Jacques-Eric Bergez

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.

An open platform to build, evaluate and simulate integrated models of farming and agro-ecosystems

Due to significant changes in agro-ecological contexts, farmers need new solutions to produce goods. Modelling complements field experiments in the design of new farming systems. French researchers involved in such design issues developed a specific modelling platform to help model, simulate and evaluate cropping systems. After testing several existing environments, the RECORD platform was developed under the VLE environment, allowing the design of atomic and coupled models. It integrates different time steps and spatial scales and proposes some standard formalisms used to model agro-ecosystems (e.g. difference equations, differential equations, state charts...). A graphic user interface was designed to simplify coding tasks. A variety of research projects already use this platform. Examples are given showing the ability to recode simple models, encapsulate more complex models, link with GIS and databases, and use the R statistical package to run models and analyse simulation outputs. The option to use web interfaces enables application by non-scientist end-users. As the models follow a given standard, they can be placed in a repository and used by other researchers. Linking RECORD to other international platforms is now a compelling issue.

Improving irrigation schedules by using a biophysical and a decisional model

Abstract New irrigation scheduling approaches aimed at increasing efficient use of the allocated irrigation water, so as to give the highest crop production with the least water use, must be developed. Models can be used for such a purpose as they enable rapid and extensive condition testing. This study uses the management-oriented cropping system model MODERATO to improve irrigation schedules of a corn crop on a medium clay-silt soil of southwestern France, with no constraints regarding the amount of water for irrigation. Weather records from 1949 to 1997 are available of the study site. A ‘basic’ strategy resulting from discussions with the irrigation advisors is tested first. Then the starting, returning and ending rules for irrigation are optimised. The optimisation process is based on a step-by-step screening of the parameters for each rule. The calculated optimal strategy is the one which gives the highest average direct margin from irrigation over the 49 years of the weather records. Applying the ‘basic’ strategy gives a 10.30 Mg ha −1 average yield using 312 mm of irrigation water. The average direct margin is 454.76 € ha −1 . Optimising the starting and returning rules leads to a 555.68 € ha −1 average direct margin with a slightly lower yield (−0.11 Mg ha −1 ), but the amount of water used for irrigation is significantly reduced (−111 mm). Optimising the ending rule allows a further 7.62 € ha −1 , mainly due to an average 20 mm decrease in irrigation water used. The use of MODERATO as a training aid for irrigation problems is then discussed and considerations regarding the calculated optimal strategy are debated.

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.

A package of parameter estimation methods and implementation for the STICS crop-soil model

Parameter estimation for complex process models used in agronomy or the environmental sciences is important, because it is a major determinant of model predictive power, and difficult, because the models and associated data are complex. Statistics provides guidance for parameter estimation under various assumptions concerning model error, but it is hard to know which assumptions are most acceptable for these models. We therefore propose a collection of parameter estimation methods. All are based on weighted least squares, but different assumptions lead to different weights. The methods allow one to fit simultaneously several different response variables. One can assume that all errors are independent or on the contrary are correlated. One can assume that model error has expectation zero or not. A software package called OptimiSTICS has been developed, that allows one to implement all of the proposed methods with the STICS crop-soil model. The software can in addition treat the case where some parameters are genotype specific while others are common to all genotypes. The software can also automatically do several sequential stages of parameter estimation. An example is presented, which shows the information that can be obtained, and the conclusions drawn, from comparing the different estimation methods.

IRRIGATE: A dynamic integrated model combining a knowledge-based model and mechanistic biophysical models for border irrigation management

Water management practices in southern France (the Crau plain) need to be modified in order to ensure greater water use efficiency and less environmental damage while maintaining hay production levels. Farmers, water managers and policy makers have expressed the need for new methods to deal with these issues. We developed the biodecisional model IRRIGATE to test new irrigation schedules, new designs for water channels or fields and new distribution planning for a given water resource. IRRIGATE simulates the operation of a hay cropping system irrigated by flood irrigation and includes three main features: (i) border irrigation with various durations of irrigation events and various spatial orders of water distribution, (ii) species-rich grasslands highly sensitive to water deficit, (iii) interactions between irrigation and mowing. It is based on existing knowledge, adapted models and new modules based on experiments and survey data. It includes a rule-based model on the farm scale, simulating dynamically both irrigation and mowing management, and two biophysical models. The two biophysical models are a dynamic crop model on the field scale simulating plant and soil behaviour in relation to water supply, and a flood irrigation model on the border scale simulating an irrigation event according to plant and hydraulic parameters. Model outputs allow environmental (water supply, drainage), social (labour) and agronomic (yields, water productivity and irrigation efficiency) analyses of the performance of the cropping system. IRRIGATE was developed using firstly a conceptual framework describing the system modelled as three sub-systems (biophysical, technical, and decision) interacting within the farm. Then a component-based spatially explicit modelling based on the identification of the interactions between modules, the identification of temporal and spatial scales of modules and the re-use of previous models was used to develop the numerical model. An example of the use of the biodecisional model is presented showing the effects on a real farm of a severe water shortage in 2006.

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.

Maize grain yield variability between irrigation stands: a theoretical study

Variability of irrigation application has been mainly studied in terms of uniformity of water distribution on the field scale. However, to irrigate a whole field several days are required and therefore a within-field yield variability is expected. In this paper, the maize grain yield variability due to the time required to irrigate different stands of a field is studied. The management-oriented cropping system model MODERATO is used on that purpose. Variation in soil depth, available soil water capacity, flow rates and irrigation regime are tested. Flow rates and irrigation regimes modify the number of days required to irrigate the whole field. It is then shown that for the given tested configurations, the maximum grain yield variability is on average 1.41 Mg ha-1 and for some specific year the value reaches 2.11 Mg ha-1. Maize grain yield variability decreases when the flow rate increases, the soil depth increases, the gravimetric soil available water capacity increases and the irrigation amount decreases. A graphical analysis of the variability is then proposed. The integration of this knowledge in decision tools is then discussed. Author Keywords: Maize; Irrigation scheduling; Dynamic modelling; Decision rules; Grain yield variability

Stockless organic farming: strengths and weaknesses evidenced by a multicriteria sustainability assessment model

Agronomists need methodologies to assess the sustainability of cropping systems. Few models such as MASC have been recently developed for evaluation. The effective use of those models is still a challenge, notably for low-input systems. Here a more specific model entitled MASC-OF was developed and applied to study stockless organic cropping systems. The MASC-OF model is original because it is based on agricultural advisers’ needs and expertises. Two groups of advisers supported by agronomic scientists were involved in a nine-step methodology to progress from preliminary meetings to data analysis. The methodology allowed advisers to design a model including their own views on what is a sustainable organic cropping system. Soil fertility and weed and pest control were integrated as a new branch in the original MASC model. We also developed evaluation criteria for each basic attribute, defining aggregation rules and weighting attributes. Tested case studies were based on 44 real cropping systems identified on 19 farms in the Midi-Pyrenees region of France and on 23 cropping system types developed by the advisers from the Centre, Ile-de-France, Pays de Loire, Poitou-Charentes and Rhône-Alpes regions of France. Our results show that a high score of economic sustainability is the most difficult to achieve. This finding is explained by low productivity of cereal crops and high variability of market prices for organic grain. Further, agronomic viability is also difficult to ensure, as a consequence of poor soil-fertility management practices. The ability to achieve social acceptability for the producer, including workload and health risk, is high. By contrast, acceptability for the society has a medium score due to reduced productivity and contribution to local employment. Environmental sustainability is the easiest dimension to achieve, despite nitrogen-loss risks in some situations and high water and energy consumption in irrigated systems. Overall our findings show that the potential for the development of more sustainable organic cropping systems in stockless farms is high.

Evaluating integrated assessment tools for policy support

Integrated Assessment Modelling tools are complex tools requiring specific evaluation methodologies. Based on the example of the SEAMLESS-Integrated framework, we show how the conceptual, technical and system evaluation steps of the different components (procedures, quantitative models, graphic user interfaces) were performed by a multidisciplinary team. To make the not-yet-available tool real, mock-up and test cases were mobilized throughout the development process in order to integrate final end-users in the evaluation process. The main lessons from the project are that the evaluation required: (i) the use of prototypes to advance properly in the design and testing (spiral methodology); (ii) the use of case studies to stick to the end-users requirements; (iii) a proper timing of development and delivery in order to keep on schedule and leave time to the evaluation process; (iv) a multidisciplinary team of evaluators as tools are of diverse types; and (v) that it is difficult to keep independence between testers, end-users and modellers in order to guaranty transparency in the development and evaluation process.