Advertisement
Browse Subject Areas
?

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

Tradeoffs between Maize Silage Yield and Nitrate Leaching in a Mediterranean Nitrate-Vulnerable Zone under Current and Projected Climate Scenarios

  • Bruno Basso ,

    basso@msu.edu

    Affiliation Department of Geological Sciences and W.K. Kellogg Biological Station, Michigan State University, East Lansing, MI, United States of America

  • Pietro Giola,

    Affiliation Sezione di Scienze Agronomiche, Coltivazioni Erbacee e Genetica (SACEG), Dipartimento di Agraria, Università di Sassari, Sassari, Italy

  • Benjamin Dumont,

    Affiliation Department of Geological Sciences and W.K. Kellogg Biological Station, Michigan State University, East Lansing, MI, United States of America

  • Massimiliano De Antoni Migliorati,

    Affiliation Institute of Future Environment, Queensland University of Technology, Brisbane, QLD, Australia

  • Davide Cammarano,

    Affiliation James Hutton Institute, Invergowrie, Dundee, DD2 5DA, Scotland, United Kingdom

  • Giovanni Pruneddu,

    Affiliation Sezione di Scienze Agronomiche, Coltivazioni Erbacee e Genetica (SACEG), Dipartimento di Agraria, Università di Sassari, Sassari, Italy

  • Francesco Giunta

    Affiliation Sezione di Scienze Agronomiche, Coltivazioni Erbacee e Genetica (SACEG), Dipartimento di Agraria, Università di Sassari, Sassari, Italy

Tradeoffs between Maize Silage Yield and Nitrate Leaching in a Mediterranean Nitrate-Vulnerable Zone under Current and Projected Climate Scenarios

  • Bruno Basso, 
  • Pietro Giola, 
  • Benjamin Dumont, 
  • Massimiliano De Antoni Migliorati, 
  • Davide Cammarano, 
  • Giovanni Pruneddu, 
  • Francesco Giunta
PLOS
x

Abstract

Future climatic changes may have profound impacts on cropping systems and affect the agronomic and environmental sustainability of current N management practices. The objectives of this work were to i) evaluate the ability of the SALUS crop model to reproduce experimental crop yield and soil nitrate dynamics results under different N fertilizer treatments in a farmer’s field, ii) use the SALUS model to estimate the impacts of different N fertilizer treatments on NO3- leaching under future climate scenarios generated by twenty nine different global circulation models, and iii) identify the management system that best minimizes NO3- leaching and maximizes yield under projected future climate conditions. A field experiment (maize-triticale rotation) was conducted in a nitrate vulnerable zone on the west coast of Sardinia, Italy to evaluate N management strategies that include urea fertilization (NMIN), conventional fertilization with dairy slurry and urea (CONV), and no fertilization (N0). An ensemble of 29 global circulation models (GCM) was used to simulate different climate scenarios for two Representative Circulation Pathways (RCP6.0 and RCP8.5) and evaluate potential nitrate leaching and biomass production in this region over the next 50 years. Data collected from two growing seasons showed that the SALUS model adequately simulated both nitrate leaching and crop yield, with a relative error that ranged between 0.4% and 13%. Nitrate losses under RCP8.5 were lower than under RCP6.0 only for NMIN. Accordingly, levels of plant N uptake, N use efficiency and biomass production were higher under RCP8.5 than RCP6.0. Simulations under both RCP scenarios indicated that the NMIN treatment demonstrated both the highest biomass production and NO3- losses. The newly proposed best management practice (BMP), developed from crop N uptake data, was identified as the optimal N fertilizer management practice since it minimized NO3- leaching and maximized biomass production over the long term.

Introduction

Nitrate (NO3-) leaching from agricultural land is a pervasive problem in areas with intensive agricultural production [14]. Application of N fertilizer in agricultural fields, while necessary to achieve adequate levels of crop production and quality, is often associated with significant environmental impacts because it is difficult to match crop N demand with N supply [5]. In areas where farmers rely on manure or slurry as a source of N, application of excessive amounts of these organic wastes can result in significant loss of nutrients and lead to environmental degradation [6]. Groundwater contamination due to NO3- leaching has received particular consideration in European Union legislation because pollution of this valuable resource has significantly increased in areas where intensive agriculture is practiced [4]. Excessive N applications are both economically and environmentally costly [5] and lead to large N surpluses in the soil and/or N losses [68]. Under field conditions, N losses are mainly due to NO3- leaching to the groundwater, ammonia volatilization from the leaves of N-rich plants, and emission of nitrogen (N2) and nitrous oxide (N2O) to the atmosphere [918].

Animal manure, if properly managed, provides both physical and chemical benefits to a crop system. The effect of manure application on NO3- leaching has been quantified by many researchers with results that have been contradictory. For example, [6] reported that use of manure in maize-alfalfa rotations in the Midwest U.S. caused high levels of NO3- leaching when compared to inorganic N fertilization. This was attributed to high NO3- concentration flushes that occur in the spring after the soil thaws. However, [18] reported that application of slurry in a maize-oat rotation in central Portugal caused less NO3- leaching than mineral fertilization. Similar results were reported by [19] for a maize system in northern Italy, where organic fertilization reduced NO3- leaching between 20 to 50%. [20] also reported positive effects for manure compost on NO3- leaching. In contrast, [21] reported no significant differences in NO3- leaching between dairy slurry and mineral fertilizers in southern Chile.

[22] conducted a meta-analysis on 32 published studies and found a worldwide average of 22% of N fertilizer applied to wheat and 15% of N fertilizer applied to maize systems is lost as NO3- leaching. This suggests that it is crucial to identify regional best management strategies of agricultural N application to effectively reduce NO3- leaching losses.

When accurately verified, crop simulation models can be useful tools to evaluate the effects of various practices on crop N uptake, production, and environmental quality [2329]. Evaluation of a given crop model is an important first step in its application. Integration of field data with crop simulation models has been shown to be crucial to the development of a precise, long-term assessment of NO3- leaching in relation to various N fertilizer management strategies [3034].

Projections of climate data predict several changes in future climatic conditions which include increased atmospheric CO2 concentrations, increased air temperatures and altered rainfall patterns [35]. Such changes will affect crop development and therefore a crop’s ability to obtain N from the soil. For example, at the middle and higher latitudes of Europe, higher temperatures are expected to reduce the duration of the crop cycle and its N uptake capacity. In addition, the rate of carbon decomposition is predicted to increase which can result in accumulation of N in the soil and thereby increase NO3- leaching potential [36]. [8] reported that conventional management practices in this area often result in NO3- concentrations that range from 40 mg L-1 (just below the 50 mg L-1 maximum threshold) to as much as 120 mg L-1. The hypothesis of this research is that N management strategies that comply with the European legislation under current climatic conditions may not be capable of offsetting additional NO3- leaching predicted under future climatic conditions. The objectives of this work were to i) evaluate the ability of the SALUS crop model to reproduce experimental results for yield and soil N dynamics under various N fertilizer treatments, ii) estimate the impacts of various N treatments on NO3- leaching under predicted future climate conditions, and iii) identify the N management strategy that best demonstrates the ability to minimize NO3- leaching and maximize yield in a NVZ under predicted future Mediterranean climatic conditions.

Materials and Methods

Site description, field trials, and agronomic management

A field experiment was conducted during the 2010 and 2011 growing seasons on a commercial farm near Arborea (Latitude 39° 46' 26" N, Longitude 08° 34' 53" E, 7 m a.s.l.), on the west coast of Sardinia, Italy. Permission to carry out this study was granted by the owner of the farm. The field study did not involve endangered or protected species. Extensive reclamation has been done in this area since 1930 to improve the soil which has included addition of a sand layer to improve drainage properties. Over time, establishment of commercial dairy and grain farming operations in the area has led to a sharp increase in application of both organic and inorganic N to these soils. The region currently has about 35,600 dairy cattle raised in intensive systems. Additional details on the geographical and agronomic characteristics of the area are given in [8].

Three N fertilization treatments were evaluated in this study: nil N fertilization (N0), mineral N (urea) fertilization (NMIN), and organic (cattle slurry) plus mineral N (urea) (CONV), the conventional fertilization practice adopted by farmers in the area. Detailed information regarding the time of application and fertilizer rates for 2010 and 2011 are reported in Table 1. The 4-hectare experimental field was divided into three parts, one for each fertilization treatment. However, due to the irregular shape of the field trial, areas allocated to each treatment were not uniform and consisted of 1.76 ha, 2.00 ha and 0.24 ha for the NMIN, CONV, and N0 treatments, respectively.

thumbnail
Table 1. Fertilizers and organic amendments rates, N content and dates of application.

http://dx.doi.org/10.1371/journal.pone.0146360.t001

Pioneer hybrid maize cultivars (PR31A34, FAO class 700 in 2010 and PR32F73, FAO class 600 in 2011) were planted at seven plants m-2 with an inter-row spacing of 75 cm on June 13, 2010 and May 21, 2011. The field was tilled in both growing seasons with a chisel plow to a depth of 25 cm, and the seedbed was finalized with a rotary harrow to 10 cm. The total irrigation supplied using permanent sprinkler systems in 2010 was 4140 m3 ha-1, split into 11 applications. In 2011 the total amount applied in 15 applications was 5740 m3 ha-1. Weed control was accomplished both years of the study with Syngenta LUMAX (S-metolachlor 31.25% + Terbuthylazine 18.7% + Mesotrione 3.75%). Crops were harvested for silage on September 14, 2010 and September 8, 2011.

The “Agrano” triticale cultivar was planted October 4, 2010 with a row spacing of 15 cm and a seeding rate of 200 kg ha-1. The field was prepared by chisel-plowing to a depth of 30 cm and the seedbed was completed with a rotary harrow to a depth of 10 cm. To avoid water stress, triticale was irrigated with 300 m3 ha-1 applied by sprinklers and split into two applications, one in October and another in December 2010. Triticale was harvested for silage on May 10, 2011.

Climate data

Historical weather data of daily minimum and maximum temperature, solar radiation, and rainfall (1959–2013) were obtained from the nearby meteorological station located at the “Santa Lucia experimental farm” (Zeddiani, OR; latitude 39°56'03.11''N, longitude 8°41'13.41''E, 15 m a.s.l.) of the University of Sassari. Historical daily weather data were used as input for the crop simulation model to simulate crop growth with the different treatments.

Projections of future climate data were generated with DSSAT-Perturb software [37]. The altered weather data formats are compatible with the SALUS crop model used in this study. The software follows the IPCC Fifth Assessment Report and uses CMIP5 datasets with different emission scenarios. The data were processed by a pattern scaling method, and then were re-gridded to a common 720*360 grid (0.5°*0.5°) using a bilinear interpolation method. The software uses four different Representative Concentration Pathways (RCPs) which represent four greenhouse gas concentration trajectories as adopted by the IPCC Fifth Assessment Report. The four RCPs in DSSAT-Perturb were RCP2.6, RCP4.5, RCP6.0 and RCP8.5 as associated to a range of plausible radiative forcing values of 2.6, 4.5, 6.0, and 8.5 W m-2, respectively. The RCP6.0 and RCP8.5 scenarios were chosen for use in this study because they were identified as having the highest probability of occurrence given current emissions trends [37, 38] Simulated data from these two RCPs were compared with simulated data from a baseline scenario (BL) using local historic weather data from 1959 to 2013.

The Global Circulation Models (GCMs) data in the DSSAT-Perturb were obtained from the Earth System Grid (ESG) data portal for CMIP5. Twenty-nine GCM models were selected in order to capture the variability between GCMs (Table 2) and pattern scaling was used to process the data. This method is based on the assumption that a simple climate model will correctly characterize the global responses of a GCM (even for non-linear responses), and that a wide range of climatic variables in a given GCM are a linear function of its changes in global annual mean temperature at different spatial-temporal scales [39, 40]. More details on the methodology and the software used were reported on the CLIM systems manual (http://www.climsystems.com/dssat-perturb/).

thumbnail
Table 2. List of the CMIP5 GCMs used in this study as future projection climate data in SALUS model.

http://dx.doi.org/10.1371/journal.pone.0146360.t002

Field data collection

The study took place in a Nitrate-vulnerable zone (NVZ) according to the European Nitrate Directive 91/676 [41]. NVZ relates to both the high permeability of the area’s sandy soils which have little potential to retain N and the conventional local agricultural practice in which irrigation and N amendments (mainly manure, slurry and inorganic N fertilizers) are applied at high rates. This combination results in leaching of high levels of NO3- to the aquifer [8]. Initial chemical and physical characteristics of soils in the study area were determined from samples collected June 3, 2010, before fertilization or sowing. These values were used as initial inputs for the crop simulation model. Additional soil and crop samples were taken at various growth stages throughout the maize-triticale rotation to measure total soil nitrogen (N), organic carbon (OC), NO3- and ammonium (NH4+) concentrations, as well as total crop N content and biomass production. Soil and crop samples were collected at every sampling date from three replicates for each of the three treatments (N0, NMIN, CONV).

A total of eight soil depths were sampled at 10 cm increments for the first two layers (0–10, 10–20 cm) and at 20 cm increments for the other six layers to a depth of 140 cm (20–40, 40–60, 60–80, 80–100, 100–120, 120–140 cm). For each sampling point, all the plants growing along a one meter length were removed from the field and analyzed. Slurry samples were collected in April 2010 and 2011 with a NISKIN bottle (0.8 m height, 0.07 m dia.), which was specifically designed for sampling liquids at a given depth. Soil and slurry samples were stored in a deep freeze at −20°C until analysis.

Soil texture was determined using the modified pipette procedure for particle-size analysis [42, 43] and organic carbon was determined with the Walkley–Black method [44]. Total N was determined with the Kjeldahl method; NO3- was measured by extracting each sample with NaHCO3 0.01 M (weight/vol 1/20) and quantified with the Fox and Piekielek method [45]. Ammonium (NH4+) was determined by KCl 2M extraction and colorimetric quantification [46]. A pH meter (GLP 21, CRISON, 08328 Alella, Barcelona, Spain), calibrated with pH 4.0 and 7.0 buffer solutions, was used to analyze pH in water samples. Available soil P was determined using the Olsen et al. method [47], and K was determined using a BaCl2 and triethanolamine solution [48].

Crop simulation model

The SALUS model (System Approach to Land Use Sustainability [5,10,30,33,49,50]) used in this study has previously been calibrated and tested on field data collected in the same area of Sardinia at a different location [8]. This study further evaluates SALUS with maize yield and soil NO3- levels under a maize-triticale rotation.

SALUS, derived from the CERES models, was designed to simulate, in a continuous mode [33], the long-term impact of management, soil and climate, on crop yield and the environmental impact of cropping systems. SALUS represents an advancement of the CERES models in that it includes several algorithms that improve the simulation of water balance, soil carbon dynamics and crop phenology [33, 51, 52]. SALUS simulates the daily effects of crop rotations, planting dates, plant populations, irrigation, and fertilizer applications on plant growth and soil conditions and has been tested for crop yield (e.g. [5, 24, 53]), soil C dynamics (e.g. [50]), plant N uptake and phenology (e.g. [24, 49]), and NO3- leaching (e.g. [8]).

Crop model simulation scenarios

Simulations were performed with a rotational mode approach, which consists of running the model for the entire duration of the scenario without annual re-initialization of soil parameters. This method makes it possible to fully account for any carry-over effects of water and nutrients that may occur from one year to the next [54]. Crop rotation simulations were performed under the following guidelines:

  1. ○. A first set of rotational simulations was carried out for the experimental years to evaluate the model’s ability to simulate maize yield and soil N in each treatment.
  2. ○. A second set of rotational simulations was carried out for a long-term assessment of the treatments, using future climate scenarios on the three treatments and on a best management practice (BMP) that was defined in this study. The BMP replicated the use of both slurry and urea as in CONV, but reduced the N rate by 50 kg N ha-1 compared to CONV. The BMP consisted of a total N fertilization rate of 223 kg N ha-1 year−1 (synthetic + organic) with 173 kg N ha-1 applied as liquid manure before sowing (DOY 162) and 50 kg N ha-1 applied as urea during the growing season (DOY 189). This N rate was determined as optimal based on crop N uptake values observed during the 2010–2011 growing seasons and was designed to comply with the limits imposed by NVZ regulation (170 kg N ha−1 year−1 from organic amendments).

Statistical analyses

The ability of the model to predict grain yield was evaluated using the root mean square error (RMSE) as calculated with the following equation: (1) where N is the total number of observations (yield measurements at the end of each season), Obs are the observed values, and Sim are the simulated values. The relative error (R.E.) between the mean of simulated values and the mean of observed values was calculated to determine how closely the simulation matched the observed mean: (2) where Sim is the simulated value and Obs is the mean of observed values. Nitrogen use efficiency (NUE) was calculated with the partial balance approach. Inputs to this method include grain yield, percent moisture, crop N content, and the amount of N applied. NUE was calculated as follows: (3) where Napp is the amount of N fertilizer applied per hectare.

Nitrogen fertilizer efficiency (NFE) was calculated as follows: (4) where Nup is the crop N uptake.

N fertilizer recovery (NFrec) was calculated using the difference method, which is the difference between the N uptake simulated in a given fertilised treatment (Nup) and in the unfertilised treatments (Nup,N0), divided by the amount of N applied in the given treatment (∆N): (5)

Results

Climate data

Climate data for 2010 and 2011 showed that the annual rainfall for these two years was highly variable (Fig 1A and 1B). Annual rainfall in 2010 was 811.2 mm with a maximum of 89 mm in November and a minimum of 0.2 mm in July, while annual rainfall in 2011 was 544 mm, with a maximum of 123 mm in November and a minimum of 0.2 mm in August. Overall, annual rainfall in 2010 was similar to the long-term historic mean, while rainfall in 2011 was below the 1959–2011 historic average (Fig 1C). Temperatures in both years were close to the long-term historic means. Compared to long-term historic means, simulated projections highlighted higher rainfall in March, August, September and October, while projected temperatures were higher from February to August and lower during the remainder of the year (Fig 1C).

thumbnail
Fig 1. Growing season climate of the study site.

Rainfall (bars), maximum (solid line) and minimum (dashed line) temperatures in the years 2010 (a) and 2011 (b). Rainfall (grey bars), maximum (solid line and filled triangle), minimum (dashed line and filled circle) temperatures for fifty-three years: from 1 January 1959 to 31 December 2011 (c). Rainfall (white bars), maximum (solid line and open triangle), minimum (dashed line and open circle) temperatures for eighty-four years of future climate: from 1 January 2012 to 31 December 2095 (c). Rainfall values are sums (a and b) and 84 years average of monthly sums (c); temperature values are means, over 10-day periods (a and b) and 84 years average of monthly means (c).

http://dx.doi.org/10.1371/journal.pone.0146360.g001

Experimental and modelled data

The results of initial soil chemical and physical analyses are reported in Table 3. The top 40 cm of the soil profile is characterized by very high sand content, (mean value 97.3%), and high concentrations of both OC (20.5 g kg-1) and total N (2 g kg-1). Average OC at the beginning of the experiment was 8.3 g kg-1over the entire soil profile and the average total N was 0.98 g kg-1 (Tables 4 and 5). Mean pH was 7.5. The soil had an adequate supply of K2O and high levels of P2O5, especially in the top 60 cm (Table 4). The soil profile NO3- and NH4+ content were, on average, 26.8 and 47.7 mg kg-1, respectively (Table 3).

thumbnail
Table 3. Soil physical and chemical characteristics for each layer of the soil profile (means and standard errors, n = 3).

Soil samples were collected June 3, 2010, before fertilization and sowing.

http://dx.doi.org/10.1371/journal.pone.0146360.t003

thumbnail
Table 4. Means and standard errors of organic carbon content in soil profile layers related to three sampling dates during the maize-triticale-maize rotation for the N MIN, CONV and N0 treatments (n = 3).

Sample dates correspond to the harvest of maize (09/14/2010), triticale (05/10/2011) and maize (09/08/2011). Dates are reported as mm/dd/yy.

http://dx.doi.org/10.1371/journal.pone.0146360.t004

thumbnail
Table 5. Means and standard errors of total N content in soil profile layers related to three sampling dates during the maize-triticale-maize rotation for the N MIN, CONV and N0 treatments (n = 3).

Sample dates correspond to the harvest of maize (09/14/2010), triticale (05/10/2011) and maize (09/08/2011). Dates are reported as mm/dd/yy.

http://dx.doi.org/10.1371/journal.pone.0146360.t005

OC and total N content were measured again when maize and triticale were harvested. These results are summarized in Tables 4 and 5. Average OC levels for both maize harvest dates for NMIN, CONV and N0 were 11.2, 10.7, and 11.2 g kg-1 in 2010 and 14.5, 13.1, and 13.3 g kg-1 in 2011 (Table 4). At the triticale harvest date (05/10/2011), average soil profile OC levels were 8.6 g kg-1 (NMIN), 8.4 g kg-1 (CONV), and 7.9 g kg-1 (N0) (Table 4). The three treatments under triticale showed values closer to the OC observed at the beginning of the experiment (8.3 g kg-1), while after maize was harvested, higher values were observed (Table 4). Total N, as the average for the soil profile for maize at harvest time, were 1.1, 0.9, and 1.0 g kg-1 for NMIN, CONV, and N0 in 2010 and 1.3, 1.1, and 1.2 g kg-1 for NMIN, CONV, and N0 in 2011 (Table 5). At the harvest date of triticale, the values for total N, as an average for the soil profile, were 0.80, 0.73, and 0.74 g kg-1 for NMIN, CONV, and N0. Concentrations of OC and total N measured for triticale were lower than the values observed for maize for all three treatments. This was observed both in the soil profile average, and for each of the soil layers. Overall, values for soil OC and N for triticale were about 32% lower than those for maize.

NO3- and NH4+ concentrations in the soil profile were measured at various dates during the experiment and the results are shown as the means for the soil profile (0–140 cm) in Table 6. NO3- concentrations ranged between 13.6 and 43.1 mg kg-1 for NMIN, 13.8 and 47.6 mg kg-1 for CONV, and 9.3 and 30.6 mg kg-1 for N0 (Table 6). Ammonium concentrations ranged between 11.9 and 51.3 mg kg-1 for NMIN, 4.3 and 81.6 mg kg-1 for CONV, and 8.8 and 33.0 mg kg-1 for N0 (Table 6). Within each treatment, the concentration of both NO3- and NH4+ varied greatly and values obtained during the triticale growing season were lower than those that were measured at the beginning of the experiment (Tables 3 and 6).

thumbnail
Table 6. Measured means and standard errors of nitrate and ammonium soil profile content during the maize-triticale-maize rotation for the N MIN, CONV and N0 treatments (n = 3).

Values of the eight soil depths were averaged for each sample and the standard errors calculated for the three replicates. Dates are reported as mm/dd/yy.

http://dx.doi.org/10.1371/journal.pone.0146360.t006

Crop biomass, grain and stover N contents, and crop N uptake were measured in each treatment at harvest (Table 7). No results are provided for the N content in the triticale grain since triticale was harvested for silage. Overall, the N0 treatment showed the lowest values for all the variables evaluated, whereas similar values for all variables were observed in the NMIN and CONV treatments (Table 7). Crop N uptake for triticale was higher for NMIN than for the other two treatments (Table 7).

thumbnail
Table 7. Means and standard errors of biomass, N content and N uptake at the harvest dates of maize and triticale crops (n = 3).

Sampling dates correspond to the harvest of maize (09/14/2010), triticale (05/10/2011) and maize (09/08/2011). Dates are reported as mm/dd/yy.

http://dx.doi.org/10.1371/journal.pone.0146360.t007

The model evaluation of maize yield for both growing seasons (2010 and 2011) and for the three N treatments is shown in Table 8. The observed maize yields in 2010 were 23.68, 22.45 and 20.22 t ha-1 for NMIN, CONV, and N0, respectively (Table 8). In 2011, observed yields were 25.50t ha-1 for NMIN, 25.6t ha-1 for CONV and 12.7t ha-1 for N0. Overall, SALUS effectively reproduced the observed yield for each treatment. RMSE values for the harvested grain during the two cropping seasons varied from 0.73 t ha-1 to 4.10 t ha-1 in NMIN, from 0.45 t ha-1 to 3.53 t ha-1 in CONV, and from 0.10 t ha-1 to 0.12 t ha-1 in N0 (Table 8). The R.E. of observed and simulated grain yield ranged from 3.08% to 16.08% in NMIN, from 2% to 13.77% in CONV, and from 0.49% to 0.94% in N0 (Table 8).

thumbnail
Table 8. Observed (means and standard errors, n = 3) and simulated silage maize yields for the NMIN, CONV and N0 treatments.

Yields are only reported for maize as triticale was harvested for silage. Sampling dates correspond to the harvest of maize.

http://dx.doi.org/10.1371/journal.pone.0146360.t008

The SALUS model also effectively reproduced the magnitude and the temporal patterns of the soil profile NO3- over the crop rotation (Fig 2). The total soil profile NO3- content under CONV treatment was well-simulated, except for four dates when it was underestimated by an amount that ranged from 62.7 to 476.9 kg NO3- ha-1 (see Figs 2B and 3B). The total soil profile NO3- content for the N0 treatment was overestimated by 120 kg NO3- ha-1 on only one date and was underestimated for five dates by amounts that ranged from 51.8 to 225.4 NO3- ha-1 (see Figs 2C and 3C).

thumbnail
Fig 2. Observed and simulated soil profile nitrate content.

Observed (filled bars) and simulated (open bars) soil profile nitrate (NO3-) content during the maize-triticale-maize rotation for the NMIN (a), CONV (b) and N0 (c) treatments.

http://dx.doi.org/10.1371/journal.pone.0146360.g002

thumbnail
Fig 3. Observed and simulated soil profile nitrate content.

Observed (filled bars) and simulated (open bars) nitrate (NO3-) content in soil profile layers for the NMIN (a), CONV (b) and N0 (c) treatments on August 18, 2010.

http://dx.doi.org/10.1371/journal.pone.0146360.g003

The underestimation of total NO3- concentrations on some dates was mainly due to the simulation of low NO3- levels in the lower layers of the soil profile. This can be observed in Fig 3, where NO3- levels are reported for each soil layer on August 18 2010, when NO3- levels were underestimated in all three treatments. On this date, simulation of the NMIN treatment highlighted some slight overestimation at 0.6 m (by 13.2 kg NO3- ha-1), at 0.8m (by 25.8 kg NO3- ha-1), and at 1 m (by 31 kg NO3- ha-1), and moderate underestimation at 0.1, 0.2 and 1.2 m by 19.9, 18 and 9.7 kg NO3- ha-1 (Fig 3A). For the CONV treatment, soil NO3- levels were overestimated at 0.8 m by 44 kg NO3- ha-1 and underestimated at 1 m and 1.2 m by 31.2 and 71.9 kg NO3- ha-1, respectively (Fig 3B). The soil NO3- for N0 were overestimated at 1 m by 15.7 kg NO3- ha-1, and slightly underestimated at 0.1, 0.2, 0.4, 0.6 and 1.2 m by 10, 19, 8.7, 15.7 and 9.2 kg NO3- ha-1, respectively (Fig 3C).

Rotational scenarios simulation results

The comparison between BL and the two RCPs highlighted minor differences between the three scenarios, while the coefficient of variation (the ratio between the daily standard deviation over the 29 GCMs and the daily mean value) varied depending on the specific parameter measured. On average, the difference in temperature equaled +1.4°C between the historic baseline scenario (BL—1959–2013) and the RCP6.0 emission scenario and +2.0°C between BL and RCP8.5. The coefficient of variation (CV) between BL and the two emission scenarios equaled 4% for RCP6.0 and 6% for RCP8.5. The CV tended to be constant and independent from the magnitude of the temperature. The average difference between BL and the two RCPs in terms of solar radiation was approximately null (0.04 MJ m-2 d-1 and 0.06 MJ m-2 d-1, respectively), and was characterized by a very small CV of 0.025 MJ.m-2 d-1 for RCP6.0 and 0.04 MJ.m-2 d-1for RCP8.5. Even though the average difference in daily precipitation between BL and the two RCP was approximately null (-0.036 and -0.054 mm d-1, respectively), its CV over the GCMs was larger (25% for RCP6.0 and 41% for RCP8.5). The higher rain events were characterized by the highest variability over the 29 GCMs as the standard error increased with the absolute precipitation amount.

Limited variations in daily maximal temperatures were observed between emission scenarios when downscaling the climatic conditions with the 29 GCMs. This is shown in Fig 4A and 4B, where intra-annual variability of maximal temperatures is shown for one sample year (2025) to improve readability. Simulation of long-term soil NO3- dynamics resulting from the 29 GCMs showed lower average NO3- contents under RCP6.0, while the RCP 8.5 emission scenario was characterized by higher inter-annual variability (Fig 4C and 4D).

thumbnail
Fig 4. Daily observed and projected maximal temperature under two different emission scenarios and projected impacts on soil nitrogen content.

Daily maximal temperature observed (black line) and projected (dashed black line) under emission scenario RCP6.0 (a) and RCP 8.5 (b) for the year 2025. Simulated annual median soil N-NO3- content (black line) for CONV treatment and under emission scenario RCP 6.0 (c) and RCP 8.5 (d). The confidence interval (CI) drawn out of the 29 GCMs is represented by the shaded grey area.

http://dx.doi.org/10.1371/journal.pone.0146360.g004

Each of the simulations obtained with a different GCM was compared to the simulation obtained with the BL scenario. For each emission scenario the differences were then averaged and the standard errors computed using a yearly average. The CVs were then computed to express the variability due to the choice of GCMs, as a percentage of the average difference between BL and the considered emission scenario.

The average simulated silage yield difference equaled 1440 kg ha-1 between BL and RCP6.0 and 3460 kg ha-1 between BL and RCP8.5. These differences were characterized by an average standard deviation due to the GCM of 48.9 kg.ha-1 (3.4%) and 155 kg.ha-1 (4.5%) considering RCP6.0 and RCP8.5, respectively. Differences in projected yields between the BL and the two emission scenarios remained constant across treatments. The slight increase in biomass accumulation under climate change occurred because the crop is harvested as silage, and not for grains, in fact the harvest index is rather lower than the baseline. Annual average N uptake was slightly higher under RCP6.0 (+4.32 to +24.26 kg N ha-1) and RCP8.5 (+6.13 to +34.85 kg N ha-1) compared to BL scenario (Table 9). BMP treatment showed the lowest increase (+6.13 kg N ha-1 under RCP8.5 vs. BL), followed by N0 (+14.95 kg N ha-1 under RCP8.5 vs. BL). NMIN and CONV had the highest increase (+32.20 and + 34.85 kg N ha-1 under RCP8.5 vs. BL). NMIN exhibited the lowest variability due to GCMs in terms of N uptake difference (on average +- 5.2% under RCP8.5 vs BL) and N0 treatment had the highest variability due to the GCMs (+9.1% under RCP8.5).

thumbnail
Table 9. Difference (Δ) in terms of model simulations between historic BL and downscaled climatic data under both RCP6.0 and RCP8.5 emission scenarios.

Total biomass, N uptake, and N-NO3- leaching are compared for each treatment in the long term rotation (2020–2073). Results report means and coefficients of variation. Positive value indicates that simulation obtained under RCM is superior to simulation obtained under BL scenario.

http://dx.doi.org/10.1371/journal.pone.0146360.t009

Differences in NO3- annually leached were globally very low, when BL is compared to RCP6.0 and RCP8.5 (Table 9). Results were quite similar under both emissions scenarios. BMP was characterized by the lowest difference in annually leached NO3- (+4.0 kg N ha-1 on average for RCP6.0 and RCP8.5) and CONV by the highest (+11.5 kg N ha-1 on average for RCP6.0 and RCP8.5). NMIN demonstrated a leaching difference of +5.8 kg N ha-1 under RCP6.0 but a negative difference of -7.7 kg N ha-1 under RCP8.5. While the differences from BL were quite similar for both emission scenarios, the variability of these differences were higher for all treatments under RCP8.5, i.e. 25–50% under RCP6.0 and 40–67% under RCP8.5 (Fig 4C and 4D). This is thought to be associated to the high variability of downscaled rainy events.

Table 10 shows the simulated yearly averages of maize biomass, N uptake, N use efficiency (NUE), N fertilizer efficiency (NFE) and the % fertilizer recovery for the four management practices (the original three from the field study plus the BMP treatment) using future climate projections under both RCP6.0 and 8.5 emissions scenarios. The medians of model simulations were computed over the 29 GCMs, at daily and seasonal time steps. Table 10 also reports the mean and standard errors over the 53 years of simulations. Under the RCP8.5 emission scenario, simulated maize biomass was 26.3 t ha-1 for NMIN, 26.1 t ha-1 for CONV, and 25.9 t ha-1 for BMP, while simulated biomass for N0 was 23.8 t ha-1 (Table 10). Simulated crop N uptake was 372.6, 267.3, 151.1 and 231.8 kg N ha-1 for NMIN, CONV, N0, and BMP, respectively (Table 8). Nitrogen use efficiency (NUE) was 114.4 kg kgN-1 for NMIN, 95.7 kg kgN-1 for CONV, and 116.2 kg kgN-1 for BMP, while the fertilizer recovery rates were 95.8% for NMIN, 42.6% for CONV, and 35.8% for BMP (Table 10).

thumbnail
Table 10. Simulated yearly averages of total N supply, biomass (expressed as dry matter), N uptake, N use efficiency (NUE), N fertilizer efficiency (NFE) and fertilizer recovery related to the maize crop for the NMIN, CONV, N0 and BMP treatments in the long-term rotation (2020–2073).

Results report means and standard errors.

http://dx.doi.org/10.1371/journal.pone.0146360.t010

Simulated soil NO3- content (Fig 5A) for maize under future climate conditions was high for NMIN after the first year of the long-term simulations (521 kg N ha-1), but decreased by 75% over the simulated period, to reach 129 kg N ha-1 remaining in the soil. On the other hand, CONV treatment left 309 kg N ha-1 in the soil after the first year, and the simulation ended with 76 kg N ha-1 (75.4% reduction). Similarly, N remaining in the soil after the first year was also observed for BMP (331 kg N ha-1) and NO (365 kg N ha-1), while NO3- was reduced in these treatments by 86–88% by the end of the simulated period. Simulated soil NO3- contents showed similar trends for both emission scenarios.

thumbnail
Fig 5. Simulated annual soil nitrate content and maize nitrogen uptake.

Simulated median soil nitrate content (a) and annual median nitrogen uptake by the maize crop (b) for the NMIN (grey empty triangle line), CONV (grey empty circle line), N0 (black full triangle line) and BMP (black full circle line) treatments in the long term rotation (2020–2073).

http://dx.doi.org/10.1371/journal.pone.0146360.g005

Simulated maize N uptake showed different but consistent patterns for NMIN, CONV, and BMP but decreased sharply for the N0 treatment (Fig 5B). The average N uptake for NMIN, CONV and BMP equaled 372, 267 and 231 kg N ha-1 year-1 respectively, and were consistent between RCP scenarios (Fig 5B). Overall, the crop N uptake decreased to 3.2 kg N ha-1 year-1 for N0, both under RCP6.0 and RCP8.5.

Simulated cumulative NO3- leached increased over time for all the simulated treatments (Fig 6). Under N0 treatment, the cumulated NO3- amount leached equaled 786 kg N ha-1 under RCP8.5 treatment (762 kg N ha-1 under RCP6.0). CONV management led to the leaching of 1139 and 1103 kg N ha-1 under RCP8.5 and RCP6.0 scenarios. NMIN management showed the highest losses, with 4150 and 4882 kg N ha-1 under RCP8.5 and RCP6.0 scenarios, respectively. The amount of NO3- leached (median simulated values out the 29 GCMs) were thus systematically lower under RCP8.5 emission, compared to RCP6.0. The trade-off between maize biomass production and NO3- leaching for the four simulated treatments can be seen in Fig 7. N0 produced the lowest biomass (23,780 kg ha-1 under RCP8.5) and was characterized by an average annual NO3- leached of 23.9 kg N ha-1 year-1 (under RCP8.5). NMIN exhibited the highest simulated biomass yields (24,060–26,300 kg ha-1 on average, according to RCP6.0 and RCP8.5), but at the price of the highest leaching levels (92–78 kg N ha-1 year-1 under RCP6.0 and RCP8.5).

thumbnail
Fig 6. Simulated cumulative nitrate leaching.

Simulated median cumulative nitrate leached for the NMIN (grey empty triangle line), CONV (grey empty circle line), N0 (black full triangle line) and BMP (black full circle line) treatments in the long term rotation (2020–2073) under RCP8.5.

http://dx.doi.org/10.1371/journal.pone.0146360.g006

thumbnail
Fig 7. Inter-annual variability and inter-GCMs variability associated to nitrate leaching and harvested biomass.

Average annual nitrate leached and harvest biomass for maize for the NMIN (open triangle), CONV (open circle), N0 (filled triangle) and BMP (filled circle) treatments in the long term rotation (2020–2073). Fig7a and b shows the inter-year variability when GCMs simulations are averaged. Fig7c and d shows the variability over the GCMs when average are computed over years. Fig7a and c refer to RCP6.0 scenario, while Fig7b and d refer to RCP8.5.

http://dx.doi.org/10.1371/journal.pone.0146360.g007

The BMP treatment resulted in one of the highest biomass values (23,830–25,905 kg ha-1) and the lowest annual NO3- leaching (14.4–14.8 kg NO3- ha-1 year-1), which highlights the importance of optimal use of Nitrogen for plant growth. Overall, CONV showed similar results to BMP in terms of biomass. Fig 7A and 7B show the inter-year variability when GCMs simulations are averaged. Fig 7C and 7D show the variability over the GCMs when averages are computed over the years. Wilcoxon tests were performed to compare the simulations averaged over GCM’s or years.

When considering inter-year variability (Fig 7A and 7B), no significant differences were found in biomass production between the fertilised treatments (NMIN, CONV and BMP), even though NO3- leaching under BMP treatment was found to be highly significantly different (p-value < 0.001) from all other treatments.

When considering yield variability over the 29 GCMs (Fig 7C and 7D), BMP was statistically different (p-value < 0.01) from NMIN but not from CONV, while NMIN and CONV had biomass production values that were not significantly different. Statistical tests revealed the same results under both RCP 6.0 and RCP8.5 emission scenarios.

Discussions

The application of N in the form of mineral fertilizers or organic [55, 56] amendments is necessary to achieve adequate levels of crop production and quality. However, this is often associated with significant environmental impacts due to the difficulty of matching crop N demand with N supply [5, 5558].

SALUS was able to effectively reproduce the measured patterns of soil NO3- and maize silage yields observed in data collected from an experiment over the course of two years. This suggests that the model could be used to test alternative N management strategies to abate NO3- leaching and maintain crop biomass production under future climate conditions.

This study showed that agronomic practices aimed at minimizing NO3- leaching under current conditions in NVZ [59] will not be sufficient to optimize the same economic and environmental benefits under future climatic conditions. BMP, as determined from data collected from two growing seasons, was the practice that best minimized leaching and maximized biomass production over the long term.

This study highlighted the challenge that current N management practices adopted to comply with the Directive 91/676 [41, 59], such as CONV and NMIN, will not perform well under projected climate change. Even though NO3- concentrations in these treatments were slightly lower than the maximum threshold of 50 mg L-1 in Directive 91/676 (Table 6), simulations showed that the NUE of these management practices will be far from optimal under projected future conditions. Climate patterns predicted for this region are expected to bring higher temperatures from February to August and increased rainfall from August to October and in March (Fig 1C). During the summer months these environmental conditions increase the potential for crop stress which may result in a reduction of crop N uptake. In addition, projected changes in precipitation during the spring and autumn months increase the potential for NO3- leaching.

In terms of crop N uptake, there were no important differences between CONV and BMP (30 kg N ha-1, Fig 5B), which suggests that any effect of climate change will not be reflected in the crop’s ability to utilize soil N. However, substantial differences were noticed in soil NO3- concentrations and the amount of NO3- leached from the soil (Figs 5A and 6) among the different treatments. The reason for this result is twofold: first, the N application rate in maize under the BMP treatment (223 kg N ha-1) was slightly lower than under NMIN (230 kg N ha-1) and substantially lower than under CONV (304 kg N ha-1). Moreover, the separate application of liquid manure before sowing (173 kg N ha-1) and the in-season application of urea (50 kg N ha-1) maintained a high degree of synchronicity between plant N demand and N supply. As a result, N accumulation in the soil was limited and resulted in substantially lower nitrate leaching rates under both RCP6.0 and RCP8.5 scenarios (Figs 5A, 6 and 7). Accordingly, the NMIN and CONV systems showed the highest values of NO3- leaching under projected changes in climate (Fig 5).

The reduced N rates of BMP also resulted in average plant N uptake levels (230 kg N ha-1) that were comparable to CONV (260 kg N ha-1) or substantially lower than in NMIN (360 kg N ha-1) as shown in Fig 5B and Table 9. Importantly, even though the N rate in BMP was reduced by 50kg N ha-1 compared to CONV and NMIN, crop N availability in BMP did not affect average biomass production (25 t dry matter ha-1), which was comparable to both NMIN and CONV under the RCP6.0 and RCP8.5 scenarios (Fig 7, Table 9). These results confirm the observations of [60] who reported no differences in maize biomass production between treatments fertilised with mineral N or with slurry in a Mediterranean environment.

Critically, the ratio between harvested biomass and N lost via leaching simulated for the BMP practice were substantially higher compared to the other treatments (Fig 7, Table 9).

A N management practice that is able to deliver high crop productivity and limit NO3- leaching will be critical for future Mediterranean farming systems as climate change is projected to increase summer temperatures and autumn rainfall events. Accordingly, N management practices that will enable farmers to reduce N inefficiencies under these circumstances will result in substantial economic gains [5].

These results are critical for future agricultural practices in Mediterranean nitrate-vulnerable zones. Crop yields in these systems will need to be maintained and N leaching minimized even as changes in climate cause substantial shifts in temperature and rainfall patterns [61]. This study also highlights the potential for using crop models to predict crop response to different N management strategies and environmental stresses under future climatic scenarios. This methodology made it possible to identify BMP as the N management strategy best suited to comply with European regulations, since BMP demonstrates the ability to achieve high maize biomass production levels and minimize NO3- leaching losses through the use of lower N inputs. Overall, the results of this research can assist farmers and policy makers to define the N management practices best suited to comply with Directive 91/676 [41] and maintain high crop productivity levels under changing climatic conditions.

Conclusions

The scenarios simulated in this study illustrate the implications that future climate changes could have on N dynamics in a Mediterranean NVZ. While these results are influenced by the particular crops and soil characteristics of the site chosen, they provide insight into the potential to increase NUE and decrease nitrate leaching in cereal-based cropping systems in a Mediterranean NVZ.

Simulations from the SALUS model show that it reproduces the patterns of soil NO3- and silage yield observed in field conditions. The model was used, along with future climate scenarios, to extrapolate the results an experiment conducted over two year into the future.

In the projected scenarios, the three N treatments assessed in the field study (N0, NMIN, CONV) were compared to a best management practice (BMP) chosen on the basis of observed crop N uptake from data collected during the two years of field trials. NMIN, CONV, and BMP showed similar crop N uptake over the long-term simulation (Fig 5B). However, NMIN and CONV showed higher NO3- leaching than BMP. Therefore, the trade-off between the amount of biomass produced and the amount of NO3- leached suggests that BMP was the best practice for reducing environmental pollution and maximizing production.

The SALUS crop model demonstrated its ability to reproduce field experiment results in the short term and its utility in projecting alternative management scenarios beyond a few years of experimental data into the future. Current conventional practices intended to minimize N loss will be inadequate in the future (Fig 6) because of changes in weather patterns. Importantly, farmers will be able to achieve substantial reductions in nitrate leaching by decreasing current mineral and organic fertilizer N rates without suffering yield penalties.

The results of this research will assist farmers and policy makers to define N management practices best suited to comply with the Directive 91/676 [41] while maintaining high productivity levels.

Acknowledgments

The authors wish to thank Luigi Zuccaro and Anne Stanton for their technical support.

Author Contributions

Conceived and designed the experiments: BB FG PG GP. Performed the experiments: PG GP BB FG. Analyzed the data: PG BB BD DC MDM GP FG. Wrote the paper: BB MDM BD DC PG FG.

References

  1. 1. Rode M, Klauer B, Petry D, Volk M, Wenk G, Wagenschein D (2008) Integrated nutrient transport modeling with respect to the implementation of the European WFD: The WeißeElster case study, Germany. Water SA 34: 490–496.
  2. 2. Volk M, Liersch S, Schmidt G (2009) Towards the implementation of the European Water Framework Directive? Lesson learned from water quality simulations in an agricultural watershed. Land Use Policy 26:5 80–588.
  3. 3. Syswerda SP, Basso B, Hamilton SK, Tausig JB, Robertson GP (2012) Long-term nitrate loss along an agricultural intensity gradient in the Upper Midwest USA. Agriculture, Ecosystems & Environment 149: 10–19.
  4. 4. EC (2000) Council directive of 23th October 2000 establishing a framework for Community action in the field of water policy (2000/60/EC). Official Journal of the European Union 22/12/2000 L327/1 to 73.
  5. 5. Basso B, Sartori L, Cammarano D, Fiorentino C, Grace PR, Fountas S et al. (2012) Environmental and economic evaluation of N fertilizer rates in a maize crop in Italy: A spatial and temporal analysis using crop models. Biosys Eng 113: 103–111.
  6. 6. Basso B, Ritchie JT (2005) Impact of compost, manure and inorganic fertilizer on nitrate leaching and yield for a 6-year maize-alfalfa rotation in Michigan. Agriculture Ecosystems & Environment 108: 329–341.
  7. 7. Velthof GL, Oudendag D, Witzke HP, Asman WAH, Klimont Z, Oenema O (2009) Integrated assessment of nitrogen losses from agriculture in UE-27 using MITERRA-EUROPE. J Environ Qual 38: 402–417. doi: 10.2134/jeq2008.0108. pmid:19202011
  8. 8. Giola P, Basso B, Pruneddu G, Giunta F, Jones JW (2012) Impact of manure and slurry applications on soil nitrate in a maize-triticale rotation: field study and long term simulation analysis. European Journal of Agronomy 38: 43–53.
  9. 9. Robertson GP, Gross KL, Hamilton SK, Landis DA, Thomas M (2014) Farming for Ecosystem Services: An Ecological Approach to Production Agriculture. BioScience 64: 404–415.
  10. 10. Fenn ME, Poth MA, Aber JD, Baron JS, Bormann BT, Johnson DW et al. (1998) Nitrogen excess in North American ecosystems: predisposing factors, ecosystem responses, and management strategies. Ecol Appl 8: 706–733.
  11. 11. Robertson GP, Paul EA, Harwood RR (2000) Greenhouse Gases in Intensive Agriculture: Contributions of Individual Gases to the Radiative Forcing of the Atmosphere. Science 289: 1922–1925. pmid:10988070
  12. 12. Galloway JN, Dentener FJ, Capone DG, Boyer EW, Howarth RW, et al (2004) Nitrogen cycles: past, present and future. Biogeochemistry 70:153–226.
  13. 13. Sanchez JE, Harwood RR, Willson TC, Kizilkaya K, Smeenk J, Parker E, et al. (2004) Managing soil carbon and nitrogen for productivity and environmental quality. Agron J 96: 769–775.
  14. 14. Crutzen PJ, Mosier AR, Smith KA, Winiwarter W (2008) N2O release from agro-biofuel production negates global warming reduction by replacing fossil fuels. Atmos Chem Phys 8:389–395.
  15. 15. Tafteh A, Sepaskhah AR (2012) Yield and nitrogen leaching in maize field under different nitrogen rates and partial root drying irrigation. Int J Plant Prod 6: 93–113.
  16. 16. Zhou MH, Zhu B, Butterbach-Bahl K, Wang T, Bergmann J, Bruggemann N (2012) Nitrate leaching, direct and indirect nitrous oxide fluxes from sloping cropland in the purple soil area, southwestern China. Environ Pollut 162: 361–368. doi: 10.1016/j.envpol.2011.12.001. pmid:22243886
  17. 17. van der Laana M, Annandalea JG, Bristowa KL, Stirzakera RJ, du Preezc CC, Thorburnba PJ (2014) Modelling nitrogen leaching: Are we getting the right answer for the right reason? Agricultural Water Management 133: 74–80.
  18. 18. Carneiro JP, Coutinho J, Trindade H (2012) Nitrate leaching from a maize x oat double-cropping forage system fertilized with organic residues under Mediterranean conditions. Agriculture Ecosystems & Environment 160: 29–39.
  19. 19. Zavattaro L, Monaco S, Sacco D, Grignani C (2012) Options to reduce N loss from maize in intensive cropping systems in Northern Italy. Agriculture Ecosystems & Environment 147: 24–35.
  20. 20. Leclerc B, Georges P, Cauwel B, Lairon D (1995) A Five Year Study on Nitrate Leaching under Crops Fertilised with Mineral and Organic Fertilisers in Lysimeters. Biological Agriculture & Horticulture: An International Journal for Sustainable Production Systems 11(1–4): 301–308.
  21. 21. Salazar F, Martínez-Lagos J, Alfaro M, Misselbrook T (2012) Low nitrogen leaching losses following a high rate of dairy slurry and urea application to pasture on a volcanic soil in Southern Chile. Agriculture, Ecosystems & Environment 160: 23–28.
  22. 22. Zhou MH, Zhu B, Butterbach-Bahl K (2014) Assessment of nitrate leaching loss on a yield-scaled basis from maize and wheat cropping systems. Plant Soil 374: 977–991.
  23. 23. Michalczyk A, Kersebaum KC, Roelcke M, Hartmann T, Yue SC, Chen XP et al. (2014) Model-based optimisation of nitrogen and water management for wheat–maize systems in the North China Plain. Nutr Cycl Agroecosyst 98: 203–222. doi: 10.1007/s10705-014-9606-0.
  24. 24. Basso B, Cammarano D, Troccoli A, Chen D, Ritchie JT (2010) Long-term wheat response to nitrogen in a rainfed Mediterranean environment: Field data and simulation analysis. European Journal of Agronomy 33: 132–138.
  25. 25. Jones JW, Tsuji GY, Hoogenboom G, Hunt LA, Thornton PK, Wilkens PW (1998) Decision support system for agrotechnology transfer; DSSAT v3. In: Tsuji GY, Hoogenboom G, Thornton PK, editors. Understanding Options for Agricultural Production. Kluwer Academic Publishers, Dordrecht, the Netherlands. pp. 157–177.
  26. 26. Saseendran SA, Nielsen DC, Ma L, Ahuja LR, Halvorson AD (2004) Modeling nitrogen management effects on winter wheat production using RZWQM and CERES-wheat. Agron J 96:615–630. doi: 10.2134/agronj2004.0615.
  27. 27. Norwood CA, Schlegel AJ, Morishita DW, Gwin RW (1990) Cropping system and tillage effects on available soil water and yield of grain sorghum and wheat. J Prod Agric 3: 356–362.
  28. 28. Kolberg RL, Kitchen NR, Westfall DG, Peterson GA (1996) Cropping intensity and nitrogen management impact of dryland no-till rotations in the semi-arid western Great Plains. Journal of Production Agriculture 9: 517–522.
  29. 29. O’Leary GJ, Connor DJ (1996) A simulation model of the wheat crop in response to water and nitrogen supply: II. Model validation. Agric Syst 52: 31–55.
  30. 30. Basso B, Sartori L, Bertocco M, Cammarano D, Grace PR (2011) Economic and environmental evaluation of site-specific tillage in a maize crop in NE Italy. European Journal of Agronomy 35: 83–92.
  31. 31. Ludwig B, Jäger N, Priesack E, Flessa H (2011) Application of the DNDC model to predict N2O emissions from sandy arable soils with differing fertilization in a long-term experiment. J Plant Nutr Soil Sci 174: 350–358. doi: 10.1002/jpln.201000040.
  32. 32. Duan Y, Xu M, Gao S, Yang X, Huang S, et al. (2014) Nitrogen use efficiency in a wheat–corn cropping system from 15 years of manure and fertilizer applications. Field Crops Research 157: 47–56.
  33. 33. Basso B, Ritchie JT (2015) Simulating Crop Growth and Biogeochemical Fluxes in Response to Land Management using the SALUS Model. In Hamilton SK, Doll JE, Robertson GP, editors. The ecology of agricultural landscapes: long-term research on the path to sustainability. Oxford University Press, New York, NY USA. pp. 252–274.
  34. 34. Dumont B, Basso B, Bodson B, Destain JP, Destain MF (2015) Climatic risk assessment to improve nitrogen fertilisation recommendations: A strategic crop model-based approach. European Journal of Agronomy 65: 10–17.
  35. 35. IPCC, Summary for Policymakers (2014). In: Climate Change 2014, Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Edenhofer O, Pichs-Madruga R, Sokona Y, Farahani E, Kadner S, Seyboth K et al. editors. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.
  36. 36. Olesen JR, Bindi M (2002) Consequences of climate change for European agricultural productivity, land use and policy. European Journal of Agronomy 16: 239–262.
  37. 37. Cubasch U., Wuebbles D., Chen D., Facchini M.C., Frame D., Mahowald N., J.-G. , W. , 2013. Introduction. Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker T.F., Qin D., Plattner G.-K., Tignor M., Allen S.K., Boschung J., Nauels A., Xia Y., Bex V. and Midgley P.M. (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.
  38. 38. van Vuuren D., Edmonds J., Kainuma M., Riahi K., Thomson A., Hibbard K., Hurtt G., Kram T., Krey V., Lamarque J.-F., Masui T., Meinshausen M., Nakicenovic N., Smith S., Rose S., 2011. The representative concentration pathways: an overview. Climatic Change 109, 5–31
  39. 39. Raper SCB, Gregory JM, Osborn TJ (2001) Use of an upwelling-diffusion energy balance climate model to simulate and diagnose A/OGCM results. Climate Dynamics 17(8): 601–613.
  40. 40. Whetton PH, McInnes KL, Jones RN, Hennessy KJ, Suppiah R, Page CM et al. (2005) Australian Climate Change Projections for Impact Assessment and Policy Application: A Review. Climate Impact Group, CSIRO Marine and Atmospheric Research, Aspendale, Victoria, Australia.
  41. 41. EEC (1991), Council Directive of 12 December 1991 concerning the protection of waters against pollution caused by nitrates from agricultural sources. Official Journal of European Communities 91/676/EEC.
  42. 42. Indorante SJ, Follmer LR, Hammer RD, Koenig PG (1990) Particle analysis by a modified pipette procedure. Soil Sci Soc Am J 54: 560–563.
  43. 43. Moshrefi N (1993) A new method of sampling soil suspension for particle-size analysis. Soil Science 155: 245–248.
  44. 44. Walkley A, Black IA (1934) An examination of the Degtjareff method for determining organic carbon in soils: Effect of variations in digestion conditions and of inorganic soil constituents. Soil Science 63:251–263.
  45. 45. Fox RH, Piekielek WP (1978) A rapid method for estimating the nitrogen-supplying capability of a soil. Soil Science Society of America Journal 42: 751–753.
  46. 46. Henzell EF, Vallis I, Lindquist JE (1968) Automatic colorimetric methods for the determination of nitrogen in digests and extract of soils. Transactions, Ninth International Congress of Soil Science 3: 513–520.
  47. 47. Olsen SR, Cole CV, Wantanabe FS, Dean LA (1954) Estimation of available phosphorus in soil by extraction with sodium bicarbonate. U.S. Gov. Print. Office, Washington, D.C. USDA circular 939.
  48. 48. Ministry of Agricultural and Forestry Policies (1999) Official methods for chemical analysis of soil. Official Journal of Italian Republic n° 248.
  49. 49. Basso B, Ritchie JT, Cammarano D, Sartori L (2011) A strategic and tactical management approach to select optimal N fertilizer rates for wheat in a spatially variable field. European Journal of Agronomy 35: 215–222.
  50. 50. Senthilkumar S, Basso B, Kravchenko AN, Robertson GP (2009) Contemporary Evidence of Soil Carbon Loss in the U.S. Corn Belt. Soil Science Society of America Journal 73: 2078–2086. doi: 10.2136/sssaj2009.0044.
  51. 51. Dzotsi KA, Basso B, Jones JW (2013) Development, uncertainty and sensitivity analysis of the simple SALUS crop model in DSSAT. Ecological Modelling 260: 62–76. doi: 10.1016/j.ecolmodel.2013.03.017.
  52. 52. Dzotsi KA, Basso B, Jones JW (2015) Parameter and uncertainty estimation for maize, peanut and cotton using the SALUS crop model. Agricultural Systems 135: 31–47.
  53. 53. Basso B, Bertocco M, Sartori L, Martin EC (2007) Analyzing the effects of climate variability on spatial pattern of yield in a maize-wheat-soybean rotation. European Journal of Agronomy 26(2): 82–91.
  54. 54. Basso B, Hyndman DW, Kendall AD, Grace PR, Robertson GP (2015) Can Impacts of Climate Change and Agricultural Adaptation Strategies Be Accurately Quantified if Crop Models Are Annually Re-Initialized? PLoS ONE 10(6): e0127333. doi: 10.1371/journal.pone.0127333. pmid:26043188
  55. 55. Cassman KG, Dobermann A, Walters D (2002) Agroecosystems, nitrogen-use efficiency, and nitrogen management. Ambio 31:132–140. pmid:12078002
  56. 56. Robertson GP, Vitousek PM (2009) Nitrogen in Agriculture: Balancing the Cost of an Essential Resource. Annual Review of Environment and Resources 34: 97–125.
  57. 57. Angus JF (2001) Nitrogen supply and demand in Australian agriculture. Australian Journal of Experimental Agriculture 41: 277–288.
  58. 58. Jensen E.S., Peoples M.B., Boddey R.M., Gresshoff P.M., Hauggaard-Nielsen H., Alves B., Morrison M.J., 2012. Legumes for mitigation of climate change and the provision of feedstock for biofuels and biorefineries. A review. Agron. Sustain. Dev. 32, 329–364.
  59. 59. Cau P, Paniconi C (2007) Assessment of alternative management practices using hydrological simulation and a decision support tool: Arborea agricultural region, Sardinia. Hydrology and Earth System Sciences Discussions 4: 747–775.
  60. 60. Daudén A., Quílez D., 2004. Pig slurry versus mineral fertilization on corn yield and nitrate leaching in a Mediterranean irrigated environment. European Journal of Agronomy 21, 7–19.
  61. 61. Olesen J.E., Carter T.R., Díaz-Ambrona C.H., Fronzek S., Heidmann T., Hickler T., Holt T., Minguez M.I., Morales P., Palutikof J.P., Quemada M., Ruiz-Ramos M., Rubæk G.H., Sau F., Smith B., Sykes M.T., 2007. Uncertainties in projected impacts of climate change on European agriculture and terrestrial ecosystems based on scenarios from regional climate models. Climatic Change 81, 123–143.