Research PaperEnvironmental and economic evaluation of N fertilizer rates in a maize crop in Italy: A spatial and temporal analysis using crop models
Highlights
► The best amount of N fertilizer to apply on a field varies spatially and temporally. ► A crop simulation model was used to select optimal N rate over space and time. ► Net marginal return increased using the N rate chosen by model. ► N leaching and N2O emission were reduced with the N rate selected by the model.
Introduction
Increasing maize yield has been one of the main challenges for agronomists and researchers worldwide for the last 50 years (e.g. Egli, 2008, Hafner, 2003). In areas where Nitrogen (N) fertilizer is affordable or subsidized, there is an increased probability that farmers will apply in large quantities, potentially imposing a high environmental impact, including nitrate leaching (Basso and Ritchie, 2005, Giola et al., 2012, Martin et al., 2006, Syswerda et al., 2012), ammonia volatilization, nitrous oxide (N2O) emissions and soil acidification (Chen et al., 2008, Grace et al., 2011, Spiertz, 2010). The environmental impact that agriculture exerts is gaining more attention from society. For example, the European Union (EU) Nitrates Directive (91/676/EEC) aims to preserve the quality of groundwater through promotion of good farming practises to increase N use efficiency through a reduction in direct the application of N.
Determining the optimum amount of N fertilizer to meet plant needs while simultaneously minimizing environmental impacts is not an easy task (Robertson & Vitousek, 2009). The optimum N fertilizer rate varies within the same field with each growing season as a result of the heterogeneity of soil properties (which in turn affects soil water content) and inter- and intra-annual weather patterns (Basso, Bertocco, Sartori, & Martin, 2007). In most cases, farmers apply N fertilizer without considering the within field variation of soil properties. The concept of ‘precision farming’ was introduced in the early 1990s, with yield monitors being the most important technological tool for a successful application of precision farming (Pierce & Sadler, 1997). Since then, much research has been conducted in the search for site-specific and optimized application rates for several input resources, such as fertilizers and pesticides.
Some research has examined and found reasonable consistency between variable rate N fertilizer applications and the factors affecting nitrogen variability. These factors that have shown to have high influence are elevation, apparent electrical conductivity (ECa), and soil texture (Fraisse et al., 2001, Godwin and Miller, 2003, Kyveryga et al., 2011, Ruffo et al., 2005, Welsh et al., 2003). Walter, Bausch, and Brodahl (2012) recommended using ECa maps as a method of obtaining reference strip normalizing values in fields with spatially variable sandy soils.
Delin, Linden, and Berglund (2005) reported that the potential for improvement of yield or nitrogen efficiency by site-specific nitrogen fertilization is only relevant if the causes of within-field variation are predictable before fertilization. Approaches to derive uniform management zones have been described by Mulla, 1991, Schepers et al., 2004, Chang et al., 2004, Miao et al., 2006, Basso et al., 2007, and Basso, Ritchie, Cammarano, and Sartori (2011). Specific studies by Lawes and Roberston (2011) found that information needed for precision fertilizer management may neither be feasible nor easy to interpret. However, the complete decision making process on the application of N at the farm level must consider the specifics of information necessary to aid in decision making, including the cost–benefit of acquiring this information. The decision making process on N-application therefore becomes an integral and sustained element of the farm management information system as demonstrated by numerous studies (Basso et al., 2003, Doole et al., 2009, Fountas et al., 2006, Janssen and van Ittersum, 2007, Lawes and Roberston, 2011, Sørensen et al., 2010). An improved understanding of the factors affecting the determination of the optimal N-application in terms of both external influences (e.g. cost of fertilizers, chemicals, fuels, etc.) as well as the on-farm and in-field influences will assist farmers in achieving higher yields at lower costs.
The complexity of decision making is illustrated by the fact that even if farmers have a spatial map of soil properties, the decision about the amount of N fertilizer to apply on the field is taken without any prior knowledge of future weather conditions. A feasible approach to cope with such uncertain future information is to quantify the uncertainty under different scenarios as part of a predictive decision support system (Basso et al., 2011, Fountas et al., 2006).
Crop simulation models can quantify the interaction between multiple stresses and crop growth under different environmental and management conditions (Basso, Ritchie, Pierce, Jones, & Braga, 2001; Basso et al., 2011, Batchelor et al., 2002, Schnebelen et al., 2004). Using long-term historical weather data, the models can be used to develop alternative management strategies for optimizing productivity and maximizing profit as well as capturing the diversity of environments that can be encountered at a given farm. Crop simulation models are rarely used in precision farming because of the costs of obtaining detailed site-specific field attributes, as inputs are prohibitive, except in few case (Basso et al., 2001, Basso et al., 2011; Basso, Fiorentino, Cammarano, Cafiero, & Dardanelli, 2012; Batchelor et al., 2002, Booltink et al., 2001, Cora et al., 1999, Link et al., 2008, Miao et al., 2006; Wong & Asseng, 2006). Models can help farmers in a strategic way assessing the probability that a certain outcome will occur for those measured pedo-climatic condition and management practises. Models have also being shown to be useful in a tactical management of N fertilizer rate associated with more easily observed variables (i.e. water availability based on rainfall amounts). Basso, Ritchie, et al. (2011) demonstrated that N fertilizer amount needs to be different over space and time depending on the amount of water stored in the soil profile. In the past, best management of N fertilizer and irrigation recommendations have mainly aimed at increasing crop yield giving the environmental impact (potential for NO3 leaching and N2O emissions) a lower priority. In order to obtain information on nitrate leaching data over a long period of time, long-term experiments need to be implemented. This kind of information is normally not available due to the prohibitive cost of the long-term experiment and nitrate leaching collection protocol. A much more practical way of obtaining these data is through the use of crop simulation models. Several models are able to predict nitrate leaching potential under different fertilization strategies. Asseng et al. (1998) used the APSIM model to predict the leaching potential under different initial soil water and inorganic soil N showing that the soil water and the soil inorganic N content at the beginning of each season had no effect on grain yield, implying that pre-seed soil NO3 was mainly lost from the soil by leaching.
The objective of this study was to simulate the environmental and economic impact (nitrate leaching and N2O emissions) of spatially variable N fertilizer applications in an irrigated maize field in Italy using a validated crop model. The research aims at demonstrating the importance of using crop models for selecting best N management strategies from the economic and environmental perspective over space and time and in places where previous knowledge or long-term experiments are not present to reduce the risks the farmers face when selection N fertilizer strategies.
Section snippets
Site description
The study was carried out on an 8-ha field with a near zero slope, located close to Rovigo (44° 4′ 12″ N, 11° 47′ 22″ E, 6 m.a.s.l.) NE Italy grown with continuous maize for 5 years (1998–2002). The soil type was clay according to the USDA particle-size distribution limits, defined as FAO Ombric and Thionic Histosols. The climate of the area was characterized by an average annual rainfall of 700 mm (in years 1997–2008), distributed mostly in autumn and spring. The annual average temperature, for
Results
The differences between simulated and measured maize yield for the six years of study are shown in Fig. 1. There is a high correlation between simulated and measured yield demonstrating the consistency of the model used (y = 1.02x − 493; r2 = 0.92; RMSE = 398.7 kg ha−1). The long-term simulation of yield at different N rates showed significant differences in yields as a function of the quantity of N applied to the crop (Table 1). An application rate of 50 N showed lower yield with values ranging from 6.1
Discussions
This research presented a modelling approach for selecting the most sustainable N fertilizer rate on two spatially and temporally stable zones of a maize field. The alternative to this approach, which accounts for the interaction between soil, plant, climate and management for 25 years is a long-term study, where despite the enormous value in having such an experiment, the cost for running and maintain are prohibitive. By running a validated model with a long-term weather data recorded from a
Conclusions
The potential of using a crop model as a decision tool for improving farmer’s economic return by maximizing yield or reducing input while protecting the environment was demonstrated. The crop growth simulation model allowed for deriving valuable information needed to decide on the optimal N rate to apply on spatially and temporally stable zones of a field. The long-term simulation of maize yield and N leaching showed the variability of yield response at different N rates. Derived differences
Acknowledgements
This research was partially funded by Veneto Agricoltura and the MIUR-PRIN 2008 project (Prof. Bruno Basso coordinator).
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