B.J. van Alphen

Tools for optimizing management of spatially-variable fields

Abstract Efficient use of agro-chemicals is beneficial for farmers as well as for the environment. Spatial and temporal optimization of farm management will increase productivity or reduce the amount of agro-chemicals. This type of management is referred to as Precision Agriculture. Traditional management implicitly considers any field to be a homogeneous unit for management: fertilization, tillage and crop protection measures, for example, are not varied within a single field. The question for management is what to do when . Because of the variability within the field, this implies inefficient use of resources. Precision agriculture defines different management practices to be applied within single, variable fields, potentially reducing costs and limiting adverse environmental side effects. The question is not only what and when but also where . Many tools for management and analysis of spatial variable fields have been developed. In this paper, tools for managing spatial variability are demonstrated in combination with tools to optimize management in environmental and economic terms. The tools are illustrated on five case studies ranging from (1) a low technology approach using participatory mapping to derive fertilizer recommendations for resource-poor farmers in Embu, Kenya, (2) an example of backward modelling to analyze fertilizer applications and restrict nitrogen losses to the groundwater in the Wieringermeer in The Netherlands, (3) a low-tech approach of precision agriculture, developed for a banana plantation in Costa Rica to achieve higher input use efficiency and insight in spatial and temporal variation, (4) a high-tech, forward modelling approach to derive fertilizer recommendations for management units in Zuidland in The Netherlands, and (5) a high-tech, backward modelling approach to detect the relative effects of several stress factors on soybean yield.

A methodology for precision nitrogen fertilization in high-input farming systems.

Nitrogen (N) emissions to ground and surface waters have become a major concern in many regions. In reaction, policy makers are tightening environmental constraints on agriculture, resulting in a call for more efficient management systems. This study presents a methodology for precision N fertilization in high-input farming systems applying split fertilizer strategies. Essentially, the method uses a mechanistic simulation model to quantify (i) soil mineral-N levels and (ii) N uptake rates on a real-time basis. Early warning signals are generated once N concentrations drop below a critical threshold level, indicating that additional fertilizer should be applied. Thresholds are not static, but defined in relation to actual uptake rates. Spatial variation is incorporated through the concept of management units: i.e., stable units with relatively homogeneous characteristics in terms of water regimes and nutrient dynamics. Separate simulations are conducted for each management unit, based on selected representative soil profiles. The proposed methodology was tested in a winter wheat (Triticum aestivum L.) field during the 1998 growing season. Six experimental strips were delineated receiving either ‘precise’ or traditional fertilization. Precision fertilization proved efficient in reducing fertilizer inputs (−23%), while slightly improving grain yields (+3%) and hectoliter weights (+4%). Results clearly illustrate the significance of precision management in the process of increasing fertilizer use efficiency.

Combining pedotransfer functions with physical measurements to improve the estimation of soil hydraulic properties

Simulation modelling is an important tool in evaluating the economical and environmental effects of different farm management practices. Availability and quality of input data are crucial factors that determine the accuracy of modelling results. With respect to soil water regimes, much depends on the accuracy of soil hydraulic properties. These are conventionally derived through expensive laboratory measurements. Pedotransfer functions have been developed to increase cost effectiveness: hydraulic properties are estimated using basic soil properties that can be measured with relative ease. This study compares four methods to derive hydraulic properties by analysing their effect on simulated soil moisture contents. Applied methods include: (A) laboratory measurements, (B) class pedotransfer functions, (C) continuous pedotransfer functions and (D) continuous pedotransfer functions combined with simple laboratory measurements. Modelling performance was evaluated by comparing simulated and measured soil moisture contents for three sites and two depths. Modelling uncertainty was evaluated through Monte Carlo simulations. The combination of continuous pedotransfer functions and simple laboratory measurements (method D) clearly produced best results. Modelling performance was highest overall and results were consistent for individual profiles and depths. Modelling uncertainty was lowest, far lower than the uncertainty resulting from the measured data set (method A). Based on these results, a general procedure was defined to combine continuous pedotransfer functions with simple soil physical measurements.

A case study on precision nitrogen management in Dutch arable farming

Throughout Europe new environmental laws are being implemented to limitnitrogen (N) fertilization on arable land. This is particularly relevant in TheNetherlands where arable farms rank among the most intensively managed in theworld. An efficient use of inputs is therefore crucial. Precision agricultureaims at increasing this efficiency by incorporating spatial and temporalvariation into fertilizer management. A method developed for this purpose isevaluated, based on two fertilizer experiments conducted in consecutive years(1998–1999) on different winter wheat fields (Triticumaestivum L.). A simulation model and real-time weather data were usedto monitor soil mineral N levels in the experimental fields. Spatial variationwas incorporated through management units, which were defined in terms of waterregimes and N dynamics. Early warning was provided when soil mineral Nconcentrations dropped below a critical threshold. Used as atrigger, this information served to optimize the timing offour consecutive N fertilizations. Fertilizer rates were determined throughexploratory simulations, which calculated the amount of mineral N required undernormal conditions. Compared to conventional management,fertilizer input was reduced by 15–27% without affecting grainyield. Grain quality was either not affected (1999) or significantly increased(1998; P < 0.01). Soil mineral N residues measured afterharvest were consistently lower under precision management. This is importantsince leaching of nitrates mainly occurs during winter when a precipitationsurplus is present. Results provide an illustration of efficiency gains attainedthrough precision N management. They also underline the relevance of managingtemporal variation (along with spatial variation) on farms applying intensiveand dynamic management strategies.Throughout Europe new environmental laws are being implemented to limitnitrogen (N) fertilization on arable land. This is particularly relevant in TheNetherlands where arable farms rank among the most intensively managed in theworld. An efficient use of inputs is therefore crucial. Precision agricultureaims at increasing this efficiency by incorporating spatial and temporalvariation into fertilizer management. A method developed for this purpose isevaluated, based on two fertilizer experiments conducted in consecutive years(1998–1999) on different winter wheat fields (Triticumaestivum L.). A simulation model and real-time weather data were usedto monitor soil mineral N levels in the experimental fields. Spatial variationwas incorporated through management units, which were defined in terms of waterregimes and N dynamics. Early warning was provided when soil mineral Nconcentrations dropped below a critical threshold. Used as atrigger, this information served to optimize the timing offour consecutive N fertilizations. Fertilizer rates were determined throughexploratory simulations, which calculated the amount of mineral N required undernormal conditions. Compared to conventional management,fertilizer input was reduced by 15–27% without affecting grainyield. Grain quality was either not affected (1999) or significantly increased(1998; P < 0.01). Soil mineral N residues measured afterharvest were consistently lower under precision management. This is importantsince leaching of nitrates mainly occurs during winter when a precipitationsurplus is present. Results provide an illustration of efficiency gains attainedthrough precision N management. They also underline the relevance of managingtemporal variation (along with spatial variation) on farms applying intensiveand dynamic management strategies.