Farm typology in the Berambadi watershed (India)

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Today’s and tomorrow’s challenges for the agriculture

Agriculture is facing many challenges both in terms of productivity and revenue and in terms of environmental and health impacts. Agriculture must thus face a demand for increasing production regarding quantity, quality, accessibility and availability to secure food production and improve product quality to cope the needs of the world’s growing population (Meynard et al. 2012; Hertel 2015; McKenzie and Williams 2015). The FAO (Food and Agriculture Organization of the United Nations) estimates that global agricultural production must increase by nearly 60% from 2005/2007 to 2050 to meet the food demand of the estimated 9 billion people by 2050 while ensuring fair incomes for farmers (FAO 2012). Increasing agricultural productivity is all the more important to face the increasing competition for land, water and investment between urban, agricultural and industrial sectors (FAO 2011).
However, increasing agricultural productivity must be made within a framework of environmental and health constraints. First it should consider limiting the impact on the environment, by reducing the impacts on water and aquatic environments (nitrate, pesticide, drug residue pollutions through leaching and runoff), on air (nitrous oxide, methane, ammonia and other greenhouse gas) and finally on soil (soil structural discontinuity, compacted areas, risk of leaching and erosion, decline in soil biodiversity). It should also consider limiting habitat modification to encourage and maintain biodiversity. Second, agricultural productivity should take into account the scarcity of resources mobilized by agricultural production such as water resources, phosphorus and fossil energy (particularly for the production of nitrogen fertilization) (FAO 2011; Brown et al. 2015).
These agricultural challenges also have to be considered within the known context of climate change. Under climate change conditions, warmer temperatures, changes of rainfall patterns and increased frequency of extreme weather are expected to occur. The global mean temperature expected by the end of this century could be 1.8° to 4.0°C warmer than at the end of the previous century within an uneven pattern across the globe. Climate change could lead to extreme climatic events, such as increased intensity and frequency of hot and cold days, storms, cyclones, droughts and flooding (Anwar et al. 2013). Climate change alters weather conditions and thus has direct, biophysical effects on agricultural production and would negatively affects crop yields and livestock (Nelson et al. 2014). Sea-level rises will increase the risk of flooding of agricultural land in coastal regions. Changes in rainfall patterns may support the growth of weeds, pests and diseases (Lapeyre de Bellaire and al. 2016).

Designing farming systems

Facing the aforementioned challenges, conventional farming systems have their limitations and a particular attention is made on the dynamics of innovations likely to consider and resolve the former issues (Novak 2008). In a broad sense, innovation is seen as the action of « transforming a discovery on a technique, a product or a conception of social relationship into new practices » (Alter 2000).
In agronomy, innovation is generally defined as a process which promotes the introduction of new changes and leading to its spread and its recognition through applications cases (INRA Sens 2008; Klerkx et al. 2010). Innovation requires a design process based on scientific and / or empirical knowledge. The design process is conducted by agricultural and development research institutes in close collaboration with farmers to address their needs, their constraints and their knowledge on agricultural production systems (Le Gal et al. 2011).
Two ways of designing systems are distinguished: i) the rule-based design aims at gradually improving existing technologies and systems, based on predefined objectives and standardized evaluation processes (Meynard et al. 2012); ii) the innovative design is built to meet completely new expectations initially undetermined but getting more and more specific as the exploration process takes shape (Meynard et al. 2012; Lefèvre et al. 2014). In an uncertain and changing environment, traditional rule-based analytical frameworks are challenged. The adaptable design approaches that take into account varying objectives, skills and modes of validation and do not need to be specified in advance may be preferable (Meynard et al. 2012).
Different tools and methods have already been developed to address the issue of farming system designs. Loyce and Wery (2006) classified them into three groups: (i) diagnosis (e.g. Doré et al. 1997) allows to understand and evaluate agricultural systems from field measurements and surveys, (ii) prototyping (e.g. Vereijken 1997) consists in designing a limited number of systems based on expert-knowledge, in testing and evaluating them, and in adapting the prototypes; (iii) model and simulation based approaches (e.g. Romera et al. 2004) where the model allows to design a simplified representation of a real system and the simulation allows to change the state of the system in order to understand and evaluate its behavior.
Given the complexity of the agricultural production systems, simulation modeling is a commonly used tool for the design and the evaluation of innovative agricultural production systems (Bergez et al. 2010). Indeed, systemic modeling and dynamic simulations appear to be powerful tools to represent the dynamic interactions between biological and technical processes at different time and space scales and to assess and quantify the performances of a variety of alternative systems for a diversity of production contexts (Bergez et al. 2013).

Agricultural production systems and complex systems

Definition and organization of farming systems

An agricultural production system is defined as a complex system of resources, technical activities, biological processes and decisional processes that aims at meeting farm production objectives by producing agricultural goods (Tristan et al. 2011). The agricultural production system is a complex matrix of interdependent items that are partially controlled by the farm manager or the farm household subjected to a socio-economic and climatic external environment (Figure 1.1).
Dury et al. (2013) identified five categories of objectives that drive decisional processes within farming systems: 1) financial like maintain, secure, increase or maximize farm income, 2) workload by decreasing, minimizing, maintaining or spreading working hours, 3) farm status considering the future of the farm, 4) technical aspects on crop management techniques, 5) environmental aspects with reasoning on biodiversity and pollution.
Decisional processes aim at developing a resource management strategy that transforms land, capital, labor resources into agricultural products taking into consideration infrastructure and intuitional constraints such as equipment, storage and transportation, marketing facilities and farm credits. This transformation is the result of farmer’s short-term technical activities on the farm. The farmer mobilizes knowledge to make decisions based on know-how, skills and specific observations made previously on his production system.
Agricultural production system may be composed of several production sub-systems with specific production objectives. Three main production subsystems are identified in Coléno et al., (2005): crop production, animal production and transformation unit. These sub-systems are interrelated since the end product and wastes of one sub-system may be used as inputs in others (Figure 1.1).
Figure 1.1 : Agricultural production system organized into three integrated production subsystems (from Coléno et al. 2005).

Management of farming system

The farmer dynamically plans and coordinates his technical activities on his farm at different time and space scales. However agricultural production systems are facing new challenges due to a constantly changing global environment that is a source of risk and uncertainty, and in which past experience is not sufficient to gauge the odds of a future negative event. Concerning risk, farmers are exposed to production risk mostly due to climate and pest conditions, to market risk that impact input and output prices, and institutional risk through agricultural, environmental and sanitary regulations (Hardaker 2004). Farmers may also face uncertainty due to rare events affecting, e.g. labor, production capital stock, and extreme climatic conditions, which add difficulties to the production of agricultural goods and calls for re-evaluating current production practices. To remain competitive, farmers have no choice but to adapt and adjust their daily management practices (Hémidy et al. 1996; Hardaker 2004; Darnhofer et al. 2010; Dury 2011).
Based on his past experience and on forecasts on weather and market prices, the famer can anticipate some events and production conditions. Thus he is able to plan several management options to face these different production conditions. However, given the limitations of human cognition to anticipate the future, everything cannot be anticipated (Chavas 2012). The farmer must therefore be able to establish a reflexive analysis on the observations he made on his environment in order to instantly review his initial management plan and if necessary his production objectives. The farmer’s decision-making process is therefore a dynamic sequence of planning, observation, reflection, adaptation, implementation as technical activities and learning processes (Risbey et al. 1999; Le Gal et al. 2011). This variable and uncertain production context justifies why a management plan repeated over several years won’t give the same production results and why different management plans may lead to the same production results. Farmer’s decision-making is a continuous process in time and space. Farmers make decisions based on his visibility and expectations on the production context that impact his management on the long-, medium- or short-term. Decisions may affect the whole agricultural production system, a production sub-system or even smaller spatial unit such as the plot (Cerf and Sebillotte 1988; Papy et al. 1988; Osman 2010). For instance, investing in equipments, in buildings or in lands are decisions that reflect a willingness to expand or modernize the farm. These decisions have long-term consequences because 1) loans are often over several years, 2) the farm structure and infrastructures are changed for the coming years (life duration of a tractor, building, etc.). However, decisions on selecting varieties and crop management techniques have an impact on the short term and at a local scale corresponding to the production season and the plot. Finally, deciding to delay the sowing, to extend the water turns or to apply pesticide treatment will have an immediate effect on the biophysical system because these decisions correspond to technical activities executed on each plot.

Specificities of irrigated farming systems

A production system is considered as irrigated when water supply other than rainfall is provided on one or several plots. The irrigation water is pumped from a water point and distributed to the fields through appropriate water transport infrastructure. In irrigated production systems, crops benefit from both the contribution of rainfall and irrigation water to cover their water needs. Irrigation is an effective management tool against the variability and uncertainty of rainfall events. The irrigation water can come from surface water fed by the rainfall runoff like streams, rivers, ponds, lakes and dams. Irrigation water can also come from the aquifer that is fed by the rainfall drained into the ground. The deep aquifers are located between two impermeable layers leading to slow recharge compared to surface water reservoirs.
Irrigation water can come from different sources considered as collective when multiple users are identified or individual. Except for individual rainfall reservoirs, the other sources of irrigation are often subject to conflicts and management issues (Gleick 1993; Wolf 2007). Conflicts over rivers between upstream and downstream users are commonly seen as the upstream pumping will impact the downstream flow (Chokkakula 2015). On a reservoir, tensions appear when pumping exceeds the rainfall recharge from run-off particularly in drought conditions (Rajasekaram and Nandalal 2005).
For groundwater, pumping may exceed rainfall recharge. Moreover, lateral flows conduct the water table level to rebalance so that intensive pumping by one farmer impacts the yield of the neighbor’s borewell (Janakarajan 1999).

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Global challenges in designing agricultural production systems

The production of knowledge on agricultural production system is an important issue while designing such system. Several types of knowledge have to be produced to understand the complexity of systems: 1) knowledge on the system structure to understand its organization and composition; 2) knowledge on internal processes e.g. decision processes, biophysical processes; and 3) knowledge on inputs and outputs to understand the exchanges of information and matters within the system and with the external environment as well as the impact of an entity on another entity (how climate change impacts on farmers’ decision making processes). The use of appropriate tools to collect and organize knowledge is important to ensure the quality of the knowledge production process.
Another challenge in designing agricultural production systems is to properly define the limits of the system to be designed. Agricultural production systems are too complex to be entirely designed as they are. The level of specificities and details to be considered depend on the initial research question.
Designing agricultural production systems requires considering the time and space scales at which processes should be represented. Some processes may occur at several time and space scales (e.g. decision processes), others are specific to only one scale (e.g. sowing is made a defined date on a defined plot). An interesting issue in designing systems is to be able to upscale or downscale a representation.

THESIS PROJECT

Research context

The Indian agriculture

India is the most populous country in the world after China. India has 17.5% of the global population with 1.26 billion people in 2015. The growth rate of its population was 1.2% in 2014. A third of the Indian population (212 million) is undernourished and lives below the extreme poverty line (Central Intelligence Agency 2016). Famine and poverty remain a major obstacle to the country development.
India is the world’s fourth-ranking agricultural power. In 2014, Indian agriculture accounted for 17.8% of GDP and employed 49.7% of the workforce. India has an important agricultural area of over 190 million hectares of which 37% is irrigated. Climatic gradient, topographic and soil diversity allow a wide range of crops (India Brand Equity Foundation 2016). The main agricultural products are wheat, millet, rice, corn, sugarcane, tea, potato, cotton. Productivity and yields have risen sharply since the 1950s after the Green Revolution with the development of irrigation, the use of high-yield seeds and fertilizers and the availability of bank loans. However, subsistence farming is still dominant in India today. Farm households grow on small plots and crops are partly self-consumed (Dorin and Landy 2002). Indian farms have an average size of 1.5 hectares. This fragmentation of holdings is inheritance of the land reform made in 1947 after the Independence from the British that had the aim to redistribute land to poor farmers by restricting the size of the landed property (Chandra 2000). This fragmentation contributes to the low mechanization of farming where animal traction and manual labor are still dominant in Indian agriculture.
Three seasons regulate the farm cropping system: i) kharif (June to September) which corresponds to the South-West monsoon season, when almost all the cropping area is cultivated, either exclusively rainfed or with complementary irrigation; ii) rabi (October to January), the North-East monsoon season or winter season, when most of the plots where irrigation is possible are cultivated; and iii) Summer (February to May), the hot and dry season, when only few irrigated plots are cultivated. Despite the development of irrigation promoted by the Green Revolution, two-thirds of Indian agricultural production are still heavily dependent on the monsoon and are produced in kharif. Investments in infrastructure are also limited. Storage and conservation facilities of agricultural products are lacking in the rural area of the country and cause huge losses of up to 40% of crops for fruits and vegetables (Dorin and Landy 2002). After harvest, farmers are compelled to sell immediately their products and often at low prices. The lack of maintenance of irrigation canals and wells are causing the loss of over a third of transported water (Aubriot 2013). In this context of increasing population and industrial development, conflicts over the water resource use are increasing (Chokkakula 2015).

Table of contents :

Introduction – Synthesis Chapter
1.1. General introduction
1.1.1. Today’s and tomorrow’s challenges for the agriculture
1.1.2. Designing farming systems
1.1.3. Agricultural production systems and complex systems
1.1.4. Global challenges in designing agricultural production systems
1.2. Thesis project
1.2.1. Research context
1.2.2. Thesis objectives
1.3. Thesis proceedings
1.4. Thesis results
1.4.1. Literature review on adaptation in decision models
1.4.2. The Berambadi watershed
1.4.3. A methodology to guide the design of a conceptual model of farmers’ decision-making processes
1.4.4. The conceptual model NAMASTE
1.4.5. Strategic decisions on investments and cropping systems modeled with a stochastic dynamic programming approach
1.4.6. The computer model NAMASTE
1.5. Discussions and prospects
1.5.1. Review on the thesis results
1.5.2. From the demonstration prototype to the production model
1.5.3. Verification and validation
1.5.4. A decision model for the AICHA project
1.6. Conclusion
Processes of adaptation in farm decision-making models- A review
2.1. Introduction
2.2. Background on modeling decisions in agricultural economics and agronomy
2.3. Method
2.4. Formalisms to manage adaptive decision-making processes
2.4.1. Formalisms in proactive adaptation processes
2.4.2. Formalisms in reactive adaptation processes
2.5. Modeling adaptive decision-making processes in farming systems
2.5.1. Adaptations and strategic decisions for the entire farm
2.5.2. Adaptation and tactic decisions
2.5.3. Sequential adaptation of strategic and tactical decisions
2.6. Discussion
2.6.1. Adaptation: reactive or proactive process?
2.6.2. Decision-making processes: multiple stages and sequential decisions
2.6.3. What about social sciences?
2.6.4. Uncertainty and dynamic properties
2.7. Conclusion
Farm typology in the Berambadi watershed (India): farming systems are determined by farm size and access to groundwater
3.1. Introduction
3.2. Materials and methods
3.2.1. Case study: Hydrological and morphological description of the watershed
3.2.2. Survey design and sampling
3.2.3. Analysis method
3.3. Variability and spatialization of farm characteristics and practices
3.3.1. Farm structure
3.3.2. Farm practices
3.3.3. Water management for irrigation
3.3.4. Economic performances of the farm
3.4. Typology of farms in the Berambadi watershed
3.4.1. Characteristics of farm typology
3.4.2. Characteristics of the farm types
3.5. Discussion
3.6. Conclusion
Acknowledgements
Table caption
Figure caption
CMFDM: A methodology to guide the design of a conceptual model of farmers’ decision-making processes
4.1. Introduction
4.2. Founding principles of the methodology
4.3. The CMFDM methodology
4.3.1. Step 1: problem definition
4.3.2. Step 2: case study selection
4.3.3. Step 3: data collection and analysis of individual case studies
4.3.4. Step 4: the generic conceptual model
4.4. Methodology implementation in a case study
4.4.1. Step 1: problem definition
4.4.2. Step 2: case study selection
4.4.3. Step 3: data collection and analysis of individual case studies
4.4.4. Step 4: the generic conceptual model
4.5. Discussion – Conclusion
Acknowledgements
Figure caption
Adaptive and dynamic decision-making processes: A conceptual model of production systems on Indian farms
5.1. Introduction
5.2. Modeling processes
5.2.1. Indian case study
5.2.2. Modeling steps
5.2.3. Conceptual validation
5.3. A conceptual model of production systems on Indian farms
5.3.1. What should be modeled
5.3.2. Modeling in NAMASTE
5.3.3. Description of the other systems in the model
5.4. Discussion
5.5. Conclusion
Acknowledgements
Table caption
Figure caption
A stochastic dynamic programming approach to analyze adaptation to climate change – application to groundwater irrigation in India
6.1. Introduction
6.2. Literature review on long-term farmer decisions under uncertainty
6.3. Methods
6.3.1. The farmer’s production problem
6.3.2. Data
6.4. Simulations and results
6.4.1. Scenarios
6.4.2. Results
6.5. Discussion
6.6. Conclusion
Acknowledgements
Table caption
Figure caption
NAMASTE: a dynamic model for water management at the farm level integrating strategic, tactical and operational decisions
7.1. Introduction
7.2. Materials and methods
7.2.1. Conceptual modelling
7.2.2. RECORD: a modeling and simulation computer platform
7.3. Description of the farming system model
7.3.1. Models used to build the farming system model
7.3.2. Model structure
7.3.3. Dynamic functioning
7.4. Application case: the NAMASTE simulation model
7.4.1. Coupling the farming system model to the hydrological model
7.4.2. NAMASTE simulaton
7.4.3. Calibration and validation
7.4.4. Simulation results
7.5. Discussion
7.6. Conclusion
Acknowledgements
Figure caption
Bibliography
Appendix
Appendix 1: Thesis sequence of events
Appendix 2: Data used in NAMASTE development
Appendix 3: Conceptual model and Ontology
Appendix 4: Economic model – Model equations
Appendix 5: Economic model – Yield estimations and climatic expectations
Appendix 6: Operational decision and modeling
Appendix 7: Coupled model – a village with two farms

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