Marie-Helene Jeuffroy

Crop physiology and productivity

Abstract This paper reviews aspects of crop physiology and productivity for selected grain legumes. Vegetative development, including phases of leaf area increase and branching are described, then, the main reproductive stages and their progression along the stem are discussed. The effects of water and nitrogen shortage on reproductive development are briefly described. A model for reproductive development along a stem is proposed and applied to several grain legumes, and effects of genetic variability are discussed. Growth, and its analysis in terms of intercepted radiation and radiation use efficiency are then reviewed. The variability of these two components is analysed according to differences due to species, genotypes (mainly characterized by different foliage structures), environmental conditions and methods of measurement. Yield is then analysed as a direct consequence of crop growth. Finally, a pattern of assimilate partitioning is described, and its consequences for reproductive structure formation, i.e. the grain number on each node of the stem, are discussed.

Wheat, Barley, and Durum Wheat

In the first part of this chapter, we propose to determine the curve of critical plant N% for cereals during the development of the crop ranging from tillering to anthesis, for different cultivars and growth conditions. The results are mainly derived from Justes et al. (1994) with winter wheat. The validity of the curve of critical plant N% will also be evaluated for spring barely and durum wheat.

Replacing the nitrogen nutrition index by the chlorophyll meter to assess wheat N status

The performance of cultivars is strongly linked to the growing conditions that vary according to both controlled and uncontrolled experimental factors. Specifically, there is a need to control the efficiency of N use by wheat, Triticum aestivum L., to minimize nitrogen losses and deficiency. The nitrogen nutrition index (NNI) is a precise indicator of nitrogen status but it does not suit the users’ practical constraints because it requires time-consuming measurements and destructive plant sampling at a precise growth stage. Here we tested the soil plant analysis development (SPAD) chlorophyll meter as an alternative to the nitrogen nutrition index (NNI). The chlorophyll meter is a more convenient, leaf clip-on device that determines the relative amount of chlorophyll present in plant leaves. We first identified which leaf should be used; we then compared SPAD and NNI data from various experiments. We also followed SPAD measurements around flowering time to determine a common time span of measurements for all the cultivars of a trial presenting a wide range of earliness. Our results show a non-cultivar-dependent, exponential relationship between the SPAD index and NNI at flowering, with a r2 equal to 0.89. This result implies that the SPAD chlorophyll meter can be used as an alternative to NNI to measure N status in wheat. We also showed that SPAD measurements can be taken before flowering, e.g. during heading, to characterize nitrogen status at flowering. This result provides an organizational leeway to experimenters who can then follow more precisely the N status of their trials. Thus the SPAD index is a good substitute for NNI because it is convenient to use.

Participatory design of agricultural decision support tools: taking account of the use situations

Although many agronomic researchers currently focus on designing and developing decision support systems, they rarely discuss the methodological implications of such work. In this paper, with the examples of two decision support systems, we propose methodological elements for conducting the participatory design of such tools. Our proposition aims at building dialogue between designers and users but also between humans, tools and work situations. We focus on two main stages: first, a diagnosis of the uses of decision tools within current working situations and, second, the use of a prototype of the tool under design. The first stage serves to characterize the diversity of uses and user situations in order to determine the tool’s flexibility and to identify new concepts for tools. The second stage involves setting up an arrangement whereby a prototype of the decision support tool, open to amendment, can be used in work-like situations and then discussed during debriefing sessions among designers and users. This stage mediates dialogue between all the participants and allows them to develop cross-learning processes. We discuss how these two stages allow for a coordinated expansion of three spaces: the concept space, the knowledge space and the use space. We then discuss the need for such participatory design processes described as dialogical design processes and their contribution to produce new agronomic knowledge supporting a more sustainable agriculture. Finally, we point out a need to provide more opportunity for scientific discussion on participatory design approaches and on design methodology more broadly within the agronomic community.

Partitioning of dry matter and nitrogen to the spike throughout the spike growth period in wheat crops subjected to nitrogen deficiency

Abstract Experiments were conducted over four seasons, in the field, with five cultivars. Nitrogen (N) fertilizer was applied to crops at various dates and rates to achieve various N deficiencies. Crop N status and the accumulation of dry matter (DM) and N in the vegetative parts and in the spike was assessed regularly throughout the spike growth period. Changes in partitioning during the spike growth period were studied using cv. Soissons. For crops subjected to continuous N deficiencies starting on various dates, the proportion of aerial DM in the spike was lower and the proportion of aerial N in the spike was higher than in well-fertilized crops. The earlier the deficiency, the larger were the differences in partitioning. Some crops were subjected to temporary nitrogen deficiencies. In these cases, a nitrogen deficiency starting on a particular date was brought to an end by applying N fertilizer. Crops exposed to temporary N deficiencies displayed a higher proportion of aerial DM and a lower proportion of aerial N in the spike than did crops subjected to continuous deficiencies. Differences in DM and N partitioning became significant at the beginning of the spike growth period if crop N status had been affected by deficiency as early as 800 or 700 degree days before anthesis. At anthesis, the relative proportion of aerial N in the spike (proportion of aerial N in the spike of a crop divided by the proportion of aerial N in the spike of its well-fertilized control) was linearly related to nitrogen nutrition index (NNI), an indicator of crop N status. This relationship was determined for Soissons and four other cultivars. It did not differ significantly between cultivars or between crops subjected to temporary or continuous deficiency. Relative aerial N content and relative spike N content were also related to NNI at anthesis.

Agronomic model uses to predict cultivar performance in various environments and cropping systems. A review

The diversity of growing conditions and the development of new outlets for agricultural products favour a diversity of crop management systems requiring various cultivars, with specific characteristics. Genotype performance is usually assessed through multi-environment trials comparing a variable number of genotypes grown in a wide range of soils, climatic conditions and cropping systems. Field experiments show empirical evidence for the interactions between genotype, environment and cropping system. However, such interactions are rarely taken into account to design ideotypes or for cultivar assessment, or in the definition of crop management plans adapted to cultivars. Agronomic models, built to simulate the dynamic response of crops to their environment, and thus to techniques which modify it, appear to be appropriate tools to evaluate and predict these interactions. This paper reviews the three main uses of model-based predictions of the interactions between genotype, environment and cropping system: definition of breeding targets, characterisation of the environments in cultivar experiments and support for the choice of the best cultivar to grow in a given situation. Models specifically allow understanding the influence of one or a combination of specific traits on performances and long-term ecological impacts. We show that a diversity of models is required, from physiologically based crop models to agroecology-based cropping system or landscape models, able to account well for farmers’ practices. A way of taking cultivars into account in crop models is proposed, based on three main steps: the choice of the model, the identification and estimation of its cultivar parameters, and testing the model for decision support. Finally, the analysis of the limitations for wider use of crop models in variety breeding and assessment addresses some major questions for future research.

Lack of consideration for end-users during the design of agronomic models. A review

Agriculture should now provide not only high yields but also sustainable development with a sound management of the diversity of ecosystems. Due to this increased complexity of objectives, models have recently become major tools that can integrate several parameters. Failure to apply models outside research is however a major issue. Here, to identify the precise grounds of this failure, we analyzed what models are intended for by scientists during their design. We performed a literature analysis on agronomic modelling practices. Specifically, we analyzed 518 scientific article abstracts reporting either new models or improved existing models. Articles were published in eight mainstream agronomy journals over a 10-year period. We also analyzed 25 full-text contents randomly selected from the initial dataset. In order to assess how models match the uses they are intended for, we first analyzed the design methodology used to build models. Second, we studied how authors defined the potential use of models by analyzing both the claimed objectives and references to model use and users. We then compared our findings on design methodology with our findings on intended use. Our results first show that the design methodology for modelling is presented as a segmented and standardized process. Each article refers to one or more of the following six steps to describe the design process for modelling: (1) description of the model structure, inputs, outputs and validity domain, (2) description of the data used to build the model, (3) model formalism, (4) calibration parameterisation, (5) validation, and (6) application. We found that information about the design process like iterations, errors, improvements is never emphasized in the abstracts, whereas this information is sometimes quoted in the full-text contents. This finding demonstrates that the design methodology for modelling is not addressed as a research topic. Second, we show that whereas 88.8% of authors claim in their abstracts that the major objective of their models is to improve understanding as opposed to support action, 19.5% of authors also quote a possible use of their models outside research. The initial objective of understanding is thus extended to use the models as tools for action. Overall, we conclude that the agricultural research community is not highly concerned by the effects of the design methodology on the suitability of the model structure and on potential applications. Moreover, although the six steps of the design process may be appropriate for designing models devoted to improve understanding, no specific methods are proposed to design models for action. We did not find evidence that the modellers connect the design of the model with its use by end-users. We suggest that this issue could be solved by developing participatory methodology design involving end-users in model design.

A simulation model for assimilate partitioning between pods in pea (Pisum sativum L.) during the period of seed set; validation in field conditions

Abstract A theoretical model for assimilate partitioning among pods in pea is proposed. In this model, based on previous results from 14C-labelling experiments, assimilates produced are allocated to each pod in proportion either to their biomass (before the final stage in seed abortion) or to their seed number (after this stage). This model was tested against results obtained from field experiments. Pod growth at each node was measured between the beginning of flowering and the final stage in seed abortion of the last node on the stem. The model predicts the dry matter allocated to pods of each node between two successive sampling dates. Input data are: development parameters, total reproductive growth rate per stem, seed number in pods which have passed the final stage in seed abortion and the dry weight of pods which have not yet reached this stage at the first sampling date. Observed and predicted dry matter amounts allocated to the pods at each node between two sampling dates were highly correlated for all field situations, validating the proposed model.

Comparison of different models predicting the date of beginning of flowering in pea (Pisum sativum L.)

Abstract In pea, the time of flowering is mainly related to the photoperiod ( P ) and mean temperature ( T m ) during the vegetative period. In field conditions, both variables depend mainly on the latitude (LAT) and the date of sowing (RDS). On the basis of these four variables, several empirical models simulating the time to flowering either in days or in degree-days were calibrated (the parameters determined) and compared for pea (cv. Solara). Data were from trials in various locations throughout France over 8 years and with several sowing dates (from mid-November till mid-April). Surprisingly, the model with the more explicative variables ( P and T m ) did not give the most reliable results in field conditions as assessed with a validation sample of situations including many years and locations. The best fit and MSEP (mean square error of prediction) were obtained by combining P , RDS and LAT in the model. Models can be constructed to use days or degree-days: models in days are very useful for crop management, whereas models in degree-days are well-adapted for crop modelling. The case of autumn sowings was analysed separately: models were recalibrated to give a good account of the whole range of sowing dates.

Analysis of young Miscanthus × giganteus yield variability: a survey of farmers' fields in east central France.

Miscanthus × giganteus is often regarded as one of the most promising crops to produce bioenergy because it is renowned for its high biomass yields, combined with low input requirements. However, its productivity has been mainly studied in experimental conditions. Our study aimed at characterizing and explaining young M. giganteus yield variability on a farmers’ field network located in the supply area of a cooperative society in east central France. It included the first three growth years of the crop. We defined and calculated a set of indicators of limiting factors that could be involved in yield variations and used the mixed‐model method to identify those explaining most of the yield variation. Commercial yields averaged 8.1 and 12.8 t DM ha−1 for the second and third growth year, respectively. However, these mean results concealed a high variability, ranging from 3 to 19 t DM ha−1. Commercial yields, measured on whole fields, were on average 20% lower than plot yields, measured on a small area (two plots of 25 m2). Yields were found to be much more related to shoot density than to shoot mass, and particularly to the shoot density established at the end of the planting year. We highlighted that planting success was decisive and was built during the whole plantation year. Fields with the lowest yields also had the highest weed cover, which was influenced by the distance between the field and the farmhouse, the preceding crop and the soil type. Our findings show that growing young M. giganteus on farmers’ fields involves limiting factors different from those commonly reported in the literature for experimental conditions and they could be useful to assess the economic and environmental impacts of growing M. giganteus on farmers’ fields. They could also stimulate the discussion about growing bioenergy crops on marginal lands.