Elaboration du rendement et fertilisation azotée du blé d'hiver en Champagne crayeuse II. - Types de réponse à la fumure azotée et application de la méthode du bilan prévisionnel

Meynard, Jean-Marc; Boiffin, Jean; Caneill, Jacques; Sébillotte, Michel

doi:10.1051/agro:19810912

Cited by 29 publications

(17 citation statements)

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“…Umax is the level ofN uptake at which yield reaches its maximal value Y max· The function is thus continuous at Umax· A quantity often of interest is b = Uma.IYmax. which is N uptake per unit of yield, evaluated at the start of the yield plateau (Meynard et a!., 1981). To include this parameter explicitly in the model, we replace the parameter Umax by b Ymax· The parameters to be estimated are then a, b, and Ymax· The second yield model is a quadratic-plus-plateau model (YLD-QP), defined by…”

Section: Intermediate Models Yield As a Function Of N Uptakementioning

confidence: 99%

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Models of Yield, Grain Protein, and Residual Mineral Nitrogen Responses to Applied Nitrogen for Winter Wheat

Makowski¹,

Wallach²,

Meynard

1999

Agronomy Journal

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The proper use of N fertilizer is fundamental for farm profitability and environmental protection. In France, fertilizer recommendations are generally based on the balance-sheet method, which is not sensitive to the economic or regulatory context. A way to generalize the balance-sheet method is to use models that describe crop response to applied N. The goal of this paper is to propose, in the case of winter wheat (Triticum aestivum L.), functional forms for the response of yield, grain protein content, and residual mineral N left in the soil at harvest to applied N. The originality of our models is that all three responses (yield, protein, and residual mineral N) are modeled, using N uptake as an intermediate variable. Furthermore, these models have two clear links with the balance-sheet method. First, the parameters of the balance-sheet method appear explicitly in the models. Second, the N dose recommended by the balance-sheet method appears in our models as the smallest N dose that maximizes yield. Within this general modeling framework, three variants of the yield model, three variants of the grain protein content model, and two variants of the residual mineral N model are considered. The different models are adjusted, site-year by site-year, to 28 site-years of data. All models give satisfactory fits to the data. For example, the root mean square error varies between 0.21 and 0.23 Mg ha-• for the yield models. Almost all the adjusted parameter values vary widely from site-year to site-year. This must be taken into account when calculating optimal N doses for new site-years. P ROPER MANAGEMENT of N fertilizers for winter wheat (Triticum aestivum L.) is important for ensuring profitability and because of environmental concerns. Profitability can depend on both the quantity of yield and the grain protein content, and both of these vary with applied N. The risk of water pollution by nitrates will depend in large part on the residual mineral N left in the soil at harvest, and the amount of residual N depends on the amount of applied N. Accurate recommendations of N fertilizer doses for wheat may thus require information concerning projected yield, protein content, and residual mineral N response to applied N. Current fertilizer recommendations for winter wheat in France are mostly based on the balance-sheet method (Stanford, 1973;Remy et Hebert, 1977;Neeteson, 1990; Meynard eta!., 1997). With this method, a balance sheet is drawn up in which the N requirement of the crop is given on one side and the contribution of soil mineral

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Section: Intermediate Models Yield As a Function Of N Uptakementioning

confidence: 99%

“…Both terms are estimated from soil and crop characteristics and fertilizer N is then calculated to supply the shortfall. According to Meynard et a!. (1981Meynard et a!.…”

mentioning

confidence: 99%

Models of Yield, Grain Protein, and Residual Mineral Nitrogen Responses to Applied Nitrogen for Winter Wheat

Makowski¹,

Wallach²,

Meynard

1999

Agronomy Journal

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“…Nutrient solutions (see Moreau et al , for the detailed composition) were provided by automatic watering of pots several times a day at a frequency that allowed the pots to drain freely, thereby avoiding the accumulation of ions in the substrate. We recorded daily watering in Experiment 2 (without considering draining) to draw a parallel with the orders of magnitude of amounts of nitrogen in field conditions (Meynard et al , ). During the experiment, each pot received, on average, the equivalent of approximately 22–27 kg ha −1 at low soil‐nitrogen availability (i.e.…”

Section: Methodsmentioning

confidence: 99%

Effects of species and soil‐nitrogen availability on root system architecture traits – study on a set of weed and crop species

Moreau

Abiven

Busset

et al. 2017

Annals of Applied Biology

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Better managing crop : weed competition in cropping systems while reducing both nitrogen and herbicide inputs is a real challenge that requires a better understanding of crop and weed root architecture in relation to soil-nitrogen availability. An original approach was used which considered the parameters of a simulation model of root architecture as traits to analyse (a) the interspecific diversity of root system architecture, and (b) its response to soil-nitrogen availability. Two greenhouse experiments were conducted using three crop and nine weed species grown at two contrasted concentrations of soil-nitrogen availability. Plant traits were measured to characterise both overall plant growth and root architecture, with a focus on primary root emergence, root elongation and branching. The studied root traits varied among species (from a twofold to a fourfold factor, depending on the trait), validating their use as indicators to analyse the interspecific variability of root architecture. The largest interspecies differences were for two traits: 'maximal apical root diameter' and 'interbranch distance' (distance between two successive laterals on the same root). Conversely, most of the studied root traits varied little with soil-nitrogen availability (from no variation to a 1.1-fold factor, depending on the trait) even though soil-nitrogen availability varied with a 17-fold factor and impacted the overall shoot and root biomass. So, the root traits used in this article are stable whatever soil-nitrogen availability. As they reflect processes underlying root system architecture, this low effect of nitrogen suggests that the rules governing root architecture are little affected by plant nitrogen status and soil-nitrogen availability. We propose that the determinants of differences in root system architecture between soils with contrasted nitrogen availability mainly originate from differences in the amount of carbon allocated to and within the root system. Characterising each plant species by a combination of root traits gave insights regarding the potential species competitive ability for soil resources in agroecosystems.

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“…The "Champagne Céréales" cooperative members tend to represent the "big" cereal growers of the region, whereas the Agricultural Board is more concerned with mixed croplivestock farmers, whose specialisation towards cash crops has not begun or is just beginning; -they may have received different training: "Champagne Céréales" advisors may have been taught about the early experiments in the Champagne crayeuse of the Marne department. For example, in the late seventies, INRA did research on technical sequences on winter wheat in the "Champagne crayeuse" small agricultural district [7,19]. These studies may have been more rapidly known by the "Champagne Céréales" advisors, as the cooperative used to working in the Marne and Meuse departments, than by the advisors from the Meuse Agricultural Board;…”

Section: Consequences Of the Choice Of The Experts In The Survey Designmentioning

confidence: 99%

Spatial dynamics of agricultural practices on a basin territory: a retrospective study to implement models simulating nitrate flow. The case of the Seine basin

Mignolet¹,

Schott²,

Benoît³

2004

Agronomie

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-Within an interdisciplinary research programme which has aimed at modelling the nitrate flow evolution in the whole Seine basin since the seventies, we propose a methodological process in order to reconstitute and spatialise cropping systems' dynamics, whose characteristics are used to implement the STICS agronomic model. This process makes use of both expert opinions and departmental or national agricultural statistics that are compared, step by step, in order to build the most reliable database in relation to the time and space scales considered. Data mining and statistical cartography methods are, respectively used to model the crop sequences' temporal evolution and to spatialise them over the spatial pattern of the agricultural districts. The results show an important spatial and temporal differentiation of the cropping systems, both in the cropping sequences developed and in the cultivation techniques employed. Crossing several information sources allows the identification of historical trends, but with a decreasing precision as we go back in time.cropping system / statistical cartography / data mining / inquiry / agricultural statistics

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Elaboration du rendement et fertilisation azotée du blé d'hiver en Champagne crayeuse II. - Types de réponse à la fumure azotée et application de la méthode du bilan prévisionnel

Cited by 29 publications

References 0 publications

Models of Yield, Grain Protein, and Residual Mineral Nitrogen Responses to Applied Nitrogen for Winter Wheat

Models of Yield, Grain Protein, and Residual Mineral Nitrogen Responses to Applied Nitrogen for Winter Wheat

Effects of species and soil‐nitrogen availability on root system architecture traits – study on a set of weed and crop species

Spatial dynamics of agricultural practices on a basin territory: a retrospective study to implement models simulating nitrate flow. The case of the Seine basin

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