households anywhere in Sukumaland, due to the expected decrease in labour productivity. Apart from that, farmers with clayey rice fields see no need to invest so much in soil fertility management. A nutrient balance calculation for the rainfed lowland rice cultivation systems in Sukumaland gave no serious depletion rates for major nutrients, which seems to support the farmers' attitude. Despite massive campaigns promoting the use of organic fertilizers in Sukumaland during the colonial period and recent attempts by the Tanzanian government, the adoption rate is still very low in almost all cropping systems. Only near Mwanza town are farmers applying kraal manure to horticultural crops and, to a lesser extent, maize/cassava fields. The quantities applied are, however, not sufficient to achieve positive nutrient balances on these sandy upland soils.A review of the literature suggests that INM/LEISA successes in SSA are characterized by relatively high intensity of land use, high population density, medium to high livestock density, good market access and presence of large urban markets (in particular for horticulture products) in the vicinity. Further, the active support of SSA governments for their agricultural sectors will be needed if sustainable agriculture is to be attained through INM/LEISA approaches, as well as active intervention to protect their agricultural sectors against competition from industrial countries. INM/LEISA approaches are not appropriate for SSA farming systems that lack these characteristics. Several examples from SSA and Asia show that severe dependence on labour-intensive methods may lead to decreases in labour productivity. LEISA advocates seem to be largely unaware that farming methods based primarily on labour-intensive techniques can lead to the impoverishment of farm households. For resource-poor farmers, sustainable agriculture must first of all be socio-economically viable. Insufficient use of external inputs can turn LEISA into a non-sustainable form of agriculture; therefore LEISA advocates should take a critical look at the impact of structural adjustment programmes in SSA.The main conclusion of this thesis is that INM/LEISA approaches are currently not an appropriate way to generate sustainable soil fertility management in the rice cultivation systems of Sukumaland. Farmers with rice fields located on fertile clayey soils are still satisfied with their grain yields, and are not yet motivated to invest labour and cash in soil fertility maintenance. However, especially in rice fields located on sandier soils, in the future farmers will have to invest considerably in soil fertility maintenance to achieve sustainable rice cultivation. The current situation in Sukumaland makes such investments highly unlikely. The huge increase in rice cultivation in Sukumaland is on the other hand a good example of farmer adaptation to increasing population densities, changes in market opportunities, and soil fertility advantages in the valleys. Negative nutrient balances furthermore do not always justify recommendations to farmers that involve the immediate use of mineral fertilizers and/or organic fertilizers. More farmer participation, especially in priority setting, is necessary to prevent misunderstandings regarding farmers' objectives.Any strategy for future sustainable rice cultivation and agriculture in Sukumaland, including INM or LEISA, must be based on a thorough analysis of biophysical, socio-economic and public policy factors and their linkages. Such a strategy requires a conducive economic and policy environment. In Sukumaland this will require improvements in infrastructure, increased government support to agriculture, reduced taxation in the cotton crop sector, reduced reliance on rice imports, higher population densities, intensive livestock keeping and a greater variety of off-farm employment. A good INM strategy in Sukumaland would then be to use urea in rice, and farmyard manure in the nearby cassava/maize and cotton fields. Without a conducive economic and policy environment, population growth in Sukumaland will lead to an intensification largely based on labour inputs. Instead of agricultural evolution, agricultural involution will be the result.This thesis can be orderd: KIT-publisher R.Gunm@kit.nl