Use of a new GIS nitrogen index assessment tool for evaluation of nitrate leaching across a Mediterranean region
Introduction
Nitrogen (N) inputs are necessary to maintain viable economical agricultural production across worldwide agroecosystems. Although N inputs are necessary to maintain viable agroecosystems, higher than needed N inputs are reported to increase losses of N through the nitrogen cycle, impacting groundwater, air, and soil quality (Antweiler et al., 1995, Gruber and Galloway, 2008, Juergens-Gschwind, 1989, Kronvang et al., 2001, Wriedt et al., 2007). One of the key principles for minimizing nitrate–nitrogen (NO3–N) leaching is to manage N applications with a mass balance approach (Meisinger and Delgado, 2002), however the quantification and assessment of N lost via surface, leaching, and atmospheric pathways is difficult (Delgado, 2002). Computer models provide a quick way to evaluate the effect of best management practices (BMPs) across regions, integrating layers of information and site-specific variability to determine what can be done to protect water resources (Almasri and Kaluarachchi, 2004, Almasri and Kaluarachchi, 2007, Delgado, 2001, Jordan and Smith, 2005, Morari et al., 2004, Peralta and Stockle, 2001, de Paz and Ramos, 2002, de Paz and Ramos, 2004).
There is a demand for the development, calibration, and validation of fast-working tools that can assess N losses to the environment, specifically NO3–N leaching loss impacts on groundwater in Europe. The European Commission established a series of guidelines and directives to protect water from pollution created by NO3–N linked to agricultural sources (CEC, 1991). In Spain, each regional government has to follow this EU directive. The Valencia region (Generalitat Valenciana, 2000a) declared most of the irrigated Mediterranean region of Valencia a nitrogen vulnerable zone (NVZ) and recommended the application of a new set of BMPs to preserve water quality. However, traditional farm practices are still more widely used than the recommended BMPs across the NVZ. The NO3–N concentrations in groundwater are still high in several Valencian aquifers (Rubio et al., 2006). Traditional farming practices apply high N fertilizer rates to the large vegetable and citrus areas across this Mediterranean region of Spain. There is currently a need for scientists to develop, calibrate, and validate a quick GIS NIT-1 tool for quick assessment of N management practices across this region.
Applying complex models across large areas with scarce data may not be the best approach because of the large uncertainty introduced (Beven, 1989). Simpler models may offer faster solutions for assessing N management practices, because of their less complicated input requirements and/or structure. A tier one level tool will provide a rapid qualitative/quantitative screening to separate the N management practices or situations with very low to medium N loss risks from those with high to very high N loss risk (Shaffer and Delgado, 2002, Delgado et al., 2006, Delgado et al., 2008). A tier two tool would be used to assess the effect of management practices on a daily basis using a more complex level of N dynamic computation within application models. A tier three approach can be used to assess a detailed research problem, using a computer simulation model with field study data to quantify and assess the effects of management on N losses.
Shaffer and Delgado (2002) discussed in detail the advantages and disadvantages of previous N indices. Delgado et al., 2006, Delgado et al., 2008 called the NIT-1 approach “new” because of three modifications to previous approaches: (1) expanded and combined information (see Delgado et al., 2006, Delgado et al., 2008 for tables with new information), (2) the ability for international input, and (3) the ease of use while connecting to P-indices and N simulation models. Delgado et al., 2006, Delgado et al., 2008 N index was different from previous N indices in that it used a qualitative rating system for N leaching, surface transport of N and air quality impacts, linked to N losses. Among the input variables affecting output in the NIT-1 were the use of vegetative buffers, proximity of nearest field edge to named stream or lake, soil erosion, ammonia (NH3–N) volatilization, denitrification, rooting depths, aquifer leaching potential risk, and other combinations of factors that were not used in any previous N indices.
Although the P Index was not considered in the writing of this paper, the GIS NIT-1 represents an innovation in nutrient management science because it has an expanded range of information sources considered in the generation of qualitative rankings of N losses linked to leaching and atmospheric pathway losses across the landscape. Many of these information sources were not considered by previous N indices. For additional information about the new NIT-1 approach and other advantages and disadvantages of previous N indices, see Shaffer and Delgado, 2002, Delgado et al., 2006, and Delgado et al. (2008).
Our hypothesis was that a GIS NIT-1 can be used to separate management practices with potential for very low to moderate N losses from those with potential for high to very high N losses to the environment. Additionally, a second hypothesis was that the GIS NIT-1 can correlate quantitative/qualitative NO3–N leaching outputs across the landscape with underground water NO3–N concentrations. A third hypothesis was that the GIS NIT-1 can be used to evaluate onions, orange, artichoke, and cauliflower systems among other cropping systems. No other previous N indices were able to assess spatial and temporal N losses to the environment with quantitative and qualitative rankings.
Section snippets
Study area
The study area is located on the Mediterranean coast of Spain (Fig. 1), where the climate is semiarid to arid, with a hot, dry summer and a mild, rainy autumn. There is significant variability in soils, ranging from sandy soils near the Mediterranean shoreline to clay soils in the alluvial lands. The average farm size is 0.5 ha. Traditional farming practices include intensive applications of agrochemicals and irrigation with average application rates of 500 kg N ha−1 y−1 for vegetables (Ramos et
Calibration–validation
We observed a close agreement between measured and simulated N uptake with an RSME of 20 kg N ha−1 (Fig. 4, P < 0.05). Since there was no significant difference between the calibration and validation points, we presented the data in Fig. 4. Onions, orange, artichoke, and cauliflower were among the simulated cropping systems (P < 0.001).
The results of the calibration for the S parameter are shown in Fig. 2. We used the observed drainage data to fit the S values and arrived at a good correlation. Since
Conclusions
This paper presents a detailed case study on the application of a spatially explicit N index for quantitatively and qualitatively assessing N losses to the environment using a large data set from agricultural production across the Valencian region. The study presents a valuable extension of an existing approach to process other crop data and to improve the spatial resolution of calculated N losses due to leaching and emissions by implementing the GIS NIT-1 approach in a GIS environment. This
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Dr. De Paz was a Visiting Scientist at the USDA-ARS, Soil Plant Nutrient Research Unit and Faculty Affiliate with Colorado State University, Fort Collins, CO, USA.
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Tel.: +34 963424000; fax: +34 963424001.