Deriving Fertilizer Recommendations with a Flexible Functional Form

Saín, Gustavo; Jauregui, Mario A.

doi:10.2134/agronj1993.00021962008500040027x

Cited by 15 publications

(8 citation statements)

References 3 publications

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“…Field experiments showed a positive effect of soya bean as a previous crop of wheat in relation to corn or other summer crops in the Rolling Pampa, but this effect may be compensated by small rates (5–10 kg N ha −1 ) of fertilizer (Sain and Jauregui 1993). This supports the hypothesis that soya bean may generate an N credit for wheat.…”

Section: Discussionmentioning

confidence: 99%

Analysis of Soil Fertility and Management Effects on Yields of Wheat and Corn in the Rolling Pampa of Argentina

Álvarez¹,

Grigera²

2005

J Agronomy Crop Science

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The Rolling Pampa is the most productive region of the Argentine Humid Pampa comprising around 10 Mha. Wheat (Triticum aestivum L.), corn (Zea mays L.), and soya bean [Glycine max (L.) Merr.] are the main grain crops produced. To develop sound cropping strategies, a better understanding of the impact of soil fertility and management on crops is needed. The objective of this study was to develop models for estimating the effects of growing season precipitation, soil fertility and management on wheat and corn yields. Data from 347 wheat and 323 corn field experiments and production fields over six growing seasons were used. Soil, management and weather characteristics were determined and yields were then evaluated. Data were analysed using linear and quadratic models and a quadratic polynomial surface model. Soil fertility, management and rainfall and interactions were analysed. Growing season precipitation correlated with wheat (R2 = 0.42) and corn (R2 = 0.25) yield. Maximum wheat yield was achieved with 350–400 mm rainfall and corn yield reached a plateau around 700 mm. Soil fertility accounted for 33 % of wheat yield variability and 5 % of corn yield variability. Management accounted for 48 and 9 % respectively. Whole polynomial models integrating rainfall, fertilizer N and P rates, soil N and P, previous crop and tillage system accounted for 67 % of wheat yield variability and 51 % of corn yield variability. Soil organic matter was not included in the models but an indirect effect on yield was detected as organic matter correlated with initial soil N levels for both crops. Soya bean as a previous crop had a positive effect on wheat and corn yields. Wheat was insensitive to tillage system but corn yield was higher under no till. N and P fertilization had a two‐ to three‐fold greater impact on yield than soil nutrient levels. As this region is considered to be of high soil fertility and has a history of very low fertilizer consumption, adequate use of N and P fertilization will be essential to maintaining high wheat and corn yields.

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Section: Discussionmentioning

confidence: 99%

Analysis of Soil Fertility and Management Effects on Yields of Wheat and Corn in the Rolling Pampa of Argentina

Álvarez¹,

Grigera²

2005

J Agronomy Crop Science

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“…Parameter estimation in N response models is generally based on ordinary least squares (e.g., Mombiela et al, 1981; Sain and Jauregui, 1993). This method is easily implemented.…”

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confidence: 99%

“…To select a particular model for practical use, it is necessary to define criteria for evaluating the different possible models. In numerous studies, response models are evaluated by calculating R 2 values (Anderson and Nelson, 1975; Cerrato and Blackmer, 1990; Sain and Jauregui, 1993). Another approach is to study graphically the distribution of the model residuals (Cerrato and Blackmer, 1990; Bullock and Bullock, 1994).…”

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Statistical Methods for Predicting Responses to Applied Nitrogen and Calculating Optimal Nitrogen Rates

Makowski¹,

Wallach²,

Meynard

2001

Agronomy Journal

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plied to a set of winter wheat experiments in the Paris Basin of France. The proposed methods are quite gen-Models of response to applied N can be useful for deriving imeral and can be used with different response functions. proved N dose recommendations. Here we show how response model parameters can be estimated and how model predictions and modelParameter estimation in N response models is gener-N dose recommendations can be evaluated. For parameter estimation, ally based on ordinary least squares (e.g., Mombiela et we use a statistical approach based on random parameter models. al., 1981; Sain and Jauregui, 1993). This method is easily Two methods for evaluating models are applied. The first method implemented. However, the underlying assumption that is to calculate mean squared error of prediction (MSEP) by cross model errors are independent is unrealistic for data like validation, and the second is to perform nonparametric regressions to N response data where several measurements (correevaluate the profitability of calculated optimal N rates. The proposed sponding to different N doses) are made for the same methods are used with a data set consisting of 37 winter wheat (Tritisite-year. In this paper, we propose an alternative cum aestivum L.) experiments. Different functions taking into account method for parameter estimation. Our approach conend-of-winter mineral soil N are evaluated. The results show that the sists of using a statistical model called a random paramedifferent functions all have similar MSEP values for predictions of yield and grain protein content and lead to N recommendations of ter model, which is a specific type of mixed-effects similar profitability. However, there are substantial differences in model. A random parameter model assumes that the MSEP for residual mineral N at harvest. One of these models is form of the modeled response is common to all sitethen compared with a model that does not include any site-year years but that parameter values vary between site-years. characteristic and with a model that does not have random parameters.The parameters are thus treated as having some random We find that using the model without a site-year characteristic leads variation around their mean values. This approach is to predictions that are less accurate and optimal N rates that are less consistent with the data that are usually observed, and profitable by F 17 to F 105 ha Ϫ1 . Another result is that the gross it automatically introduces correlations among errors margin obtained with the optimal N rates calculated using the model for the same site-year. This method was applied by Walwithout random parameters is lower by F 438 to F 550 ha Ϫ1 . lach (1995a, 1995b) to a simple quadratic (Q) model for yield as a function of applied fertilizer.We show in this paper how random parameter models D. Makowski and D. Wallach, unité d'agronomie, INRA, B.P. 27, however, the goal of developing the models is either to 31326 Castanet-Tolosan Cedex, France; and J.-M. Meynard, Laboratoire d'agronomie, INRA...

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“…In fact, it is often observed that several models give similarly good fits to the data. Some authors have pro-posed models that relate response to measurable plot characteristics, such as the preceding crop or soil mineral N before fertilizer application (Smith et a!., 1987;Sain and Jauregui, 1993). Much less work has been done on modeling grain protein content or residual mineral N at harvest (Jauregui and Paris, 1985;Fowler eta!., 1989).…”

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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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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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Deriving Fertilizer Recommendations with a Flexible Functional Form

Cited by 15 publications

References 3 publications

Analysis of Soil Fertility and Management Effects on Yields of Wheat and Corn in the Rolling Pampa of Argentina

Analysis of Soil Fertility and Management Effects on Yields of Wheat and Corn in the Rolling Pampa of Argentina

Statistical Methods for Predicting Responses to Applied Nitrogen and Calculating Optimal Nitrogen Rates

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

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