Delphine Leenhardt

MODERATO: an object-oriented decision tool for designing maize irrigation schedules

Abstract The rapidly changing economic, technical and regulatory context of irrigated agriculture, coupled with seasonal variation in precipitation, presents a problem for irrigation management, especially in sub-humid regions. During years of drought, frequent irrigation bans may be applied and shortage of water for crops becomes a critical problem. Simulation models offer the opportunity to optimise production strategies, such as optimal irrigation scheduling. But few biophysical models are designed for decision making. MODERATO is a management oriented cropping system model developed for use at a strategic level by irrigation advisors confronted with the question: ‘How to irrigate maize with a limited amount of irrigation water?’. It includes the main constraints specifically related to irrigation (work time, available amount of water, flow rate, blackout days), simulates the plant-soil system with a dynamic biophysical model (parametrized on a large database) and takes into account within-field variability that results from sequentially irrigating the plots in a block of irrigation. Five elementary irrigation rules are distinguished: (1) a rule to irrigate to facilitate plant emergence; (2) a rule to decide when to start the main irrigation period; (3) a rule to determine when to start a new irrigation cycle; (4) a rule to decide when to stop irrigation; and (5) a rule to delay irrigation due to weather conditions. The elementary rules consist of two boolean conditions which depends respectively on development stage and soil water availability. The details of the rules are input using a graphic user interface. The dynamic biophysical model is based on the well-known interception–conversion process. The model outputs allow one to analyse the consequences of the decision rules for various climatic series and context. MODERATO is the result of 3 years of collaborative research between scientists and irrigation advisors and has been used to calculate optimized starting and ending rules for irrigation on a specific pedoclimate.

Describing and locating cropping systems on a regional scale. A review.

At regional scale issues such as diffuse pollution, water scarcity and pollen transfer are closely related to the diversity and location of cropping systems because agriculture interacts with many other activities. Although sustainable land use solutions for territorial development and natural resource management are needed, very few agro-environmental studies account for both the coherence and the spatial variability of cropping systems. The originality of this article is to review methods that describe and locate cropping systems within large areas. We mainly based our analysis on four case studies using the concept of cropping systems on a regional scale, but differing in their objectives and extents. We found that describing and locating cropping systems in space meets not only decision-making stakes but also a scientific stake that allows multi-simulations over large areas when models require cropping system information. Simulation models are indeed necessary when the study aims at estimating cropping system externalities. Then, the involved process determines the extent, and the model determines the support unit, unless socio-economic considerations prevail. In this case, as well as when no model is involved, it is often considerations related to stakeholders that determine extent and support unit choices. On a regional scale, the cropping system must be described by only a few variables whose selection depends on the study objective and the involved processes. Collecting cropping system information for all support units is often simplified by identifying determining factors of cropping systems. However, obtaining deterministic relations between easily accessible factors and cropping system variables is not always possible, and sometime accessing modalities of determining factors for all support units is also difficult. We found that describing and locating cropping systems relied very much on expertise and detailed survey data. The development of land management practice monitoring would facilitate this description work.

A generic framework for scenario exercises using models applied to water-resource management

Natural-resource management that concerns multiple agents with a variety of interests can be facilitated by integrated assessment methods which include modelling and/or stakeholder participation. Integrated assessment methods are increasingly used for scenario approaches that enable policy-makers to explore possible futures and assess potential consequences of different policy or management strategies. The paper proposes a conceptual and operational framework to illustrate a scenario exercise, based on a previously developed model, by building on recently published progress on the participatory and model-based assessment approach. This framework focuses on information flows in two key operational phases, problem specification and adaptation of model outputs, where scientists and stakeholders interact. In both phases, transformation steps convert narrative information into a quantitative form (and vice-versa), thereby enabling scientists to apply computer models and decision-makers to get confident in model predictions. On the basis of four case studies aimed at solving complex water-resource management problems, we illustrate the difficulties, constraints and questions of each step of the proposed framework and present original solutions. This framework, which can be applied to all natural-resource management issues, clearly defines the step(s) at which each partner should be involved in a scenario exercise and his/her contribution. Consequently, by having greater foresight and transparency, the framework determines the nature of interactions between scientists and non-scientists. A posteriori, it also describes how a scenario exercise was conducted.

Estimating the spatial and temporal distribution of sowing dates for regional water management

Accurate prediction of water demand by agriculture is a key point of water management. Current procedures of water management often use simple models and parameterisation to predict demand, that may fail in particular climatic situations. In a particular area of south-western France, it is suspected that such failure was due to poor estimation of sowing dates. To solve this problem, a model was developed to estimate the spatial and temporal distribution of summer crop sowing dates. This paper presents the development and the test of the model. The model (SIMSEM) is based on a farm typology, a soil water balance model and supplemental expert knowledge. Although many inaccuracies remain, the distributions predicted compared fairly well to observed distributions obtained from a field survey. The model improved estimated sowing dates compared to the method currently used in water management calculations. The use of SIMSEM to provide information about sowing dates seems helpful for better predicting water demand, as shown by tests performed for the critical 1998 year where the current procedure failed.

Describing and Locating Cropping Systems on a Regional Scale

At regional scale issues such as diffuse pollution, water scarcity and pollen transfer are closely related to the diversity and location of cropping systems because agriculture interacts with many other activities. Although sustainable land use solutions for territorial development and natural resource management are needed, very few agro-environmental studies account for both the coherence and the spatial variability of cropping systems. The originality of this article is to review methods that describe and locate cropping systems within large areas. We mainly based our analysis on four case studies using the concept of cropping systems on a regional scale, but differing in their objectives and extents. We found that describing and locating cropping systems in space meets not only decision- making stakes but also a scientific stake that allows multi-simulations over large areas when models require cropping system information. Simulation models are indeed necessary when the study aims at estimating cropping system externalities. Then, the involved process determines the extent, and the model determines the support unit, unless socio-economic considerations prevail. In this case, as well as when no model is involved, it is often considerations related to stakeholders that determine extent and support unit choices. On a regional scale, the cropping system must be described by only a few variables whose selection depends on the study objective and the involved processes. Collecting cropping system information for all support units is often simplified by identifying determining factors of cropping systems. However, obtaining deterministic relations between easily accessible factors and cropping system variables is not always possible, and sometime accessing modalities of determining factors for all support units is also difficult. We found that describing and locating cropping systems relied very much on expertise and detailed survey data. The development of land management practice monitoring would facilitate this description work.

Geo-referenced indicators of maize sowing and cultivar choice for better water management

Agriculture is a major consumer of water, with up to 88% of the total water consumption in summer in irrigated regions, either in France or, for instance, in Australia. Good water management therefore requires an accurate estimation of regional water demand by agriculture, which depends on both soil and weather conditions and on farmers’ practices. We studied the farmers’ practices that influence maize irrigation: sowing and the choice of cultivar in regard to its earliness. Specifically, we aimed to identify geo-referenced indicators that could be used to estimate the spatial and temporal distribution of the various combinations of sowing date, sowing density, sown area and maize earliness. The study was conducted in a 500-km2 irrigated area in south-western France. We first conducted a quantitative analysis of postal survey data to identify environmental factors and farm descriptors that could determine sowing practices and the choice of earliness of cultivar. We then interviewed a group of farmers to find out the main constraints relevant to the sowing date and earliness of cultivar. We identified variables that can be used as indicators of the spatial variability of the studied practices. Our results show that the spatial distribution of sowing date and cultivar earliness over a region can be estimated from climatic descriptors of the area and structural farm characteristics. The first factor allows estimation of tactical variables, the sowing starting date and the cultivar earliness groups, while the second allows estimation of sowing and earliness choice strategies. This is one of the first studies identifying on a regional scale geo-referenced indicators of a crop management system, and the first that provides a conjunctive estimation of sowing and earliness choice practices on a regional scale. This study suggests that for estimating any crop management system, it is helpful to treat strategic and tactical variables separately.

A Modelling Solution for Developing and Evaluating Agricultural Land-Use Scenarios in Water Scarcity Contexts

To meet sustainability challenges, regional water management and planning require approaches that assess the land-use visions of various stakeholders using their own evaluation criteria. Models and information systems are keystones in such integrated assessment activities. SPACSS (the SPAtial Cropping System Scenarios builder and evaluator) is a modelling solution that aims to help decision-makers evaluate normative land-use scenarios. A prototype of SPACSS was developed to explore concerns raised by a dam-building project in south-western France, specifically the relation between cropping system distribution and water uptake. This paper presents the initial steps of SPACSS development by scientists and agricultural experts and its evaluation by users through alternative scenarios of maize cropping (altering either its precocity or management to reduce irrigation). SPACSS can represent a wide range of land-use scenarios and aggregate impact indicators at several spatial and temporal scales. Although SPACSS served as a solid support for discussions with stakeholders and decision-makers, it needs modifications to represent more realistic, and thus more complex, land-use scenarios. These modifications will make SPACSS potentially valuable for dealing with a variety of issues concerning agricultural landscapes, far beyond the single question of quantitative water management.

Optimising irrigation management at the plot scale to participate at the regional scale water resource management

According to the FAO, a great challenge for the coming decades will be the task of increasing food production to ensure food security for the steadily growing world population. Most of that increase will have to come from intensified agriculture, supported by irrigation. Where irrigated agriculture is developed, water used for irrigation can represent more than 90% of water consumption. In an increasing number of countries, existing resources are fully exploited. Drinking-water supplies and the maintenance of a continuous, minimum water flow in rivers are often given priority over irrigation. Planning and management of water resources have become a very important issue everywhere in the world. In particular, accurate estimation of water demand by agriculture is a key need for water management. Water management includes planning (with decisions on a multiyear time scale, such as building dams), strategic management (seasonal decisions with possible adjustment during the season, such as determining the total water volume used for irrigation), and tactical management (daily decisions, such as releasing water from particular dams). Developing tools by integrating knowledge in models may be of some help for planners. However, modeling water management is complex because it concerns different scales (scale of decision, scale of action, scale of planning, scale of management) and different actors (water users, including farmers, factory managers, and general public, policy makers, and water manager). Crop models that simulate the dynamic of plant growth and water demand of one or several crops can provide quantitative contributions to the environmental impact assessment and be very useful for water management