DSSAT-CERES-maize modelling to improve irrigation and nitrogen management practices under Mediterranean conditions

DSSAT-CERES-maize modelling to improve irrigation and nitrogen management practices under Mediterranean conditions

Agricultural Water Management 213 (2019) 298–308 Contents lists available at ScienceDirect Agricultural Water Management journal homepage: www.elsev...

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Agricultural Water Management 213 (2019) 298–308

Contents lists available at ScienceDirect

Agricultural Water Management journal homepage: www.elsevier.com/locate/agwat

DSSAT-CERES-maize modelling to improve irrigation and nitrogen management practices under Mediterranean conditions

T

Wafa Malik , Ramon Isla, Farida Dechmi ⁎

Soil and Irrigation Department (associated to CSIC), Agrifood Research and Technology Centre of Aragon (CITA), Avda. Montañana 930, 50059, Zaragoza, Spain

ARTICLE INFO

ABSTRACT

Keywords: Crop model Maize Model calibration Irrigation management Fertilizer managements Nutrients losses

Maintain high agriculture production levels while reducing environmental impacts is of primordial importance in intensive irrigated areas. Adequate management of water and nitrogen are critical for a sustainable agriculture. The objectives of this work were to (i) calibrate and validate DSSAT model for maize under different nitrogen availability conditions and (ii) assess the effect of best management practices on irrigation water needs and nitrogen losses by leaching using different scenarios in an intensive irrigated area. For model calibration and validation, three field experiments were conducted with a total of 134 plots. Then, the model application was performed in three soil types in the ‘Del Reguero’ watershed (Spain) considering (i) real irrigation applied by farmers and DSSAT automatic irrigation and (ii) a recommended dose of N fertilizer (250 kg N ha−1) compared to a traditional dose of 390 kg N ha−1. Among all plots, the model simulated reasonably well grain yield with a Root Mean Square Error (RMSE) lower than 708 kg ha−1 and high Willmott agreement index (d statistic) (> 0.9). Very similar trends were observed for total biomass and total N uptake with a RMSE of 2018 kg ha−1 and 36.6 kg N ha−1. The prediction of residual nitrate in soil was acceptable with a RMSE of 43 kg N ha−1.Modeling results showed that adjusted irrigation would reduce (on average for different soil types) the total amount of seasonal irrigation water by 31% and the nitrate leaching by 97% without a significant reduction in grain yield. Regarding to N fertilizer scenarios, results showed that farmers can reduce the N fertilizer currently applied leading to a significant decrease in the N leached between 33 and 53% depending on soil types.

1. Introduction Nutrient pollution is currently the major environmental problem of surface water bodies (Vitousek et al., 2009). Reducing nutrient losses from agricultural soils to surface and groundwater resources is one of the greatest challenges facing agricultural systems in intensive irrigated areas. This is especially relevant in maize fields where nitrogen (N) is the most limiting nutrient (Nivong et al., 2007) and farmers tended to overfertilize with N to ensure maximum yields avoiding yield reductions associated to N deficiency (Gaudin et al., 2015; Liu et al., 2012). Moreover, maize has long been recognized as a major contributor of irrigated areas diffuse pollution in the world (Klocke et al., 1999; Pratt et al., 1984) and similarly in several irrigated areas in Spain (Barros et al., 2011; Cavero et al., 2012). Especially in the Ebro River Basin the N pollution has been found to be a concern and in particular in irrigation return flows (Isidoro et al., 2006). Nitrogen is applied at rates of over 300 kg N ha−1 in fields that are only fertilized with mineral N and

at more than 400 kg N ha−1 in those fertilized with manure (Sisquella et al., 2004). Furthermore, Cavero et al. (2003) found that the nitrate loads exported per unit area in the irrigation return flows of two sprinkler irrigation districts predominantly grown to maize, vary, depending on irrigation and N fertilization management, between 18 and 49 kg NO3-N ha−1 year−1. Within this area, the ground waters are frequently polluted with nitrate (Ferrer et al., 2003), with concentrations often exceeding 50 mg NO-3 L−1, the maximum level permitted by the European Union (1991) for drinking water quality. Several experimental studies have been conducted at district level in order to analyze and quantify N contamination induced by irrigation and fertilization management in irrigation return flows (Barros et al., 2011, 2012; Isidoro et al., 2006). However, most of these studies were performed for short periods of time and did not allow identifying the effects of long-term agronomic changes and climate variability on irrigation and N losses trends. Furthermore, there is a need for further studies to assess agronomic practices that would enable these areas to

Abbreviations: N, nitrogen; SMN, soil mineral nitrogen; KM, unit kernel mass; GY, grain yield; TB, total biomass; GNE, grain number per ear; GW, grain weight; DRW, Del Reguero watershed; NUE, nitrogen use efficiency; I_WUE, irrigation water use efficiency ⁎ Corresponding author. E-mail address: [email protected] (W. Malik). https://doi.org/10.1016/j.agwat.2018.10.022 Received 25 January 2018; Received in revised form 11 October 2018; Accepted 17 October 2018 0378-3774/ © 2018 Elsevier B.V. All rights reserved.

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increase the efficiency of their N fertilization while at the same time reducing nitrate leaching from maize plots to water bodies. The key strategy to reduce off-site nutrient pollution is to optimize irrigation and fertilization practices (Cavero et al., 2012; Klocke et al., 1999; Salmerón et al., 2014). Crop models have been developed and used worldwide as operational and strategic research and decision support tools in crop production and resources management (Mourice et al., 2014). However, continuous calibration and validation of a crop model is extremely important when it is applied for new locations with new crop varieties (López-Cedrón et al., 2008). Concerning maize modelling, CERES-Maize included into DSSAT model (Decision Support System for Agrotechnologie Transfer) (Hoogenboom et al., 2010) has been used extensively worldwide (Amarala et al., 2015; Jones et al., 2003) to crop simulation under different management practices and various climatic conditions (Soler et al., 2007), irrigation management (He et al., 2012; Kadiyala et al., 2015; Kovacs et al., 1995; Rötter et al., 2012), yield forecasting (Quiring and Legates, 2008), climate change (Araya et al., 2015; Farhangfar et al., 2015; Ngwira et al., 2014) and fertilizer N management. Li et al., (2015) proved that DSSAT was a useful and reliable tool for simulating long-term trends of maize grain yield, soil organic C and soil organic N based over 14 years from temperate semi-arid to semi-humid weather conditions in Northwestern China. Salmerón et al., (2014) used DSSAT to simulate the impact of including winter cover crops in maize monoculture on yields and N leaching for a range of soil types and irrigation managements during a 14 years rotation crop in La Violada watershed (Spain). The model simulated successfully crop N uptake but was less accurate simulating the evolution of inorganic soil N. Thorp et al. (2007) simulated the longterm effects of different N application rates on maize production and sub-surface nitrate-N concentration in drainage water in Iowa (USA) and concluded that reducing long-term N rates from 180 to 130 kg N ha−1 corresponded to an 18% reduction in N mass lost to water resources. Liu et al. (2011) simulated the crop yield and nitrogen dynamics under 50 continuous years maize production experiment in Canada. Soler et al. (2011) evaluated the performances of the DSSATCSM and CENTURY based soil modules in predicting crop yield and soil organic carbon dynamics for different crop rotations and fertilizer levels using the observed data set from an experiment conducted in a semi-arid region of West Africa during 1993 to 2004 period. However, studies dealing with long-term analysis of irrigation and nitrogen performance at the irrigation district level and its evolution are lacking. Moreover, applying the DSSAT model is a useful for supplementing field experiments allowing to identify the best management strategies in a particular area of study. Therefore, the main objectives of this study were to: (1) evaluate the DSSAT performance to predict maize yield, N uptake (grain and plant) and residual soil mineral N under different soil mineral N availability conditions and; (2) determine the best management practices of irrigation and nitrogen fertilization under different management scenarios in intensive semi-arid irrigated Mediterranean conditions.

Table 1 Soil characteristics in the experimental sites at Montañana and Almudévar for CERES-Maize model calibration and validation. Soil parameters

Montañana (Exp. 1)

Almudevar (Exp. 1 and 2)

Soil depth (m) pH (ext. 1:2.5 H2O) Texture USDA Coarse portion (> 2 mm, %) Organic Matter (0-40 cm, %) Carbonates (%) P Olsen (0-40 cm, mg kg−1) K2O (Amon. ac., mg kg−1)

1.20 8.42 loam 0-20 1.47 37 11 106

> 1.20 7.80 silty-clay-loam < 1 2.09 35 24 300

characterized by Mediterranean semiarid climate, with annual maximum and minimum daily air temperatures of 19.6 and 5.7 °C, in Montañana and 21 and 8.1 °C in Almudévar, respectively. The yearly average precipitation (1981–2010) is 347 mm and 443 mm, and the yearly average evapotranspiration (ET0) is 1230 mm and 1331 mm, in Montañana and Almudévar, respectively. The main soil characteristics for both locations are presented in Table 1. A maize crop (Zea mays L. cv. Pioneer PR34N43) was sown with row spacing of 0.75 m and an average final plant density of 74,000 plant ha−1 in fields managed using conventional equipment. Maize crop was irrigated using solid set sprinkler irrigation system (triangular layout of 18 m x 18 m) using an application rate of 5 mm h−1. The weekly irrigation requirements were calculated from the daily values of reference evapotranspiration (ET0) calculated using the FAO Penman-Monteith equation and the Martinez-Cob (2008) crop coefficients (Kc), following the FAO methodology (Allen et al., 1998). The irrigation efficiency was set to 85%. Main characteristics of crop management and key dates for Exp. 1, 2 and 3 are presented in Table 2. In each experiment, the previous year of the study, three clearly different doses of N fertilizer were applied to a maize crop in different areas of the field to create different initial soil mineral nitrogen (SMN) content. In addition and during the crop season, the plots included in the study received different amounts of N fertilizer, ranging from 0 (control) to 400 kg N ha−1 (overfertilized), providing a wide variation of crop N availability among the different plots (Table 2). Therefore, averaging over experiments, the minimum and maximum mineral nitrogen available estimated as the initial SMN plus the N fertilizer ranged from 93 to 744 kg ha−1. All the ears in 2 rows of 8 m length per plot (12 m2) were hand harvested to determine yield, number of plants, number of grains per square meter, and unit kernel mass (KM) at each experimental plot. The plants contained in an area of 3 m2 were harvested to estimate the total plant biomass. A subsample of grain and plants were dried at 65 °C, weighed and ground prior to analyses of total N by dry combustion method (TruSpec CN, LECO, St. Joseph, MI, USA). Grain and plant total N uptake was calculated as the product of grain or plant biomass and N content. At each experimental plot, the soil was sampled every year

2. Materials and methods

Table 2 General crop management characteristics at the different field trials (Exp. 1, Exp. 2 and Exp. 3) used for CERES-Maize model calibration and validation.

2.1. Experimental data for model calibration and validation Data were collected from three field experiments located in the Ebro Valley (Northeast Spain) in the locations of Montañana (41°44′N, 0°49′W, 225 m a.s.l) and Almudévar (42°02′N, 0°34′W, 400 m a.s.l). These experiments were performed during 2010 in Montañana (Exp. 1), 2011 and 2012 in Almudévar (Exp. 2 and Exp. 3, respectively) and were part of a study to evaluate the comparison of different strategies in order to manage the nitrogen fertilizer in irrigated areas (Isla et al., 2012). A total of 38 plots from Exp. 1, 47 from Exp. 2 and 49 from Exp. 3 were used in this study for CERES-Maize calibration and validation processes. In each experiment, the elementary plot size was 4.5 m by 12.5 m with a total of 6 plants rows per plot. The study area is 299

Item

Exp. 1

Exp. 2

Exp. 3

Sowing date Harvest date First sidedress date Second sidedress date Initial soil mineral N (0-30 cm, kg N ha−1) Initial soil mineral N (0-60 cm, kg N ha−1) N treatments (n) N fertilizer range (kg N ha−1) Grain yield (0% humidity) range (Mg ha−1) Grain yield (0% humidity) average (Mg ha−1)

May 10 Oct. 7 Jun. 29 Jul. 23 14–172 23–341 20 0–400 2.63–11.81 9.28

Apr. 19 Oct. 4 Jun. 9 Jul. 12 40–196 82–371 14 0–300 7.25–14.58 12.58

Apr. 26 Oct. 3 Jun. 6 Jul. 16 – – – 0–300 6.54–13.86 12.10

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after maize harvest. Three soil samples from each experimental plot were taken using a 5 cm diameter hand auger and the three samples were combined for each 0.3 m until 1.2 m depth. The soil was freshsieved to pass a 2 mm sieve, and 10 g were extracted with 30 mL of KCl 2 N solution for determination of NO3−–N and NH4+–N concentrations by colorimetry with a continuous flow analyzer (AA3, Bran + Luebbe, Norderstedt, Germany). Another soil subsample was dried at 105 °C to constant weight for gravimetric water content determination. Gravimetric water content was converted to volumetric water content using a bulk density of 1.5 g cm−3.

x i)2

(yi

RMSE =

(2)

n

Normalized RMSE (nRMSE) expressed as the ratio between the RMSE and the average of observed data. The model simulations were considered excellent, good, fair, and poor based on the nRMSE values of < 10%, 10–20%, 20–30%, and > 30% proposed by (Kadiyala et al., 2015);

nRMSE =

RMSE X 100 x¯

(3)

¯

where x is the observed average. Index of agreement or d statistic (d; Willmott, 1982), computed as follows:

2.2. Model calibration and validation The version DSSAT 4.5 was used in this study (Hoogenboom et al., 2010). CERES-Maize cultivar calibration requires the estimation of six genetic coefficients: P1 (thermal time from emergence to end of juvenile phase), P2 (delay in development with photoperiod above 12.5 h), P5 (thermal time from silking to physiological maturity), G3 (potential kernel growth rate), G2 (potential kernel number per plant), and PHINT (phyllochron interval). The model calibration was performed using crop data obtained under non-limiting growth conditions using 20 overfertilized plots from Exp. 2 (9 plots) and Exp. 3 (11 plots). Initially, the cultivar coefficients of the same maize cultivar previously calibrated by Salmerón et al. (2014) under similar environmental conditions were tested and considered as a starting point for our calibration process. Then, an iterative approach was used to obtain acceptable genetic coefficients through trial and error adjustments until an adequate match between measured and simulated productivity crop data was found. The soil fertility factor (SLPF) was also manually adjusted as it is an input parameter that affects the overall growth rate of simulated total biomass by modifying daily canopy photosynthesis and is attributed to soil fertility differences and soil based pests. A calibrated value of 0.69 was applied in Exp. 1 based on the best fit between potential simulated yield under adjusted condition and observed values. In addition, the N concentration coefficient (CTCNP2) is an input parameter defined in the species file related the change of plant N concentration as a function of growth stage. As recommended by Liu et al. (2012) the CTCNP2 was also calibrated in the CERES-Maize model to ensure the accuracy of the model simulations for both grain and biomass N uptake. The ecotype coefficients values considered in the model were DSGFT = 180 (growing degree days from silking to effective grain filling period); RUE = 2.8 (radiation use efficiency) and light extinction coefficient (KCAN) in the maize ecotype file was set to 0.5, as this value improved CERES-Maize simulations in northwestern Spain (LópezCedrón et al., 2008) and is similar to that measured under similar growing conditions (Cavero et al., 2000). The method of Penman–Monteith-FAO 56 (Allen et al., 1998) was selected in CERES-Maize to compute daily potential evapotranspiration. Once the model was calibrated, it was validated using the field data from the rest of the three experiments no considered in the calibration process (114 plots in total). The model was evaluated for grain yield (GY), total biomass (TB), grain number per ear (GNE), grain weight (GW), grain N uptake, total biomass N uptake (grains + stems + leaves) and residual nitrate in the soil after maize harvest. To assess the performance of the model, the following parameters were considered: The Bias, defined as the difference between simulated and observed values;

Bias = 1/ n

n i=0

(yi

x i)

d=1

(yi

x i )2 /

( |yi

x¯| + |x i y¯i |) 2

(4)

Coefficient of determination (R2) of the linear regression between simulated y observed values. The statistical significance levels considered were: “ns” to indicate no significant (P > 0.05); “*” to indicate 0.05 ≥ P > 0.01; “**” to indicate 0.01 ≥ P > 0.001 and “***” to indicate P ≤ 0.001. 2.3. Model application and management scenarios Once the model was successfully calibrated and validated, the CERES-Maize was applied to determine the best management practices for maize crop in ‘Del Reguero’ watershed (DRW) (41°54 N and 3°34 W) by assessing the effect of different irrigation and nitrogen management scenarios. Del Reguero stream is an affluent of the Alcanadre river located in the left bank of the middle Ebro river basin in Spain. DRW is a sprinkler-irrigated area of 1865 ha in which maize is the predominant summer crop. According to Skhiri and Dechmi (2012), the irrigation coefficient of uniformity (CU) of this area ranged between 60% and 94%, with an average value of 79%. According to Clemmens and Dedrick (1994), this CU average value was not considered acceptable. Three different type of soils can be found in DRW: (1) Terrace soils, which represent 38% of the total area, characterized by shallow depth (0.6 m on average), presence of calcareous horizon, and high content of stones, (2) shallow alluvial soils, mostly stone-free and with soil depth varying from 0.6 m to 0.9 m and (3) deep alluvial soils, similar to the precedent but with soil depths ranging from 0.9 to 1.2 m. A more detailed characterization of the study area and the soils can be found in Skhiri and Dechmi (2012). 2.3.1. Irrigation management scenarios Irrigation simulations were schedule using two approaches for the three different soils. In the first approach, a set of irrigation schedules based on actual maize irrigation calendar (dates and irrigation doses) applied by farmers during the 2008 and 2009 irrigation seasons. In the second approach, an adjusted irrigation scheduled by using the “automatic irrigation”, option included in the DSSAT model in which the crop is irrigated when it is needed to avoid water stress (when 70% of the available water in the root zone was depleted, supplemental irrigation was triggered to refill the soil reservoir). For both scenarios, the model was run considering the common values in the study area of sowing date (April 15th) and plant density (85,000 plants ha−1). The 2008 and 2009 daily meteorological records were obtained from a meteorological station located close to the watershed (41°56′N and 00°08′W). Farmer surveys were carried out in 2008 (16 farmer surveys) and 2009 (17 surveys) to assess the dates of the first and the last irrigation event, number of irrigations, number of days between two irrigations and volume of water applied for each type of soil. As the difference in irrigation water use between soil type was not significant in the study area (Skhiri and Dechmi, 2012) and all the irrigation schedules were quite similar for the farmers interviewed, only one representative farmer’s irrigation management practice (date and

(1)

where n is the number of observed values, y i and x i are simulated and observed values for the ith data pair. The root mean square error (RMSE) between simulated and observed values computed as: 300

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dose of each irrigation event) was considered to simulate the current farmer practices in each year. Rather than using average values from the surveys, this representative irrigation schedule was provided by the Alconadre Irrigation District collective irrigation network. Other farmers management practices necessary as inputs to run the model were obtained from the same farmers’ surveys conducted during the considered years (2008 and 2009). The main information collected from the farmers’ surveys was relative to the type and the amounts of organic and inorganic fertilizers applied, the dates of application and the crop yields obtained. In this case, average values were considered for each study case. The analyzed variables were grain yield (kg ha−1), nitrogen uptake by the plant (kg N ha−1), nitrate leached below the root zone (kg N ha−1), and the amount of residual nitrate in the soil (kg N ha−1).

3. Results and discussion 3.1. DSSAT model calibration Maize cultivar Pioneer “PR34N43″ was previously calibrated by Salmerón et al. (2014) in the same region and the obtained cultivar coefficients were: 243, 0.6, 837, 959, 6.77 and 51.2 for P1, P2, P5, G2, G3 and PHINT, respectively. Applying these genetic coefficients to our experimental fields gave a RMSE of 1400 kg ha−1 which is considered high and therefore not acceptable results for an accurate model scenarios application. Thus, an adjustment of those cultivar coefficients was done in order to obtain more accurate results. The new calibrated genetic coefficients resulted are 243, 0.2, 800, 800, 8.75, and 59.9 for P1, P2, P5, G2, G3 and PHINT respectively. The model overestimated the grain yield in both experiment considered (Exp. 1 and 2) with positive Bias and the average of observed and simulated grain yield were 13001 and 13331 kg ha-1, respectively. The model performance was considered good with a RMSE of 553 and 663 kg ha-1 in Exp. 1 and 2, respectively. These values are in the range of other published works such as Salmerón et al. (2014) who obtained a value of 530 kg ha -1. Moreover, same trend was observed comparing the nRMSE averaging the two experiments (6%) to that obtained by Liu et al. (2012) (nRMSE = 4.3%). These results confirmed that the CERES-Maize cultivar was successfully calibrated in the study area with a reliable result.

2.3.2. Nitrogen fertilization management scenarios Two fertilization approaches were compared (actual vs. optimized) during 10 years (from 2003 to 2014) with an adjusted irrigation amounts to crop requirements. First, an optimal recommended rate of 250 kg N ha−1 (Isla et al., 2008) was splitted as 50 kg N ha−1 at preplanting (April), 100 kg ha−1 in the first sidedress (15th June), and 100 kg N ha−1 in the second sidedress (10th July). Second, an actual rate of 390 kg N ha-1 (averaged rate according to farmers’ surveys performed in DRW corresponding to 2008 to 2012 crop seasons) was splitted in three applications of 70, 250, and 70 kg N ha−1 at preplanting (April), first sidedress (beginning of June) and second sidedress (beginning of July), respectively. For both approaches, three different scenarios of initial SMN were considered to take into account different potential conditions at pre-planting soil mineral N,: (1) low level of mineral N (varied from 50 to 70 kg N ha−1 for shallow soil to deep soils), (2) medium level of mineral N (varied from 100 to 140 kg N ha−1 for shallow soil to deep soils) and, (3) high level of mineral N (varied from 150 to 210 kg N ha−1 for shallow soil to deep soils). In total, 180 simulations were performed considering 3 N mineral levels in the soil before maize seeding, 3 soil types, 2 fertilization management approaches, and 10 years. For model input data of each considered year, an adjusted irrigation schedule was determined by adjusting irrigation water to the crop net irrigation requirement (NIR). The NIR was increased by 10% in order to take into account different potential losses. The daily NIR for maize was calculated as follow: NIR (mm) = [(Kc × ET0)−Pe] × 1.1

3.2. DSSAT model validation 3.2.1. Yield and yield components Validation results indicate an excellent agreement between simulated and observed maize grain yield of the three experiments with an average of RMSE and nRMSE of 703 kg ha−1 and 7%, respectively and with high D-statistic (d > 0.9) (Table 3). In general, the model tended to overpredict grain yield with a positive Bias. Analyzing each experiment separately, results show also an excellent performance of CERES-Maize to simulate grain yield with a RMSE (nRMSE) of 811 (9%), 764 (6%), 535 (5%) kg ha−1 in Exp. 1, 2 and 3, respectively. Similar trend was observed for total biomass by averaging all the experiments, with RMSE (nRMSE) of 2018 kg ha-1 (10%). Also, the model tended to overpredict the total biomass. For both yield variables, the coefficients of determination (R2) pooling data of all experiments were considered good (higher than 0.9 for grain yield and 0.81 for total biomass). The slope of

(5)

Table 3 DSSAT model performance (RMSE, nRMSE, d-statistic and Bias) of grain yield, total biomass, total N uptake, grain number per ear (GNE) and grain weight (GW) in Exp. 1, 2 and 3 experimental fields (n = 38 for each Exp.).

where ET0 is the reference evapotranspiration, Kc is the crop coefficient and Pe is the effective precipitation. Daily values of Pe were considered equal to 75% of precipitation. The ET0 was calculated using the FAO Penman–Monteith method described by Allen et al. (1998) and Kc values were obtained from Martínez-Cob et al. (1998). Once the daily NIR was calculated for each year, the daily NIR values were added until the day on which the sum amounted to an accepted value as an irrigation dose. In general, the irrigation frequency considered was 2 to 3 times per week and was increased during the maize critical stages to the water stress. Two indices were used to evaluate management practices in the DRW for irrigation and nitrogen scenarios: (1) Irrigation water use efficiency (I_WUE) defined as the ratio between maize grain yield and irrigation water applied during the crop season per hectare (kg mm−1). (2) Nitrogen use efficiency (NUE) expressed as the ratio between maize grain yield and the fertilizer N amount applied per hectare (kg kg−1 N).

Observed

Simulated

RMSE

nRMSE

d-statistic

Bias

9 6 5

0.98 0.94 0.97

139 161 13

13 9 7

0.88 0.87 0.91

1506 818 586

39 7 12

0.58 0.91 0.89

−119 −12 −46

13.3 12.6 12.7

0.75 0.75 0.74

38 −38 28

18 22 12

0.88 0.81 0.92

23 36 −9

−1

Exp. 1 Exp. 2 Exp. 3 Exp. 1 Exp. 2 Exp. 3 Exp. 1 Exp. 2 Exp. 3 Exp. 1 Exp. 2 Exp. 3 Exp. 1 Exp. 2 Exp. 3

301

Grain Yield (kg ha ) 9042 9419 811 12580 12742 764 12108 12122 535 Total Biomass (kg ha−1) 17426 18932 2218 23640 24459 2119 22955 24145 1715 −1 GNE (n ear ) 427 320 168 519 507 38 575 529 68 GW (g) 0.324 0.371 0.043 0.358 0.320 0.045 0.274 0.303 0.035 Total N uptake (kg N ha−1) 178 206 31 220 257 49 255 246 30

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Fig. 1. Relationships between simulated and observed (A) grain yield (GY), (B) total biomass (TB), (C) grain number per ear (GNE), (D) grain weight (GW), (E) total N uptake with CTCPN2 = 0.16, and (F) total N uptake with CTCPN2 = 0.25 in Exp. 1, 2 and 3. The dashed line represents the 1:1 relationship.

yield prediction with a higher RMSE (1945 kg ha−1) and nRMSE (19.2%). Regarding the grain number per ear (GNE) results, the corresponding Bias values presented in Table 3 indicated that the CERESMaize underestimated this yield component. Better results were obtained in Exp. 2 and Exp. 3 with a RMSE of 38 and 68 respectively and the nRMSE were lower than 12% in both experiments. However, in Exp. 1, a higher RMSE was observed (168). On average, the observed GNE in Exp. 1 was lower than the remaining experiments (Exp. 2 and Exp. 3) indicating some crop limiting conditions. The same behavior was observed for the simulated GNE in each experiment. This difference could be compensated by the grain weight (GW) for predicting final grain yield. Results indicate also that the model overestimated GW in Exp. 1 and Exp. 3 but underestimated such variable in Exp. 2. Moreover the RMSE for GW was reasonably acceptable ranging between 0.035 and

the regression between simulated and observed values was significantly (P < 0.05) different from one in all cases (Fig. 1, A and B). The best fit between observed and simulated values for both grain yield and total biomass was obtained in Exp. 3 compared with the other locations (Table 3). Subsequently, CERES-Maize model could be reliably used for predicting maize grain yield and total biomass supported by the obtained statistics in our study compared with other published studies. For instance, Yakoub et al. (2017) found higher RMSE than obtained in our results for both grain yield and total biomass with a 1313 kg ha−1 and 3324 kg ha−1 respectively. In addition, a lower d-statistic value (0.8) and a higher nRMSE (9.5%) were observed for grain yield and a lower d-statistic of 0.7 and nRMSE of 11.4% for biomass. Furthermore, similar performance of CERES-Maize model was obtained for grain yield by Liu et al. (2012) in Northeast China with nRMSE value of 5%. Results obtained by Liu et al. (2011) showed also less accuracy for grain 302

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Yakoub et al. (2017) who found a RMSE and d-statistic of 41 kg N ha−1 and 0.88, respectively. An accurate prediction of plant N content depends on the correct calculation of total biomass and N concentration in plant tissues. This result can be associated with the overestimation of the total biomass already described previously. Yakoub et al. (2017) suspected that the CERES-Maize simulated N concentration in the grain is higher than grain N concentration of modern hybrids. Moreover, many reports indicate that due to the efforts to increase grain yield by genetic improvement, grain N content of recent maize hybrids have decreased compared to older materials (Duvick, 2005). Other studies (Bowen et al., 1993) also showed that CERES-Maize model overestimated the plant N uptake due to an overestimation in the release of mineral N from soil organic matter. In some studies such as Asadi and Clemente (2003) and Carberry et al. (1989) also found a tendency of the CERES-Maize to overestimate the N uptake in maize.

Table 4 CERES-Maize model performance (RMSE, nRMSE, and D-statistic and Bias) of residual nitrogen in soil in each 30 cm layer to 120 cm depth at Exp. 1, 2 and 3, experimental fields (n = 38 in each experiment). Residual N (kg ha−1) Depth (cm) Exp. 1 0-30 30-60 60-90 90-120 Exp. 2 0-30 30-60 60-90 90-120 Exp. 3 0-30 30-60 60-90 90-120

Observed

Simulated

RMSE

d-stat

Bias

53 55 51 51

7 5 5 13

69 67 59 50

0.57 0.62 0.65 0.64

−46 −50 −46 −37

46 28 24 32

9 27 54 75

41 47 46 53

0.71 0.44 0.73 0.77

−37 −1 30 43

41 20 13 11

11 29 24 22

38 14 15 15

1 0.84 0.79 0.78

−30 9 11 11

3.2.3. Soil residual nitrate Statistics of residual nitrate-N analyzed by 0.30 m depth intervals upon to 1.20 m are presented in Table 4. A reasonable performance of residual nitrate was observed in the location of Exp. 3 with a RMSE varying between 14 and 38 kg N ha−1. Moreover, a high d-static was obtained (greater than 0.7) and in this experiment the model overestimated the residual nitrate-N in all layers except in the first layer (0.3 m). Meanwhile, in Exp. 1 and Exp. 2 the model underestimated the residual nitrate-N in soil except in the deeper layers (0.6–1.2 m) of Exp. 2. The RMSE ranged from 50 to 69 kg N ha-1 in Exp. 1 and from 41 to 53 kg N ha−1 in Exp. 2. This suggests inadequate estimation of N root absorption by the model in the surface layer (up to 0.3 m) or difficulties to estimate N mineralization rates.

0.045 g A good agreement was observed for the GNE with R2 greater than 0.8 however a small R2 (0.2) in the case of GW (Fig. 1, C and D). 3.2.2. Nitrogen uptake Results indicate a good agreement between simulated and observed total N grain uptake (Fig. 1, E). The CERES-Maize model simulated reasonably well the total N uptake with an average of RMSE and nRMSE across all the experiments of 37 kg ha−1 and 17% respectively (Table 3). Analyzing the three experiments separately the model overestimated the total N uptake in Exp. 1 and Exp. 2 while underestimating it in Exp. 3. In addition, a good d-statistic was obtained (greater than 0.8) in each field experiment. A significant relationship was observed between simulated and observed total N uptake with a R2 of 0.57 (Fig. 1, E). The most accurate prediction of plant N uptake was obtained in Exp. 3 with a RMSE of 30 kg N ha−1 and a d-statistic of 0.92 which is very similar to the study of Salmerón et al. (2014) who found a RMSE of 25 kg N ha−1. However, our results are better than those obtained by

3.2.4. Improving total N uptake estimation Following Liu et al. (2012) suggestion to improve the plant N estimation, the new results derived from the calibrated CTCNP2 value (CTCNP2 = 0.25) indicated that the RMSE of grain N uptake was improved by 34% while total plant N uptake by 10% considering all the plots. The RMSE decreased from 28 to 20 kg N ha−1 and from 36 to 32 kg N ha−1 for grain and total biomass N uptake, respectively. Nevertheless, there is no significant difference on RMSE of grain yield

Fig. 2. Comparison between (A) irrigation applied by farmers and simulated water applied according to DSSAT model and (B) grain yield simulated by DSSAT under real irrigation doses and simulated irrigation doses. The comparisons are presented for the three different soil types and the two considered years. 303

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Table 5 Simulated values of irrigation water use efficiency (kg of grain yield mm−1 of irrigation water) and volume of water drained (mm) under real and adjusted irrigation dose for the different soil types during 2008 and 2009 crop seasons. Terrace

Real farmer’s irrigation DSSAT automatic irrigation Real farmer’s irrigation DSSAT automatic irrigation

Shallow alluvial

2008 2009 2008 ——— Irrigation Water Use Efficiency (kg mm−1) —— 19 14 19 29 19 31 —————— Volume of drainage water (mm) ——————— 189 154 167 14 2 3

(an increase of the RMSE by 153 kg ha−1). Also, Figs. 1E and F shows the same relationship between simulated and observed total N uptake. Regarding residual N, the RMSE pooling all the plots (from 0 to 1.2 m) is reasonably acceptable with 47 kg N ha−1 using the new calibrated CTCNP2 value meanwhile a RMSE of 43 kg N ha−1 was obtained using the standard value defined in the species file (CTCNP2 = 0.16). Therefore, there is a slight differences between value of CTCNP2 regarding the residual N in soil after harvest that vary with depth. A better adjustment of CTCNP2 parameter to specific environmental conditions is important to improve maize N uptake estimation. However, the N plant uptake improvement are not clearly reflected on the residual nitrogen in the soil due to the complexity of the nitrogen balance in the soil and other parameters that may be mainly due to the model's lack of sensitivity to small changes in the soil nitrogen balance and the simulation errors generated in each plot.

Deep alluvial 2009

2008

2009

14 19

19 31

14 20

136 0

138 0

112 0

862 mm of water irrigation for maize crop during 2008 and 2009 seasons, respectively. However, the application of the model using ‘automatic irrigation’ provided a total of 518 and 646 mm for the same seasons, respectively. The higher water requirements estimated by the model during the 2009 were associated to a lower precipitation (P = 208 mm) in comparison with 2008 irrigation season (P = 292 mm). These results indicate the possibility to reduce a significant amount of irrigation water (313 and 216 mm in 2008 and 2009, respectively) when the irrigation is adjusted to the real evapotranspiration needs. Although there is an “online” irrigation service from the regional government that allows farmers to calculate the weekly water irrigation needs according to the FAO methodology, it is barely used by maize growers and the tendency is to over irrigate the maize crop. On the other hand and for each year, the DSSAT model simulated similar irrigation doses for the three different soil types (Fig. 2A). This is because the model applies the needed water to re-fill the soil profile until field capacity, increasing the frequency of irrigation events for soils having less water capacity retention as the terrace soils, so the total dose is not affected. Thus, the model simulated a total of 23

3.3. DSSAT model application 3.3.1. Irrigation management scenarios On average, and according to surveys, farmers applied 818 and

Fig. 3. Simulated values of (A) nitrogen (kg N-NO3 ha−1) leached below the root zone, and (B) soil residual nitrate (kg N-NO3 ha−1) for the different soil types and the two considered years. 304

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Fig. 4. Simulated (A) maize grain yield during the 2004–2013 period under the two N fertilizer scenarios (actual doses vs adjusted doses) and (B) 10-year average of total plant nitrogen uptake under the different soil types and initial soil mineral nitrogen conditions considered. The vertical bars in Fig. B indicate the standard deviation (n = 10).

irrigations to the Terrace soils and 20 irrigation events for alluvial soils. Results indicated no significant differences in simulated maize grain yields between real irrigation and DSSAT automatic irrigation doses across the different years and soil types (Fig. 2B) indicating that the model estimates similar yields using less amounts of irrigation water. Therefore, adjusting irrigation taking into account the soil water holding capacity and the actual evapotranspiration values allows increasing the water use efficiency compared to the real management of maize growers in the watershed (Table 5). Averaging across soil types, I_WUE corresponding to real situation was 19 kg mm−1 and 14 kg mm−1 for 2008 and 2009, respectively while I_WUE of the adjusted irrigation scenario was 30 kg mm−1 and 19 kg mm−1 for 2008 and 2009, respectively. In addition, deep percolation simulated using actual irrigation was on average 165 mm and 134 mm for 2008 and 2009, respectively while with the automatic irrigation option, the volume of water drained below the root zone was very small or zero depending on soil types and years (Table 5). The amount of total N uptake by maize presented similar value of 274 kg N ha−1 for both irrigation managements in 2009 and a very small difference in 2008 (291 kg N ha−1 for actual situation vs. 294 kg N ha−1 for adjusted irrigation scenario). However, nitrate leaching was high in the scenario of real irrigation management, ranging from 90 to190 kg NO3 ha−1, depending on soil type and year (Fig. 3A). The model showed lower N leaching in the deep alluvial soils compared to the shallower soils in the case of actual irrigation practices. The average nitrate loss below the root zone (including all soils considered) in the case of actual irrigation management was 34 kg ha−1 NO3-N. This amount is similar to that measured in the same region where annual amounts of nitrogen losses by drainage ranged between 25 to 50 kg NO3-N ha−1 under well managed sprinkler irrigated systems (Tedeschi et al., 2001; Cavero et al., 2003). However, it is important to mention that the presented results includes only N losses during the crop season period (April to October) while the cited studies included N annual

Fig. 5. 10-year average (2004–2013) of (A) simulated N leached during the growing season and (b) simulated residual soil mineral N for the two N management scenarios and under the different soil types and initial soil mineral nitrogen conditions. The vertical bars indicate the standard deviation (n = 10). 305

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Fig. 6. 10 years average (2004–2013) of (A) cumulative leached N and residual N in soil and (B) Cumulative residual N in soil.

losses. The combined lower N leaching and similar crop N uptake in the automatic irrigation scenario has leaded to higher soil residual nitrate (Fig. 3B) in all cases. For automatic irrigation scenario, values were similar for both year and soil type, and higher for alluvial soils (206 kg NO3 ha−1 in average) than terrace soils (154 kg NO3 ha−1 in average). In the case of real irrigation management and all soil types, the residual nitrate was 33 kg NO3 ha−1 in average. The automatic irrigation management results show very small N-leaching losses in deep soils, while the real farmer’s irrigation management induces important subsurface N-leaching losses below the root zone during maize growing season.

is greater than or equal to Nc, growth proceeds without any N limitation. However, if the concentration of N falls below Nc, then the crop experiences N deficit and growth is reduced accordingly. At the same time, the N uptake demand is limited by a maximum N concentration. Subsequently, the N uptake varies between critical N and maximum N. According to the model and in our study, soil N supply was greater than the critical N, across all soil types and scenarios, and therefore no significant difference in grain yield were found. Nevertheless, the amounts of N uptake is significantly affected by the availability of soil mineral N. The leached nitrate during the growing season increased proportionally to the initial amounts of soil mineral nitrogen (low, medium and high) and decreases with soil depth (Fig. 5A). Adjusting the N rate, the total mass of N leached was 58, 15, and 3 kg NO3 ha−1 for the terrace, shallow alluvial and deep alluvial soils, respectively. This results point out the importance of adequate N management in shallow soils to decrease the negative environmental effects of N leaching. As 38% of soils in the DRW are terrace soils (0.6 m depth), most of the N losses occur mainly in those soils. Averaging across soil types and initial soil mineral nitrogen (SMN), the simulated residual soil mineral N range from 174 kg NO3 ha−1 in the traditional N management scenario (rate of 390 kg N ha−1) to 52 kg NO3 ha−1 (adjusted rate of 250 kg N ha−1) (Fig. 5B). The alluvial soils have similar values of residual SMN but higher than terrace soils. The Figs. 6A and B shows the cumulative leached and residual nitrate-N in soil over10 year’s seasonal maize crop. Both of these cumulative variables are based on the sum of the final values after each maize

3.3.2. Nitrogen fertilizer scenarios According to the model, the differences in grain yield between real (traditional) fertilization and recommended fertilization (adjusted) were not significant (Fig. 4A). The average of simulated crop yield for the real fertilization management was 14041 kg ha−1 while with the adjusted fertilization scenario the yield was 14028 kg ha−1. According to the simulations performed during the 2004–2013 period under the two N fertilization scenarios (Fig. 4B), plant N uptake tended to increase as the amount initial soil mineral nitrogen increased. No increase was observed in the scenario of real farmer’s dose probably due to an excess of N availability in all situations. N uptake is simulated in CERES-Maize by contrasting potential soil N supply with crop N demand. To estimate crop N demand, the model compares the concentration of N in plant tissues with a target concentration called critical N (Nc). The model assumes that if the concentration of N in tissues 306

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harvest. We assumed that the cumulative variables represent the minimum amounts of leached nitrate-N and residual nitrate-N because the nitrate-_N released from mineralization processes was not considered during the winter intercrop periods. Long term analysis of the cumulative leached N after 10 years simulations shows that the leached N is always higher in the real farmer’s fertilization than in the adjusted fertilization. Negligible amount of leached N for the deep alluvial soil was observed and therefore was not included in the Figure 6 A. On the other hand, a remarkable discrepancy between the two managements was observed for cumulative residual N with more than 1500 kg N ha−1 after 10 years (Fig. 6B). Similar behavior was observed for the different soil types but it was more pronounced in alluvial soils. The traditional behavior of fertilization followed by farmers in the study area shows a serious problem of management practices that leads to environmental contamination problems and a negative economic effect due the unnecessary expenses in N fertilizer. Besides, nitrogen efficiency was significantly higher (56 kg grain kg−1 N applied) in the scenario of adjusted N, fertilization compared to the traditional scenario (36 kg grain kg−1 N applied) performed by farmers in the study area. Testing SMN levels before seeding and during early stages of maize growth can help to obtain more accurate N fertilizer recommendations (Cela et al., 2013) taking into account the already available SMN in the soil derived from mineralization or from preceding crops. Soils with high SMN after harvest can supply part of the nitrogen needed by maize in the following crop year. The proposed adjusted rate of 250 kg N ha−1 used in this study is a good starting point derived from regional studies but can be refined or site-specific adjusted using soil or plant analysis during the maize growing season.

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4. Conclusions In this work, we evaluated first the performance of the DSSAT CERES-Maize model to predict grain yield, total biomass, grain number per ear, kernel weight, crop N uptake, under a broad range of soil mineral nitrogen availability conditions. A good performance of the model was demonstrated in this study by a RMSE less than 703 kg ha−1 and 37 kg N ha−1 for grain yield and total N uptake among all the experiments. A good agreement was obtained for total biomass, yield components (GNE and grain weight) and reasonably accuracy for residual N in soil. The application of calibrated and validated model is a useful tool to assess management practices for reducing N leaching in intensive irrigated areas. The simulation of different scenarios of water management indicate that irrigation management could be significantly improved adjusting the irrigation water applied according the actual evapotranspiration needs instead a fixed irrigation calendar normally used by most maize growers in the area. This adjustment would reduce (on average for different types of soil), the total amount of seasonal irrigation water by 31% and the nitrate leaching by 97% without a significant reduction of grain yield. Similarly, the simulation of different N management scenarios showed the feasibility to reduce significantly the N fertilizer dose currently applied to maize by farmers, maintaining grain yield. This reduction in the N fertilizer decrease the N leached between 33 and 53% depending on soil types. Even though the DSSAT have showed some uncertainties associated with inputs and model parameters, the presented results support the potential of the model to simulate crop growth and soil N dynamics under various agricultural practices in the semi-arid irrigated conditions. Acknowledgements The Ministry of Innovation, Science of Technology of Spain (projects AGL2009-12897-C02-02 and AGL2013-48728-C2-2R) has financed this work. Thanks are also given to field and laboratory personnel of the Soils and Irrigation Department of C.I.T.A. Wafa Malik was granted with a fellowship from the IAMZ-CIHEAM and the Spanish National Research Plan. 307

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