Next Article in Journal
Effects of Composite Resin on the Enamel after Debonding: An In Vitro Study—Metal Brackets vs. Ceramic Brackets
Next Article in Special Issue
Denitrification in Intrinsic and Specific Groundwater Vulnerability Assessment: A Review
Previous Article in Journal
Intelligent Stretch Optimization in Information Centric Networking-Based Tactile Internet Applications
Previous Article in Special Issue
Carbon Availability and Nitrogen Mineralization Control Denitrification Rates and Product Stoichiometry during Initial Maize Litter Decomposition
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Applied Sciences
Volume 11
Issue 16
10.3390/app11167352
applsci-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Extreme Weather Events Affect Agronomic Practices and Their Environmental Impact in Maize Cultivation

by
Monika Marković
1,*,
Jasna Šoštarić
1,
Marko Josipović
2 and
Atilgan Atilgan
3
1
Faculty of Agrobiotechnical Sciences Osijek, University of Josip Juraj Strossmayer of Osijek, 31000 Osijek, Croatia
2
Agricultural Institute Osijek, 31000 Osijek, Croatia
3
Faculty of Engineering, Alanya Alaaddin Keykubat University, Alanya 07425, Turkey
*
Author to whom correspondence should be addressed.
Appl. Sci. 2021, 11(16), 7352; https://doi.org/10.3390/app11167352
Submission received: 30 June 2021 / Revised: 3 August 2021 / Accepted: 7 August 2021 / Published: 10 August 2021
(This article belongs to the Special Issue Denitrification in Agricultural Soils II)
Browse Figures
Versions Notes

Abstract

:
Sustainable and profitable crop production has become a challenge due to frequent weather extremes, where unstable crop yields are often followed by the negative impacts of agronomic practices on the environment, i.e., nitrate leaching in irrigated and nitrogen (N)-fertilized crop production. To study this issue, a three-year field study was conducted during quite different growing seasons in terms of weather conditions, i.e., extremely wet, extremely dry, and average years. Over three consecutive years, the irrigation and N fertilizers rates were tested for their effect on grain yield and composition, i.e., protein, starch, and oil content of the maize hybrids; soil N level (%); and nitrate leaching. The results showed that the impact of the tested factors and their significance was year- or weather-condition-dependent. The grain yield result stood out during the extremely wet year, where the irrigation rate reduced the grain yield by 7.6% due to the stress caused by the excessive amount of water. In the remainder of the study, the irrigation rate expectedly increased the grain yield by 13.9% (a2) and 20.8% (a3) in the extremely dry year and 22.7% (a2) and 39.5% (a3) during the average year. Regardless of the weather conditions, the N fertilizer rate increased the grain yield and protein content. The soil N level showed a typical pattern, where the maximum levels were at the beginning of the study period and were higher as the N fertilizer rate was increased. Significant variations in the soil N level were found between weather conditions (r = −0.719) and N fertilizer rate (r = 0.401). Nitrate leaching losses were expectedly found for irrigation and N fertilizer treatments with the highest rates (a3b3 = 79.8 mg NO3− L).

1. Introduction

After wheat, maize (Zea mays L.) is the second-most-produced crop within the European Union [1]. As for the Republic of Croatia, during the last decade (2010–2019), the average maize yield was 6.7 t ha−1 [2], where the lowest yield was achieved in dry years (2011 = 5.7 t ha−1 and 2012 = 5.3 t ha−1), while the highest yield was obtained in wet growing seasons in 2010 (7 t ha−1) and 2014 (8.1 t ha−1). It is important to emphasize that in the years with the highest maize yield, the amount of rainfall during the growing season was 68.4% (2010) and 42.2% (2014) higher than the long-term average (LTA, 1961–1990 = 368 mm). In the remaining years of the mentioned decade, the rainfall deficit was in the range from −33% (2011) to −13% (2017), whereby the lack of rainfall had to be compensated for using irrigation. Despite the considerable need, in the Republic of Croatia, only 1570 ha of agricultural land sown with maize is irrigated [3]. This data refers to the 2013–2017 period in Croatia, during which, maize was sown on 260818 ha, which indicates insufficient implementation of this agronomic practice. The analysis of irrigated maize production on a global scale was conducted by Zhang et al. [4]. An interesting study based on 162 publication results showed that maize grain yield (GY) was increased by 30.35% (7357 kg ha−1 to 9512 kg ha−1) on average. In addition, the authors stated that the water productivity in irrigated maize production was increased by 9.91% (19.1 to 20.5 kg ha−1 mm−1) and emphasize that these increases in maize yield and water productivity varied depending on the seasonal irrigation amounts, precipitation levels, annual average temperature, nitrogen application, soil organic matter, and bulk density.
Another indicator of changing weather patterns is rising air temperature. Previous studies [5,6,7] have emphasized the negative impact of increasing air temperature on maize yields in different parts of the world. As far as Croatia is concerned, over the past decade, the deviation in air temperature from the LTA has been in the range of 0.7 °C (2014) to 2.57 °C (2018). These data refer to the growing period of summer crops, where the analysis of maize yield concerning drought and high air temperatures showed a greater association between maize yield and rainfall amount (drought) than high air temperatures. This statement is in agreement with Basso and Ritchie [8], who claimed that the major and consistent cause of rain-fed maize yield reductions in the humid and sub-humid US corn belt is the prolonged absence of significant rainfall and the resulting soil water deficit, i.e., not excessive air temperatures. Thus, a study conducted by Bolaños and Edmeades [9] emphasized the importance of air temperatures on maize GY and yield formation. The authors stated that heat stress results in a significant reduction in GY, which is associated with a reduction in kernel size. Furthermore, a follow-up study [10] was conducted on maize adaptation to heat treatment in a greenhouse experiment. The authors claimed that warmer temperatures accelerate the development rate, resulting in shorter vegetative and reproductive phases and that the maize GY is reduced under heat stress, mainly via pollen viability, which in turn, determines the kernel number. Further, the negative impact of waterlogging should not be neglected. Waterlogging comes as a result of heavy rainfall or extreme weather events, which are becoming more pronounced due to climate change. The stress caused by waterlogging inhibits crop growth because the water content in the soil is above the field capacity (FC); therefore, the air is expelled and, consequently, all pores are filled with water, leading to a lack of oxygen (hypoxia). If this condition lasts for a long time, the crop will be destroyed. There have been numerous studies [11,12] that investigated the negative impact of waterlogging on the yield of maize, especially during the early seedling stage to the tasselling stage [13], with yield reductions of 25–30% [14]. Given the increasing evidence of climate change in terms of the lack of or excessive rainfall, as well as the poor distribution and high air temperatures, substantial increases in maize yields will require developing cultivars with greater water use efficiency, which is a trait that has not been a priority for breeders in the past [15].
Water (irrigation) and nitrogen (fertilization) have long been recognized as two major limiting factors for maize production. Following that, the results of numerous studies indicate a decrease in maize yield due to a water deficit [16,17,18,19,20] and an increase in maize yield with an increase in the N rate [21,22,23]. The yield reduction of maize grain is mostly proportional to the severity of the drought stress, but it should be noted that research on the impact of irrigation on maize yield gives different results and conclusions. For example, some study results indicate that the highest maize GYs are achieved after treatments with the highest net irrigation [24,25,26,27], while some of the studies [28,29,30] have confirmed that increasing the amount of water does not directly equate to higher yield results. This is supported by the results of a meta-analysis conducted by Lee et al. [31]. These authors claimed that maize yield increased linearly with increasing water input with 3% of the variation in yield when the total water input was less than 314 mm, while further water input decreased maize yield with 3% of the variation. As for N fertilization, the same authors claimed that maize yield increases linearly with increasing N input with 12% of the variation in GY when the total N input was less than 250 kg ha−1, while further N input did not affect the maize yield. In addition to GY, the water deficit also has a negative effect on the quality of maize in terms of the grains’ chemical composition. Several studies confirmed that crude oil, starch, and ash yield are considerably decreased due to water deficit [32,33,34,35,36]. This, of course, is of great importance when maize is grown for the food industry and animal feed production, where high grain quality is expected. Previous studies emphasized the importance of the interaction between growing systems (irrigation and fertilization), weather conditions, and soil properties for soil water and nutrient availability, as well as crop yield potential [37,38,39,40,41]. An interesting observation was made by Qi et al. [20]. The authors claimed that the increasing N rate from 200 to 300 kg N ha−1 resulted in increased maize biomass and GY under a 75–80% FC irrigation treatment, while it had no impact on those under the 45–50% and 60–65% FC treatments. Prior research [42] suggests that there is an optimal nitrogen application amount that maximizes the effectiveness of irrigation water on increasing GY above rainfed yields. The authors claimed that the optimal N level for maximum productivity varied not only between the irrigation levels but also exhibited interannual variability for the same irrigation level, indicating that these variables are impacted by the climatic conditions. McBratney and Field [43] and Suchy et al. [44] claimed that excessive application of N fertilizer has negative effects on crops, greatly reduces N use efficiency (NUE), and causes significant nitrate leaching losses and contamination of groundwater. The main source of nitrate contamination that is associated with groundwater is crop production, i.e., N fertilization [45] due to the low N fertilization efficiency [46]. In the European Union (UN), the Nitrate Directive (1991, 91/676/EEC) is the main regulation for reducing the environmental impacts of N fertilizer and for increasing nitrogen use efficiency. According to the regulation, the N target in groundwater is 50 mg NO3- L−1. Besides the N fertilizer rate [47,48,49], NO3 leaching depends on the weather conditions [50,51,52], soil type [53,54,55], and irrigation [56,57,58,59]. Hence, the objectives of this study were to investigate the effects of irrigation and N fertilizer rate on grain yield (GY) and grain composition in terms of the grain starch content (GSC), grain protein content (GPC), and grain oil content (GOC) of maize hybrids with similar maturity groups during three successive and quite different climatic years with pronounced extreme weather events. Furthermore, the study was conducted to evaluate the impact of the mentioned study factors on soil N accumulation and nitrate leaching to groundwater and to evaluate irrigation water and N fertilizer use efficiency in the mentioned agro-ecological conditions.

2. Materials and Methods

The research site was in Osijek, in the eastern Croatian region of Slavonia (45°32′ N and 18°44′ E, altitude 90 m). The climate is a temperate continental climate (Cfwbx climate class), with a mean annual air temperature of 12 °C and a mean annual precipitation of 650 mm [60]. The soil at the research site is classified as anthropogenic eutric cambisol (WRB) with a silty clay loamy texture; its main physical and chemical properties are presented in Table 1 [61].
Three irrigation rates (a1 = rainfed, a2 = 60–100% of FC, and a3 = 80–100% FC), three N fertilizers rates (b1 = 0 kg N ha−1, b2 = 100 kg N ha−1, and b3 = 200 kg N ha−1), and four maize hybrids (c1 = OSSK596, c2 = OSSK617, c3 = OSSK602, and c4 = OSSK552) were arranged as split–split plots in a randomized complete block design with three replications. The sizes of the basic experimental plots were as follows: a = 235 m2, b = 78.4 m2, and c = 19.6 m2, where the total size of the experimental plot was near 1 ha (Figure 1).
Each experimental plot had four rows of maize plants added on each side of the plot, which served as protective belts to prevent the study treatments from overlapping. The 3 m wide free space between the plots allowed for the passage of the irrigation system.
Maize crop was irrigated with a traveling sprinkler system with a maximum 30 m watering range. Water for the system was pumped from a 37 m deep well that was located near the experimental plot at a 5–7 L s−1 flow rate using an electric pump (5.5 kW). Water analysis was done according to the Food and Agricultural Organization (FAO) water quality standards for agriculture [62]. As presented in Table 2, there were no chemically derived contaminants, while the Mg2+ content was slightly higher (5.076 me/L) than the maximum contaminant level (MCL). The sodium adsorption ratio (SAR) was 11 me/L, and the ECw was 0.97 dS/L, which means that water could be used with a slight-to-moderate degree of restriction on use.
The irrigation time was determined by measuring the soil water content (SWC) with the use of granular matrix sensors (GMSs). Sensors were placed at two depths (15 and 30 cm) in each irrigation treatment and replicate, which means that a total of 18 sensors were placed in the experimental plot. Before the placement in soil, the GMSs were calibrated to the soil in a trial plot by comparing gravimetric measurements and sensor readings. The calibration results are presented in Table 3. The correlation analysis showed a strong negative relationship between the SWC and GMS readings.
Furthermore, according to the calibration results, the 0–40 cbar GMS range denotes 100% of the FC, while 70–80 cbar denotes the management allowable depletion (MAD). This means that the irrigation time was complete when the GMS readings were 70–80 cbar (60% of FC) for the a2 treatment and 40 cbar (80% of FC) for the a3 treatment. The sensor calibration curve was previously presented by Marković et al. [29]. The SWC was approximately measured three times per week after irrigation events or a significant amount of rainfall (>5 mm). The irrigation rate on both irrigation treatments was 35 mm, which was calculated using the following model:
IR = 100 × Bd × h × (FC − RAW),
where IR is the amount of added water in one irrigation event (mm or L m2), Bd is the bulk density (g cm−3), h is the depth of irrigation water penetration (m), FC is the field capacity (%), and RAW denotes the readily available water (as a percentage of FC). The efficiency of irrigation was tested according to Blümling et al. [63]:
IWUE = (Yi − Yr)/NIR,
where IWUE stands for the irrigation water use efficiency (kg ha−1 mm−1), Yi stands for the GY on irrigated plot (kg), Yr stands for the GY on a rainfed plot (kg), while NIR stands for the net irrigation rate (mm).
The amount and form of N fertilizers applied for each fertilizer treatment during the study period are presented in Table 4. Basic/autumn and basal application, i.e., pre-sowing (April month) maize fertilization, was done with urea. Two side dressings were performed for the b2 and b3 N fertilizer treatments with the use of calcium ammonium nitrate (CAN) spread by hand around the plants and in between the plant rows. Side dressing fertilizer applications were done during the May/June period.
The efficiency of the N fertilizer rate was tested according to Rehman et al. [64]:
FUE = (Yf − Yc)/FR,
where FUE stands for the fertilizer use efficiency, Yf stands for the GY (kg) from the fertilized treatment, Yc stands for the GY (kg) from the control (unfertilized) treatment, and FR stands for the N fertilizer rate (kg).
Four maize hybrids with similar maturity groups were used in the study: c1—OSSK596, c2—OSSK617, c3—OSSK602, and c4—OSSK552. Maize crop was sown on 6 May, 3 May, and 27 April with a hand planter. The planting space between the maize rows was 0.70 m, with a 0.25 m inter-row spacing. Maize was planted in two rows, 10 m long per experimental plot, with a total plant density of 58333 plants/ha. The sowing was preceded by soil tillage with a rotary harrow on 19 April in all three years of the study. As for the maize crop protection, weed control was done using Radazin T50 at 2 L ha−1 and Dual Gold 960 EC at 1.4 L ha−1. The maize crop was harvested at a mature stage on 12 (2010), 3 (2011), and 5 November (2012). Grain samples were collected at harvest time from each experimental plot and taken to a laboratory for grain composition analysis. Measurement of the grain moisture was done in the field immediately after the grain samples were collected. The GY was determined for each experimental plot (study treatment) and standardized to a 14% grain moisture content. The GSC (%), GPC (%), and GOC (%) contents were analyzed using a spectrophotometer (Infratec 12141, Foss Tecator).
The climatological characteristics classification for each study year was done according to the analysis of the distribution of the climatic elements, probability percentiles, and estimation of the extremes made by the Croatian Meteorological and Hydrological Service [65]. Then, climate data were collected from a weather station located near the experimental field—the amount of rainfall (mm), air temperature (°C) and humidity (%), wind speed (km day−1), and radiation (MJ m2 d−1)—and were used for the calculation of crop water requirements (CWR) with the use of the CROPWAT 8.0 computer model. Effective rainfall was determined with the use of the USDA method, which is integrated into the CROPWAT 8.0 model. Monthly values of rainfall and average monthly air temperatures were compared to the long-term averages (LTA, 1981–2010) to highlight deviations. The Penman–Monteith methodology [66] was applied to calculate the reference evapotranspiration (ETo), while the crop evapotranspiration (ETc) was determined by multiplying the ETo and single crop coefficient (kc), which integrates differences in the soil evaporation rate and crop transpiration rate between the crop and the grass reference surface and was taken from Allen et al. [66]. An observation well that was located near the experimental field was used for monitoring the groundwater level two times per week on average.
Soil samples were collected in springtime, before pre-sowing fertilization, and in autumn after the maize harvesting (before autumn fertilization). Soil samples were collected from five points of each irrigation and N fertilizers subplot in the 20 cm horizontal direction and 0–30 cm vertically downward. In total, 180 soil samples (5 soil samples from each irrigation (3) and N fertilizers (3) treatment, two times per growing season (pre-sowing and after harvesting)) were collected during the three-year study and analyzed for the total N content. The total N content was determined using the dry combustion method (RM: ISE 910, ISE 882, and ISE 955, Wepal, recovery <5%). The accuracy of the analyses was controlled by repeating the analysis of the samples (3 times) and was satisfactory (RSD < 10%). Soil samples were taken from each irrigation and N fertilization plot to study the impact of the study factors on the soil N dynamics.
Eighteen Ebermayer lysimeters (80 × 80 × 10 cm) were used for collecting drainage water. An Ebermayer lysimeter is an in situ type of lysimeter with no side walls separating a definitive soil block from the adjacent soil. The lysimeters were set up 0.8 m deep in undisturbed soil for each irrigation (3) and N fertilizer (3) treatment and three replicates. Drainage water was pumped out to open ditches, collected with plastic bottles, and taken to the laboratory for the analysis of the NO3− concentration during the first and third (last) year of the study due to the maize–soybean crop rotation. Drainage water was collected three times per growing season as mixed samples of the three replicates per irrigation and fertilizer treatment. Leached nitrate was calculated from the nitrate concentration and the volume of drainage water.
An analysis of variance (ANOVA) was conducted using the general linear model (GLM) procedure in SAS software (version 8.0, SAS Institute, Cary, NC, USA, 2013). The main effects of irrigation, N fertilizer level, maize hybrid, and year rate on maize GY, GPC, GOC, and GSC were analyzed. The results were analyzed using SAS software (SAS Institute, Cary, NC, USA). A protected least significant difference (LSD) procedure was used to separate the differences at probability levels of 0.05 and 0.01. Linear correlations (MS Excel) between the tested parameters were evaluated using t-tests.

3. Results

3.1. Weather Conditions, Groundwater Levels, and Soil Water Contents

The first year of the field study was extremely wet and warm (Figure 2). The amount of rainfall during the maize growing period was higher by 68.4% than the LTA (368 mm). During the period of maize vegetative growth, due to the excessive amount of precipitation that fell in a very short time in the area of Osijek, a natural disaster with a large amount of precipitation on 28 May and a natural disaster with a large amount of precipitation and flood on 9 June were declared by local authorities. As for air temperatures, during the growing period, the air temperatures were 0.2 °C higher than LTA (18.1 °C).
The second year of the field study was very warm and extremely dry. The amount of rainfall during the growing period was 33.4% lower than the LTA and the air temperatures were 1.82 °C higher than LTA. The last year of the field study was extremely warm and average in terms of the rainfall amount. The amount of rainfall was 21% lower than the LTA, while the air temperatures were 2.4 °C higher than the LTA.
Due to large differences in the rainfall during the study period, considerable changes in groundwater level were also recorded (Figure 3). In an extremely rainy year, the groundwater level was in the range of 20 to 160 cm from the soil top. In such a soil condition, anaerobic processes (anoxia) occurred in the soil, and yield reduction and increased nutrient leaching were expected. In an extremely dry year, the groundwater level ranged from 180 to 400 cm.
During the last year of the field study, the groundwater level ranged from 290 to 430 cm, where it can be claimed that the water level did not affect plant growth or yield formation.
During the first year of the study (extremely wet), the averages for the irrigation treatments’ SWC were 48.14 cbar for a1, 31.25 cbar for a2, and 24.36 cbar for a3 (Figure 4), which, according to the calibration results, means that the SWC was in the range of 80 to 100% FC for most of the growing period. For this reason, the maize in the a2 irrigation treatment was irrigated only once (20 July), while in a3, the maize was irrigated three times (6 and 20 July, 13 August). Accordingly, the net irrigation during the first year of the study was 35 mm (a2) and 105 mm (a3). During the second year of the study, the SWC ranged from 28 cbar (a3) to 148 cbar (a1). Since the growing season was extremely dry, a considerably higher amount of irrigation water was added compared to the previous year to keep the SWC at the set levels. Accordingly, the net irrigation for an extremely dry year was 105 mm (a2) and 245 mm (a3). Although in the last year of the study, there was no considerable aberration from the LTA in terms of the rainfall amount, the high air temperatures (Figure 2) and very low groundwater levels (Figure 3) resulted in a low SWC. For this reason, the net irrigation in the last year of the study was 175 mm (a2) and 245 mm (a3).

3.2. Irrigation Water Requirements

The monthly values of the crop water requirements (ETc), effective rainfall (Peff), and irrigation water requirements (IWR) are presented in Figure 5.
The ETc ranged from 20.6 mm/month (April) to 150.4 mm/month (August) during the extremely wet growing season, from 26.7 mm/month (April) to 154.7 mm/month (August) during the extremely dry growing season, and from 29.4 mm/month (April) to 167.7 mm/month (July) during the average year. According to the ETc and Peff analysis, in the extremely wet growing season, the water deficit occurred only during July and the beginning of August (175 mm). Unlike the first study year, in the next two growing seasons, water shortages occurred throughout the entire growing season, especially in the summer months of June, July, and August when the IWR was almost the same (325.1 mm and 327.4 mm/month).

3.3. Impact of Irrigation (a), N Fertilizer Rate (b), and Maize Hybrid (c) on Yield and Grain Compounds

According to the ANOVA, the maize GY varied significantly (p < 0.01) across the irrigation, N fertilizer rate, and maize hybrid variables (Figure 6), regardless of the weather conditions. In the first year of the study, i.e., the extremely wet year, the maize GY across the irrigation treatments ranged from 8.59 (a3) to 9.24 t ha−1 (a1). In the extremely dry year, the maize GY ranged from 7.47 (a1) to 9.35 t ha−1 (a3), while in the average year, the maize GY ranged from 7.37 (a1) to 10.28 t ha−1 (a3). The N fertilizer rate increased the maize GY in all three growing seasons (p < 0.01); that is, in the extremely wet year: 5.99 (b1) to 12.11 t ha−1 (b3), in the extremely dry year: 6.10 (b1) to 10.25 t ha−1 (b3), and in the average year: 8.11 (b1) to 9.55 t ha−1 (b3). As for maize hybrids, a significantly (p < 0.01) higher GY was recorded for the c3 hybrid with 10.55 t ha−1, regardless of the weather conditions.
The significance of the study factors on the GPC was year dependent (Figure 7). A significant (p < 0.01) impact due to irrigation was recorded in the extremely wet and average years: a1 = 7.39% to a3 = 7.74% (extremely wet) and a1 = 10.26% to a3 = 9.82% (average year). A significant (p < 0.01) impact of the N fertilizer rate was recorded, regardless of the weather condition, whereby the GPC was higher as the N fertilizer rate increased. A significantly (p < 0.01) higher GPC was recorded for c1 = 10.33% only in the average climatic year. The irrigation significantly (p < 0.01) affected the GSC in the extremely wet and average years, whereby the maximum GSC was recorded for the a3 irrigation treatment: a1 = 72.8% to a3 = 73.8 (a3) in the extremely wet year and a1 = 75.7% to a3 = 77.1% (a3) in the average climatic year (Figure 6). The impact of the N fertilizer rate on the GSC was significant (p < 0.01) only in the extremely dry year: b1 = 73.5% to b3 = 72.9%. The maize hybrid had a significant (p < 0.01) effect on GSC in all three years, whereby the highest GSC was recorded for c4 hybrid regardless of the weather condition. As for the GOC, significance (p < 0.05) of irrigation treatment was only recorded in the extremely dry year: a1 = 4.6% to a3 = 4.7% (Figure 7). The impact of the N fertilizer rate on the GOC was significant in the extremely wet and average years: b1 = 3.4% to b2 = 3.24% (extremely wet) and b1 = 4.61% to b3 = 4.74% (average). The GOC significantly (p < 0.01) varied across the hybrids in all growing seasons with inconsistency between hybrids, i.e., in the extremely wet year, a significantly (p < 0.01) higher GOC was recorded for c3 = 3.42%, c1 = 3.54% in the extremely dry year, and c1 = 4.81% in the average year.
Significant correlations (Figure 8) were found between the oil and protein content in the extremely wet year (r = 0.32, p < 0.01, N = 107). Furthermore, significantly correlations between oil and starch content were found during the extremely wet (r = −0.31, p < 0.01) and average years (r = 0.27, p < 0.01, N = 107) and between protein and starch content in the extremely wet (r = −0.48, p < 0.01, N = 107) and extremely dry growing seasons (r = −0.81, p < 0.01, N = 107).
The efficiency of the irrigation and N fertilizer rate are presented in Table 5. In the extremely wet growing season, the IWUE was lower when the irrigation rate was higher. This was opposite to the extremely dry year, where the highest IWUE was found for the fully irrigated treatment (a3). In the average growing season, the highest IWUE was found for the a2 irrigated plots. If the IWUE was analyzed in relation to the N fertilizer rate, then the efficiency was weather and N fertilizer rate dependent, and it ranged from −12.57 kg ha−1 m−1 (a2b2, extremely wet) to 18.19 kg ha−1 m−1 (a2b3, extremely dry). As for the FUE, in the extremely wet growing season, the highest FUE was found with the maximum N fertilizer rate (b3), while in the other two growing seasons, the highest FUE was found for the N fertilizer rate, regardless of the weather conditions. If the FUE was analyzed in terms of the irrigation rates, then the FUE ranged from 5.95 (a2b3, average year) to 38.3 kg ha−1 (a3b2, extremely dry).

3.4. Soil Nitrate Level across Weather Conditions, Irrigation Levels (a), N Fertilizer Rates (b), and Soil Sampling Times (c)

The soil nitrate levels showed a typical decrease during the study period, with the highest values at the beginning of the study and decreasing levels toward the end of the study period (Figure 9). The same pattern was present in each of the study years, depending on the sampling time, i.e., the highest values were measured in the first sampling, which was before sowing, while the soil N values were reduced after the harvest. These differences were more pronounced in the extremely wet and extremely dry years. Figure 8 shows that the maximum soil N (%) level during the extremely wet year (0.17%) was in the rainfed (a1) and a2 irrigated plots and with the highest N fertilizer rate (200 kg N ha−1), taking on a declining trend afterward, regardless of the N fertilizer rate and sampling time.
Statistical analysis (ANOVA) showed the significant impact (p < 0.01) of N fertilizer rate only in the average year, while the soil sampling time (c) was significant (p < 0.01) in the extremely dry and average years (Table 6). The expected higher N levels were measured for the b3 N fertilizer rate (200 kg N ha−1) and collected before sowing.
Significant correlations (Figure 10) were found between the weather condition (year) and soil N level (p < 0.05, r = −0.719, N = 53), and between the N fertilizer rate and soil N level (p < 0.05, r = 0.401, N = 53). On average, the irrigation rate (a1 = 0.127%, a2 = 0.126%, and a3 = 0.124%) and sampling time (c1 = 0.132% and c2 = 0.119%) reduced the soil N level, although this was not statistically significant.

3.5. Nitrate Leaching

The leaching losses in the extremely wet and average years are presented in Figure 11. Regardless of different weather conditions, the expected increase in leached nitrate was found with the increase in irrigation and N fertilizer rate. In the extremely wet year, nitrate concentration was in range from 11.92 mg NO3− L (a1) to 23.72 mg NO3− L (a3) and from 8.59 mg NO3− L (b1) to 29.06 mg NO3- L (b3). As for the sampling time, the maximum nitrate concentration was measured during springtime, i.e., the first sampling. In the average year, the nitrate concentration was in the range from 11.92 mg NO3− L (a1) to 23.72 mg NO3− L (a3) and from 8.59 mg NO3− L (b1) to 29.06 mg NO3− L (b3). The nitrate concentrations that exceeded the maximum allowable concentration (MAC) in the extremely rainy year were measured for a2b3 (52.6 mg NO3− L) and a3b3 (86.69 mg NO3− L) only in the first sampling during springtime. During the average year, the maximum nitrate concentration was found for the second sampling during the summertime, whereby the nitrate concertation exceeded the MAC for the a3b2 (51.37 NO3− L) and a3b3 (79.28 NO3− L) study treatments. During the springtime, the only study treatment that exceeded the MAC was a3b3 (62.47 NO3− L).
On average the across sampling times, both irrigation and N fertilizer rates intensified the leaching losses, which was more pronounced during the average year (stronger correlation coefficient, Figure 12). The annual amount of the leached nitrate ranged from 3.12 (a1b1) to 14.89 kg NO3−/ha (a3b3) in the extremely wet year and from 0.82 (a1b1) to 12.21 kg NO3−/ha (a3b3) in the average year (Figure 12), that is, a higher amount of leached nitrate was found in the extremely wet year, as expected.

4. Discussion

The impact of irrigation on the GY depended on the weather conditions, i.e., in an extremely wet year, the maize GY from the a3 irrigation treatment was significantly (p < 0.01) lower compared to those from the rainfed (a1) and a2 irrigation treatments (Figure 6 and Figure 7), which was the complete opposite of the remaining two years of the study and the results of many previously published studies, which reported an increase in yield with increasing irrigation rate [67,68,69]. Given that the leachate level in the extremely rainy year exceeded the MAC only in the springtime, it can be assumed that the decrease in yield from the a3 irrigation treatment was the result of the excessive amount of SWC due to the irrigation water. The upper soil layer in which the sensors were placed (<30 cm) was dry for a short time (Figure 2, end of July and beginning of August), because of which, the GMS readings indicated that the SWC in the upper soil layer was below the FC (Figure 4). As previously presented by Marković et al. [29], irrigation water decreases the accessibility to oxygen in the maize root zone and induces stress; therefore, the yield of the a3 irrigated treatment was significantly lower. Although the groundwater level and the SWC at the mentioned time were lower compared to the rest of the maize-growing period, the development stage of the maize, i.e., the depth of the root system, should be taken into account. Our results showed that the maize root system could draw water from deeper layers of soil, i.e., a depth greater than 30 cm, than where the sensors were placed, and that the additional irrigation caused water-induced stress due to an excessive SWC. The result of the IWUE analysis confirmed this statement (Table 4) since the higher irrigation rate reduced the maize IWUE in the extremely wet growing season for both N fertilizer treatments. One should not ignore the fact that the IWR analysis showed that even during the extremely wet growing season, the lack of water during July and August should be compensated (Figure 5). Furthermore, an interesting finding was that there was almost the same IWR during the extremely dry and average year, which came as a result of the extremely high air temperatures during the last study year, i.e., the high ETc rate. Additionally, our results demonstrate two things. First, the installation of the GMS should be adjusted to extreme weather conditions, i.e., high amount of rainfall in a short period, potential waterlogging, and the groundwater level. Second, this adjustment should also consider the crop growth stage, i.e., the root depth. Here, it is important to keep in mind that our study design was to schedule irrigation events according to SWC measurements found with the GMS. This may be considered as a potential limitation in environmental or field studies, where one factor cannot be completely separated from others, i.e., where the study factors are closely related to external factors that cannot be influenced. Another limitation that should be addressed is the fact that some observations and data collection were limited by the field conditions, for example, heavy rainfall and waterlogging during extreme weather events.
From the results of Huzsvai and Ványiné [70], it is clear that the degree of the water supply of maize can only be adequately judged based on the degree of the nutrient supply. The results from our study provide evidence that the amount of leached nitrate increased with increasing irrigation and nitrogen fertilizer rate regardless of the weather conditions (Figure 11). However, it is important to emphasize that the highest amount of leached nitrates in the extremely wet year was in the springtime (Figure 12), which occurred as a result of excessive rainfall (Figure 2) and a high N fertilizer rate. This statement was confirmed using correlation analysis, which showed a stronger positive connection between the leached nitrate and N fertilizer rate compared to the irrigation rate (Figure 12), regardless of the weather conditions. The result of this analysis was compared with Li et al. [71], who noted that irrigation has more of an influence than N fertilization on leaching water quality and that the optimal irrigation, combined with optimal fertilization, was efficient at reducing the potential environmental risk caused by excessive fertilization. These differences could be explained by the fact that, in the mentioned research, a considerably higher net irrigation (6525 m3 ha−1) was added compared to our research (1050 m3 ha−1 in the extremely wet year and 2450 m3 ha−1 in the average year). The GY vs. irrigation and N fertilizer rate (Figure 6) provides evidence for this statement since a strong positive correlation was found between GY and N fertilizer rate, regardless of the weather conditions, while for the irrigation rate, only a strong positive correlation was found during the average year. The importance of N fertilizer was also confirmed during the extremely wet year, where the GYs in the irrigated plots were reduced less with increasing N fertilizer rate. It seems that this is not only valid for growing conditions with excessive amounts of rainfall but also drought, as Wang and Xing [72] made the important claim that a higher N fertilizer rate can be used to compensate for a shortage of water under limited water resources. Nevertheless, when the GY is considered, then both the IWUE and FUE should be analyzed considering both factors, i.e., the IWUE in relation to N fertilizer rate and vice versa. Here, again, the specificity of extreme weather conditions was noted. The IWUE decreased with an increasing N rate in the extremely wet growing season (Table 5). During the extremely dry growing season, the IWUE increased with an increasing N rate, whereby the maximum IWUE was found for the a2b3 treatment (18.19 kg ha−1/mm). Moreover, Di Paolo and Rinaldi [73] found that N fertilizer positively affected IWUE. In the average year, for both the a2 and a3 irrigation treatments, the highest IWUE was noted on control plots (0 kg N ha−1). Similar results were obtained for the FUE (Table 5). In the extremely wet and average growing seasons, the highest FUE of b2 and b3 was obtained for rainfed plots (a1), while in the extremely dry year, the FUE was higher when the irrigation rate was higher. Furthermore, the highest FUE was found for the a2b3 treatment (38.3 kg ha−1 kg−1).
The present study confirmed the findings regarding the importance of maize hybrids (genotypes) that are adaptable to different growing conditions, i.e., those that produce high yields under diverse and marginal conditions (weather extremes). From our results, it is clear that hybrid c3 showed the best adaptability to environmental conditions (Figure 6) since the highest yields were recorded for this hybrid, regardless of the weather conditions. We emphasize that all hybrids used in the study belong to a similar maturity group (sown and harvested at the same time); therefore, the result of the higher yield potential was a response to a genetic trait. This is in line with previous studies from different parts of the world [74,75,76,77,78], where authors agreed that future research should involve modifying the maize selection criteria to combine tolerances to both drought and wet condition stresses.
As previously stated, grain quality in maize is a result of the interaction of genetic, environmental, and agronomic management factors [79], and the effect of these factors on grain quality is more complex than on yield [80]. In our study, the highest values of GPC, GOC, and GSC were recorded in the average year (Figure 6). Among the tested factors, a similar pattern was found across the study period, i.e., a higher irrigation rate reduced the GPC and increased the GSC, while a higher N rate increased the GPC and reduced the GSC. This was also explored in a prior study by Ben Mariem et al. [80]. These authors reported an increased GPC in drought conditions, but in their study, the drought conditions had no effect on the GSC. The results of other studies on this issue are also different. For example, Rahimi Jahangirlou et al. [81] stated that irrigation and cultivar did not influence the GPC and GOC in either year, which was opposite to our study results. Interesting study results published by Kresović et al. [32] showed that the highest maize GY and GOC and the lowest GSC were recorded after a fully irrigated treatment. In our study, the impact of irrigation on the GOC was year dependent, whereby in the extremely wet year (a1 = 3.28%, a3 = 3.38%) and in the average year (a1 = 4.6%, a3 = 4.8%), the irrigation rate increased the GOC, while in the extremely dry year, the irrigation rate decreased the GOC (a1 = 3.35%, a2 = 3.55%). As previously stated, in our study, a higher N rate increased the GPC and reduced the GSC, which was partially in line with Jahangirlou et al. [81]. These authors reported an increased GPC with a higher N rate. From the correlation analysis, it is clear that the GPC and GSC had a significant (p < 0.01) negative correlation (Figure 8) in the extremely wet (r = −0.48) and extremely dry years (r = −0.81). Different year-to-year study results, as well as the results of other studies, indicate the complexity of the issue and that additional research is needed.
As for the soil N level, although the net irrigation rates were different depending on the weather conditions, no statistical significance was observed (Table 6). As expected, the soil N level increased with the increase of N fertilizers (r = 0.40) and decreased toward the end of the study period (r = −0.72), which is in line with the previous study of Kühling et al. [49]. Our result confirmed that nitrate leaching due to a higher N rate occurred, especially in growing conditions with a high rainfall amount and a high irrigation rate. The same general pattern of higher leaching losses was found in many previous studies [46,49,82,83].

5. Conclusions

The present findings confirmed that the yearly variability in maize cultivation was closely tied to the amount of precipitation, as well as the distribution, during the summer crops growing season, which indicates the complexity of field studies and poses a challenge regarding drawing conclusions. Overall, our results demonstrated the strong effect of weather conditions and emphasized the importance of optimizing irrigation and N fertilization management during extreme weather conditions, particularly rainfall amounts. The significant year-to-year and within-study treatment variability of nitrate leaching implied that irrigation and N fertilizer management should be implemented in such a manner to avoid over- or under-application consequences. In our study, surplus N fertilizer leached into the groundwater (not accumulated in the soil), which should be considered when full irrigation is applied, since even under optimal conditions, N fertilizers are never fully utilized by the crop. Future research should consider the potential effects of extreme weather events more carefully, for example, the importance of the groundwater level and waterlogging for irrigation scheduling. Grain yield and grain quality in maize are a result of the interaction of genetic, environmental, and agronomic management factors; however, more studies are needed on how these factors work in an interactive way in different environmental conditions that cannot be controlled.

Author Contributions

Conceptualization, M.M. and M.J.; methodology, M.M., J.Š., and M.J.; investigation, M.M. and M.J.; resources, J.Š. and M.J.; writing—original draft preparation, M.M. and A.A.; writing—review and editing, M.M and A.A.; supervision, J.Š. and M.J. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external founding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Eurostat. Available online: https://ec.europa.eu/eurostat/documents/3217494/10317767/KS-FK-19-001-EN-N.pdf/742d3fd2-961e-68c1-47d0-11cf30b11489 (accessed on 8 May 2021).
  2. CBS. Croatian Bureau of Statistics. Available online: https://www.dzs.hr/Hrv_Eng/publication/2019/01-01-14_01_2019.htm (accessed on 1 May 2021).
  3. AQUASTAT. Available online: http://www.fao.org/aquastat/statistics/query/results.html (accessed on 1 May 2021).
  4. Zhang, H.; Han, M.; Comas, L.H.; DeJonge, K.C.; Gleason, S.M.; Trout, T.J.; Ma, L. Response of maize yield components to growth stage-based deficit irrigation. Agron. J. 2019, 111, 3244–3252. [Google Scholar] [CrossRef] [Green Version]
  5. Thomson, L.M. Weather variability, climate change and grain production. Science 1966, 188, 535–541. [Google Scholar] [CrossRef] [PubMed]
  6. Lobell, D.B.; Burke, M.B. On the use of statistical models to predict crop yield responses to climate change. Agric. For. Meteorol. 2010, 150, 1443–1452. [Google Scholar] [CrossRef]
  7. Lobell, D.B.; Bänziger, M.; Magorokosho, C.; Vivek, B. Nonlinear heat effects on” African Maize as evidenced by historical yield trials. Nat. Clim. Chang. 2011, 1, 42–45. [Google Scholar] [CrossRef]
  8. Basso, B.; Ritchie, J. Temperature and drought effects on maize yield. Nat. Clim Chang. 2014, 4, 233. [Google Scholar] [CrossRef]
  9. Bolaños, J.; Edmeades, G.O.; Martinez, L. Eight cycles of selection for drought tolerance in tropical maize. III. Responses in drought adaptive physiological and morphological traits. Field Crops Res. 1993, 31, 269–286. [Google Scholar] [CrossRef]
  10. Lizaso, J.I.; Ruiz-Ramos, M.; Rodríguez, L.; Gabaldon-Leal, C.; Oliveira, J.A.; Lorite, I.J.; Sánchez, D.; García, E.; Rodríguez, A. Impact of high temperatures in maize: Phenology and yield components. Field Crops Res. 2018, 216, 129–140. [Google Scholar] [CrossRef] [Green Version]
  11. Dickin, E.; Wright, D. The effects of winter waterlogging and summer drought on the growth and yield of winter wheat (Triticum aestivum L.). Eur. J. Agron. 2008, 28, 244–282. [Google Scholar] [CrossRef]
  12. Ren, B.; Zhang, J.; Li, X.; Fan, X.; Dong, S.; Liu, P.; Zhao, B. Effects of waterlogging on the yield and growth of summer maize under field conditions. Can. J. Plant. Sci. 2014, 94, 23–31. [Google Scholar] [CrossRef]
  13. Zaidi, P.H.; Rashid, Z.; Vinayan, M.T.; Almeida, G.D.; Phagna, R.K.; Babu, R. QTL Mapping of Agronomic Waterlogging Tolerance Using Recombinant Inbred Lines Derived from Tropical Maize (Zea mays L) Germplasm. PLoS ONE 2015, 10, e0124350. [Google Scholar] [CrossRef]
  14. Zaidi, P.H.; Maniselvan, P.; Srivastava, A.; Yadav, P.; Singh, R.P. Genetic analysis of water-logging tolerance in tropical maize (Zea mays L.). Maydica 2010, 55, 17–26. [Google Scholar]
  15. DeLucia, E.H.; Chen, S.; Guan, K.; Peng, B.; Li, Y.; Gomez-Casanovas, N.; Ort, D.R. Are we approaching a water ceiling to maize yields in the United States? Ecosphere 2019, 10, e02773. [Google Scholar] [CrossRef]
  16. Sah, R.P.; Chakraborty, M.; Prasad, K.; Pandit, M.; Tudu, V.K.; Chakravarty, M.K.; Narayan, S.C.; Rana, M.; Moharana, D. Impact of water deficit stress in maize: Phenology and yield components. Sci. Rep. 2020, 10, 2944. [Google Scholar] [CrossRef] [PubMed]
  17. Wang, C.; Linderhol, H.W.; Song, Y.; Wang, F.; Liu, Y.; Tian, J.; Xu, J.; Song, Y.; Ren, G. Impacts of Drought on Maize and Soybean Production in Northeast China During the Past Five Decades. Int. J. Environ. Res. Pub. Health 2020, 17, 2459. [Google Scholar] [CrossRef] [Green Version]
  18. Ya-nan, H.U.; Ying-jie, L.; Hua-jun, T.; Yin-long, X.; Jie, P. Contribution of Drought to Potential Crop Yield Reduction in a Wheat-Maize Rotation Region in the North China Plain. J. Integr. Agric. 2014, 13, 1509–1519. [Google Scholar]
  19. Comas, L.H.; Trout, T.J.; DeJonge, K.C.; Zhang, H.; Gleason, S.M. Water productivity under strategic growth stage-based deficit irrigation in maize. Agric. Water Manage. 2019, 212, 433–440. [Google Scholar] [CrossRef]
  20. Qi, D.; Hu, T.; Song, X. Effects of nitrogen application rates and irrigation regimes on GY and water use efficiency of maize under alternate partial root-zone irrigation. J. Integr. Agric. 2020, 19, 2792–2806. [Google Scholar] [CrossRef]
  21. Bélec, C.; Tremblay, N. Adapting nitrogen fertilization to unpredictable seasonal conditions with the least impact on the environment. Horttechnology 2006, 16, 408–412. [Google Scholar]
  22. Biswas, D.K.; Ma, B.L. Effect of nitrogen rate and fertilizer nitrogen source on physiology, yield, grain quality, and nitrogen use efficiency in corn. Can. J. Plant. Sci. 2016, 96, 392–403. [Google Scholar] [CrossRef] [Green Version]
  23. Sinclair, T.R.; Rufty, T.W. Nitrogen and water resources commonly limit crop yield increases, not necessarily plant genetics. Glob. Food Sec. 2012, 1, 94–98. [Google Scholar] [CrossRef]
  24. Fang, J.; Su, Y. Effects of Soils and Irrigation Volume on Maize Yield, Irrigation Water Productivity, and Nitrogen Uptake. Sci. Rep. 2019, 9, 7740. [Google Scholar]
  25. Marković, M.; Josipović, M.; Šoštarić, J.; Jambrović, A.; Brkić, A. Response of Maize (Zea mays L.) GY and Yield Components to Irrigation and Nitrogen Fertilization. J. Cent. Eur. Agric. 2017, 18, 55–72. [Google Scholar] [CrossRef] [Green Version]
  26. Ibrahim, M.M.; El-Baroudy, A.A.; Taha, A.M. Irrigation and fertigation scheduling under drip irrigation for maize crop in sandy soil. Int. Agrophysics 2016, 30, 47–55. [Google Scholar] [CrossRef] [Green Version]
  27. Kara, T.; Biber, C. Irrigation Frequencies and Corn (Zea mays L.) Yield Relation in Northern Turkey. Pak. J. Biol. Sci. 2008, 11, 123–126. [Google Scholar] [CrossRef] [Green Version]
  28. Orfanou, A.; Pavlou, D.; Porter, W. Maize Yield and Irrigation Applied in Conservation and Conventional Tillage at Various Plant Densities. Water 2019, 11, 1726. [Google Scholar] [CrossRef] [Green Version]
  29. Marković, M.; Tadić, V.; Josipović, M.; Zebec, V.; Filipović, V. Efficiency of maize irrigation scheduling in climate variability and extreme weather events in eastern Croatia. J. Water Clim. Chang. 2015, 6, 586–595. [Google Scholar] [CrossRef]
  30. Kuscu, H.; Karsu, A.; Ozi, M.; Demir, A.O.; Turgut, I. Effect of irrigation amounts applied with drip irrigation on maize evapotranspiration, yield, water use efficiency, and net return in a sub–humid climate. Turkish J. Field Crop. 2013, 18, 13–19. [Google Scholar]
  31. Li, Y.; Cui, S.; Zhang, Z.; Zhuang, K.; Wang, Z.; Zhang, Q. Determining effects of water and nitrogen input on maize (Zea mays) yield, water- and nitrogen-use efficiency: A global synthesis. Sci. Rep. 2020, 10, 9699. [Google Scholar] [CrossRef]
  32. Kresović, B.; Gajić, B.; Tapanarova, A.; Dugalić, G. How Irrigation Water Affects the Yield and Nutritional Quality of Maize (Zea mays L.) in a Temperate Climate. Pol. J. Environ. Stud. 2018, 27, 1123–1131. [Google Scholar] [CrossRef]
  33. Barutçular, C.; Dizlek, H.; EL-Sabagh, A.; Sahin, T.; Elsabagh, M.; Shohidul, I.M. Nutritional quality of maize in response to drought stress during grain-filling stages in mediterranean climate condition. J. Exp. Biol. Agric. Sci. 2016, 4, 644–652. [Google Scholar]
  34. Ali, Q.; Ashraf, M.; Anwar, F. Seed composition and seed oil antioxidant activity of maize under water stress. J. Am. Oil Chem. Soc. 2010, 87, 1179–1187. [Google Scholar] [CrossRef]
  35. Ali, Q.; Anwar, F.; Ashraf, M.; Saari, N.; Perveen, R. Ameliorating Effects of Exogenously Applied Proline on Seed Composition, Seed Oil Quality and Oil Antioxidant Activity of Maize (Zea mays L.) under Drought Stress. Int. J. Mol. Sci. 2013, 14, 818–835. [Google Scholar] [CrossRef] [PubMed]
  36. Ji, R.P.; Che, Y.S.; Zhu, Y.N.; Liang, T.; Feng, R.; Yu, W.Y.; Zhang, Y.S. Impacts of drought stress on the growth and development and GY of spring maize in Northeast China. Chin. J. Appl. Ecol. 2012, 11, 3021–3026. [Google Scholar]
  37. Nagy, J. Effect of Irrigation on Maize Yield (Zea mays L.). Acta Agrar. Debr. 2003, 11, 30–35. [Google Scholar] [CrossRef] [PubMed]
  38. Rad, S.; Gan, L.; Chen, X.; You, S.; Huang, L.; Su, S.; Taha, M.R. Sustainable Water Resources Using Corner Pivot Lateral, A Novel Sprinkler Irrigation System Layout for Small Scale Farms. Appl. Sci. 2018, 8, 2601. [Google Scholar] [CrossRef] [Green Version]
  39. Hwang, S.J.; Lee, J.Y.; Nam, J.S. Irrigation System for a Roller-Type Onion Pot Seeding Machine. Appl. Sci. 2019, 9, 430. [Google Scholar] [CrossRef] [Green Version]
  40. Nouraein, M.; Skataric, G.; Spalevic, V.; Dudic, B.; Gregus, M. Short-Term Effects of Tillage Intensity and Fertilization on Sunflower Yield, Achene Quality, and Soil Physicochemical Properties under Semi-Arid Conditions. Appl. Sci. 2019, 9, 5482. [Google Scholar] [CrossRef] [Green Version]
  41. Schröder, J.J.; Neeteson, J.J.; Oenema, O.; Stuik, P.C. Does the crop or soil indicate how to save nitrogen in maize production? Reviewing the state of the art. Field Crop. Res. 2000, 66, 151–164. [Google Scholar] [CrossRef]
  42. Rudnick, D.; Irmak, S. Impact of water and nitrogen management strategies on maize yield and water productivity indices under linear-move sprinkler irrigation. Biosyst. Eng. 2013, 56, 1769–1783. [Google Scholar]
  43. McBratney, A.; Field, D. Securing our soil. Soil Sci. Plant. Nutr. 2015, 61, 587–591. [Google Scholar] [CrossRef] [Green Version]
  44. Suchy, M.; Wassenaar, L.I.; Graham, G.; Zebarth, B. High-frequency NO3− isotope (delta N-15, delta O-18) patterns in groundwater recharge reveal that short-term changes in land use and precipitation influence nitrate contamination trends. Hydrol. Earth Syst. Sci. 2018, 22, 4267–4279. [Google Scholar] [CrossRef] [Green Version]
  45. Busico, G.; Kazakis, N.; Colombani, N.; Khosravi, K.; Voudouris, K.; Mastrocicco, M. The Importance of Incorporating Denitrification in the Assessment of Groundwater Vulnerability. Appl. Sci. 2020, 10, 2328. [Google Scholar] [CrossRef] [Green Version]
  46. Zhao, H.L.; Xiaozong, S.; Lihua, J.; Haitao, L.; Yu, X.; Xinhao, G.; Fuli, Z.; Deshui, T.; Mei, W.; Jing, S.; et al. Strategies for Managing Soil Nitrogen to Prevent Nitrate-N Leaching in Intensive Agriculture System. In Soil Health and Land Use Management; Soriano, M.C.H., Ed.; IntechOpen: London, UK, 2012; pp. 133–154. [Google Scholar]
  47. Andraski, T.W.; Bundy, L.G.; Brye, K.R. Crop management and corn nitrogen rate effects on nitrate leaching. J. Environ. Qual. 2000, 29, 1095–1103. [Google Scholar] [CrossRef]
  48. Puntel, L.A.; Sawyer, J.E.; Barker, D.W.; Dietzel, R.; Poffenbarger, H.; Castellano, M.J.; Moore, K.J.; Thorburn, P.; Archontoulis, S.V. Modeling Long-Term Corn Yield Response to Nitrogen Rate and Crop Rotation. Front. Plant. Sci. 2016, 7, 1630. [Google Scholar] [CrossRef] [PubMed]
  49. Kühling, I.; Beiküfner, M.; Vergara, M.; Trautz, C. Effects of Adapted N-Fertilisation Strategies on Nitrate Leaching and Yield Performance of Arable Crops in North-Western Germany. Agronomy 2021, 11, 64. [Google Scholar] [CrossRef]
  50. Jabloun, M.; Schelde, K.; Tao, F.; Olesen, J.E. Effect of temperature and precipitation on nitrate leaching from organic cereal cropping systems in Denmark. Eur. J. Agron. 2015, 62, 55–64. [Google Scholar] [CrossRef]
  51. Martinez-Feria, R.; Nichols, V.; Basso, B.; Archontoulis, S. Can multi-strategy management stabilize nitrate leaching under increasing rainfall? Environ. Res. Lett. 2019, 14, 124079. [Google Scholar] [CrossRef]
  52. Hess, L.J.T.; Hinckley, E.L.S.; Robertson, G.P.; Matson, P.A. Rainfall intensification increases nitrate leaching from tilled but not no-till cropping systems in the U.S. Midwest. Agric. Ecosys. Environ. 2020, 290, 106747. [Google Scholar] [CrossRef]
  53. Meissner, R.; Rupp, H.; Seeger, J.; Schonert, P. Influence of mineral fertilizers and different soil types on nutrient leaching: Results of lysimeter studies in East Germany. Land Degrad. Dev. 1995, 6, 163–170. [Google Scholar] [CrossRef]
  54. Köhler, K.; Duynisveld, W.H.M.; Böttcher, J. Nitrogen fertilization and nitrate leaching into groundwater on arable sandy soils. J. Plant. Nutr. Soil Sci. 2006, 169, 185–195. [Google Scholar] [CrossRef]
  55. Rolbiecki, R.; Rolbiecki, S.; Figas, A.; Jagosz, B.; Wichrowska, D.; Ptach, W.; Prus, P.; Sadan, H.A.; Ferenc, P.F.; Stachowski, P.; et al. Effect of Drip Fertigation with Nitrogen on Yield and Nutritive Value of Melon Cultivated on a Very Light Soil. Agronomy 2021, 11, 934. [Google Scholar] [CrossRef]
  56. Mailhol, J.; Ruelle, P.; Nemeth, I. Impact of fertilisation practices on nitrogen leaching under irrigation. Irrig. Sci. 2001, 20, 139–147. [Google Scholar]
  57. Spalding, R.F.; Watts, D.G.; Schepers, J.S.; Burbach, M.E.; Exner, M.E.; Poreda, R.J.; Martin, G.E. Controlling Nitrate Leaching in Irrigated Agriculture. J. Environ. Qual. 2001, 30, 1184–1194. [Google Scholar] [CrossRef] [Green Version]
  58. Hu, K.; Li, Y.; Chen, W.; Chen, D.; Wei, Y.; Edis, R.; Li, B.; Huang, Y.; Zhang, Y. Modeling nitrate leaching and optimizing water and nitrogen management under irrigated maize in desert oases in Northwestern China. J. Environ. Qual. 2010, 39, 667–677. [Google Scholar] [CrossRef]
  59. Sharma, P.; Shukla, M.K.; Sammis, T.W.; Adhikari, P. Nitrate-Nitrogen Leaching from Onion Bed under Furrow and Drip Irrigation Systems. Appl. Environ. Soil Sci. 2012, 2012, 650206. [Google Scholar] [CrossRef]
  60. Zaninović, K.; Gajić-Čapka, M.; Tadić, M.P.; Vučetić, M.; Milković, J.; Bajić, A.; Cindrić, K.; Cvitan, L.; Katušin, Z.; Kaučić, D.; et al. Climate Atlas of Croatia; Meteorological and Hydrological Service of Croatia: Zagreb, Croatia, 2008. [Google Scholar]
  61. Marković, M.; Krizmanić, G.; Brkić, A.; Božica, J.P.; Davor, P.; Željko, B. Sustainable Management of Water Resources in Supplementary Irrigation Management. Appl. Sci. 2021, 11, 2451. [Google Scholar] [CrossRef]
  62. FAO. Food and Agriculture Organization of the United Nations. Water Quality for Agriculture. Available online: http://www.fao.org/docrep/003/T0234E/T0234E00.htm (accessed on 21 May 2021).
  63. Blümling, B.; Yang, H.; Pahl-Wostl, C. Proposal for the integration of irrigation efficiency and agricultural water productivity. Options Méditerranéennes 2011, 57, 263–280. [Google Scholar]
  64. Rehman, A.; Saleem, M.F.; Safdar, M.E.; Hussain, S. Grain Quality, Nutrient use Efficiency, and Bioeconomics of Maize Under Different Sowing and NPK Levels. Chil. J. Agric. Res. 2011, 71, 2011. [Google Scholar] [CrossRef] [Green Version]
  65. Croatian Meteorological and Hydrological Service. Climate Monitoring. Available online: https://meteo.hr/klima.php?section=klima_pracenje&param=ocjena (accessed on 30 July 2021). In Croatian.
  66. Allen, R.G.; Pereira, L.S.; Raes, D.; Smith, M. Crop Evapotranspiration-Guidelines for Computing Crop Water Requirements-FAO Irrigation and Drainage Paper 56; FAO: Rome, Italy, 1988. [Google Scholar]
  67. Abbas, G.; Hussain, A.; Ahmad, A.; Wajid, S.A. Effect of Irrigation Schedules and Nitrogen Rates on Yield and Yield Components of Maize. J. Agric. Soc. Sci. 2005, 1, 335–338. [Google Scholar]
  68. Oktem, A. Effects of deficit irrigation on some yield characteristics of sweet corn. Bangladesh J. Bot. 2008, 37, 127–131. [Google Scholar] [CrossRef]
  69. Hammad, H.M.; Ahmad, A.; Abbas, F.; Farhad, W. Optimizing water and nitrogen use for maize production under semiarid conditions. Turk. J. Agric. For. 2012, 36, 519–532. [Google Scholar]
  70. Huzsvai, L.; Ványiné, S.A. Water stress. In which cases does irrigation reduce the yield of maize? Proceedings of VIII. Alps-Adria Scientific Conference. Neum, Bosnia-Hercegovina, 27th April to 2nd May 2009. Cereal Res. Commun. 2009, 37, 45–48. [Google Scholar]
  71. Li, Y.; Li, J.; Gao, L.; Tian, Y. Irrigation has more influence than fertilization on leaching water quality and the potential environmental risk in excessively fertilized vegetable soils. PLoS ONE 2018, 13, e0204570. [Google Scholar] [CrossRef]
  72. Wang, X.; Xing, Y. Effects of Irrigation and Nitrogen Fertilizer Input Levels on Soil NO3--N Content and Vertical Distribution in Greenhouse Tomato (Lycopersicum esculentum Mill.). Scientifica 2006, 2016, 5710915. [Google Scholar]
  73. Di Paolo, E.; Rinaldi, M. Yield response of corn to irrigation and nitrogen fertilization in a Mediterranean environment. Field Crops Res. 2008, 105, 202–210. [Google Scholar] [CrossRef]
  74. Oliveira, T.; Carvalho, H.; Nascimento, M.; Costa, E.; Oliveira, G.; Gravina, G.A.; Junior, A.; Filho, J. Adaptability and stability evaluation of maize hybrids using Bayesian segmented regression models. PLoS ONE 2020, 15, e0236571. [Google Scholar] [CrossRef] [PubMed]
  75. Sabagh, A.; Hossain, A.; Iqbal, M.A.; Barutçular, C.; Islam, M.S.; Çiğ, F.; Erman, M.; Sytar, O.; Brestic, M.; Wasaya, A.; et al. Maize Adaptability to Heat Stress under Changing Climate. In Plant Stress Physiology; Hossain, A., Ed.; IntechOpen: London, UK, 2020; p. 579. [Google Scholar]
  76. Faria, S.V.; Luz, L.S.; Rodrigues, M.C.; de Souza Carneiro, J.E.; Carneiro, P.C.S.; Lima, R.O. Adaptability and stability in commercial maize hybrids in the southeast of the State of Minas Gerais, Brazil. Sci. Agron. 2017, 48, 347–357. [Google Scholar] [CrossRef] [Green Version]
  77. Tokadilis, I.S. Adapting maize crop to climate change. Agron. Sustain. Dev. 2013, 33, 63–79. [Google Scholar] [CrossRef] [Green Version]
  78. Tofa, A.I.; Kamara, A.Y.; Babaji BAAkinseye, F.M.; Bebeley, J.F. Assessing the use of a drought-tolerant variety as adaptation strategy for maize production under climate change in the savannas of Nigeria. Sci. Rep. 2021, 11, 8983. [Google Scholar] [CrossRef]
  79. Butts-Wilmsmeyer, C.J.; Seebauer, J.R.; Singleton, L.; Below, F.E. Weather during Key Growth Stages Explains Grain Quality and Yield of Maize. Agronomy 2019, 9, 16. [Google Scholar] [CrossRef] [Green Version]
  80. Mariem, S.B.; Soba, D.; Zhou, B.; Loladze, I.; Morales, F.; Aranjuelo, I. Climate Change, Crop Yields, and Grain Quality of C3 Cereals: A Meta-Analysis of [CO2], Temperature, and Drought Effects. Plants 2021, 10, 1052. [Google Scholar] [CrossRef] [PubMed]
  81. Jahangirlou, M.R.; Akbari, G.A.; Alahdadi, I.; Soufizadeh, S.; Parsons, D. Grain Quality of Maize Cultivars as a Function of Planting Dates, Irrigation and Nitrogen Stress: A Case Study from Semiarid Conditions of Iran. Agriculture 2021, 11, 11. [Google Scholar] [CrossRef]
  82. Robertson, G.P.; Vitousek, P.M. Nitrogen in agriculture: Balancing the cost of an essential resource. Annu. Rev. Environ. Resour. 2009, 34, 97–125. [Google Scholar] [CrossRef] [Green Version]
  83. Lin, B.L.; Sakoda, A.; Shibasaki, R.; Suzuki, M. A modelling approach to global nitrate leaching caused by anthropogenic fertilisation. Water Res. 2001, 35, 1961–1968. [Google Scholar] [CrossRef]
Applsci 11 07352 g001 550
Figure 1. Experimental plot design.
Figure 1. Experimental plot design.
Applsci 11 07352 g001
Applsci 11 07352 g002 550
Figure 2. Rainfall (mm) and air temperatures (°C) during the study period and the long-term average (LTA, 1981–2010).
Figure 2. Rainfall (mm) and air temperatures (°C) during the study period and the long-term average (LTA, 1981–2010).
Applsci 11 07352 g002
Applsci 11 07352 g003 550
Figure 3. Groundwater levels (cm) during the study period.
Figure 3. Groundwater levels (cm) during the study period.
Applsci 11 07352 g003
Applsci 11 07352 g004 550
Figure 4. Soil water content (SWC, cbar) for the irrigation treatments (a1, a2, and a3) during the three-year study (I = extremely wet, II = extremely dry, III = average).
Figure 4. Soil water content (SWC, cbar) for the irrigation treatments (a1, a2, and a3) during the three-year study (I = extremely wet, II = extremely dry, III = average).
Applsci 11 07352 g004
Applsci 11 07352 g005 550
Figure 5. Crop water requirements (ETc), effective rainfall (Peff), and irrigation water requirements (IWR) in different weather conditions.
Figure 5. Crop water requirements (ETc), effective rainfall (Peff), and irrigation water requirements (IWR) in different weather conditions.
Applsci 11 07352 g005
Applsci 11 07352 g006 550
Figure 6. Averages and significances (ANOVA) of the GY, protein, starch, and oil content across the irrigation (a1 = rainfed, a2 = 60–100% field capacity (FC), and a3 = 80–100% FC), N fertilizer rate (b1 = 0 kg N ha−1, b2 = 100 kg N ha−1, b3 = 200 kg N ha−1), and maize hybrid (c1 = OSSK596, c2 = OSSK617, c3 = OSSK602, and c4 = OSSK552) variables.
Figure 6. Averages and significances (ANOVA) of the GY, protein, starch, and oil content across the irrigation (a1 = rainfed, a2 = 60–100% field capacity (FC), and a3 = 80–100% FC), N fertilizer rate (b1 = 0 kg N ha−1, b2 = 100 kg N ha−1, b3 = 200 kg N ha−1), and maize hybrid (c1 = OSSK596, c2 = OSSK617, c3 = OSSK602, and c4 = OSSK552) variables.
Applsci 11 07352 g006
Applsci 11 07352 g007 550
Figure 7. Relationship between irrigation (a1 = rainfed, a2 = 60–100% field capacity (FC), and a3 = 80–100% FC) and the tested variables—(a) yield, (b) protein, (c) starch, and (d) oil content—and N fertilizer rate (b1 = 0 kg N ha−1, b2 = 100 kg N ha−1, b3 = 200 kg N ha−1) and the tested variables—(e) yield, (f) protein, (g) starch, and (h) oil content—in different weather conditions (extremely wet, extremely dry, and average year) significantly different at p < 0.05 (*) and p < 0.01 (**).
Figure 7. Relationship between irrigation (a1 = rainfed, a2 = 60–100% field capacity (FC), and a3 = 80–100% FC) and the tested variables—(a) yield, (b) protein, (c) starch, and (d) oil content—and N fertilizer rate (b1 = 0 kg N ha−1, b2 = 100 kg N ha−1, b3 = 200 kg N ha−1) and the tested variables—(e) yield, (f) protein, (g) starch, and (h) oil content—in different weather conditions (extremely wet, extremely dry, and average year) significantly different at p < 0.05 (*) and p < 0.01 (**).
Applsci 11 07352 g007
Applsci 11 07352 g008 550
Figure 8. Relationship between (a) protein and oil (%), (b) starch and oil (%), and (c) protein and starch content (%) in different weather conditions (extremely wet, extremely dry, and average) significantly different at p < 0.05 (*) and p < 0.01 (**).
Figure 8. Relationship between (a) protein and oil (%), (b) starch and oil (%), and (c) protein and starch content (%) in different weather conditions (extremely wet, extremely dry, and average) significantly different at p < 0.05 (*) and p < 0.01 (**).
Applsci 11 07352 g008
Applsci 11 07352 g009 550
Figure 9. Soil N (%) levels across weather conditions (extremely wet, extremely dry, average), irrigation (a1 = rainfed, a2 = 60–100% field capacity (FC), a3 = 80–100% FC), N fertilizer rate (b1 = 0 kg N ha−1, b2 = 100 kg N ha−1, b3 = 200 kg N ha−1), and soil sampling time (c1 = before sowing; c2 = after harvesting).
Figure 9. Soil N (%) levels across weather conditions (extremely wet, extremely dry, average), irrigation (a1 = rainfed, a2 = 60–100% field capacity (FC), a3 = 80–100% FC), N fertilizer rate (b1 = 0 kg N ha−1, b2 = 100 kg N ha−1, b3 = 200 kg N ha−1), and soil sampling time (c1 = before sowing; c2 = after harvesting).
Applsci 11 07352 g009
Applsci 11 07352 g010 550
Figure 10. Correlation between (a) soil N (%) level and weather conditions (extremely wet, extremely dry and average) and (b) soil N (%) level and N fertilizer rate (b1 = 0 kg N ha−1, b2 = 100 kg N ha−1, b3 = 200 kg N ha−1) significantly different at p < 0.05 (*).
Figure 10. Correlation between (a) soil N (%) level and weather conditions (extremely wet, extremely dry and average) and (b) soil N (%) level and N fertilizer rate (b1 = 0 kg N ha−1, b2 = 100 kg N ha−1, b3 = 200 kg N ha−1) significantly different at p < 0.05 (*).
Applsci 11 07352 g010
Applsci 11 07352 g011 550
Figure 11. Nitrate leaching in the (a) extremely wet and (b) average years for different irrigation levels (a1 = rainfed, a2 = 60–100% FC, and a3 = 80–100% FC) and N fertilizer rates (b1 = 0 kg N ha−1, b2 = 100 kg N ha−1, and b3 = 200 kg N ha−1).
Figure 11. Nitrate leaching in the (a) extremely wet and (b) average years for different irrigation levels (a1 = rainfed, a2 = 60–100% FC, and a3 = 80–100% FC) and N fertilizer rates (b1 = 0 kg N ha−1, b2 = 100 kg N ha−1, and b3 = 200 kg N ha−1).
Applsci 11 07352 g011
Applsci 11 07352 g012 550
Figure 12. Leaching losses across sampling times for (a) irrigation levels (a1 = rainfed, a2 = 60–100% FC, and a3 = 80–100% FC) and N fertilizer rates (b1 = 0 kg N ha−1, b2 = 100 kg N ha−1, and b3 = 200 kg N ha−1) significantly different at p < 0.05 (*), and (b) leaching losses (kg NO3− ha−1) in the extremely wet and average years.
Figure 12. Leaching losses across sampling times for (a) irrigation levels (a1 = rainfed, a2 = 60–100% FC, and a3 = 80–100% FC) and N fertilizer rates (b1 = 0 kg N ha−1, b2 = 100 kg N ha−1, and b3 = 200 kg N ha−1) significantly different at p < 0.05 (*), and (b) leaching losses (kg NO3− ha−1) in the extremely wet and average years.
Applsci 11 07352 g012
Table
Table 1. Physical and chemical properties of the soil at the study site.
Table 1. Physical and chemical properties of the soil at the study site.
Physical Properties
DepthSiltClaySandPRCACPWPPD
(cm)(%)(%)(%)(%)(%)(%)(%)(g cm−3)
0–3064.732.52.844.839.65.223.72.75
Chemical Properties
DepthpHAl-P2O5Al-K2OOrganic matterCaCO3
(cm)H2OKCl(mg/100 g)(%)(%)
0–305.596.6026.4029.702.551.25
P = porosity; RC = retention capacity; AC = air capacity; PWP = permanent wilting point; FC = field capacity; SAT = saturation; PD = particle density.
Table
Table 2. Irrigation water quality.
Table 2. Irrigation water quality.
ParameterSymbolUnitRangeResult
ReactionpH1–146.0–8.57
Sodium adsorption ratioSARme/L0–1511
Electrical conductivityECwdS/m0–30.97
NitrogenNitrateNO3-Nmg/L0–100.68
NitriteNO2-Nmg/L0–100.0012
AmmoniumNH4-Nmg/L0–50.429
Phosphate PO4-Pmg/L0–20.865
Sulfate SO4-Sme/L0–201.898
Chloride Cl−me/L0–300.449
Calcium Ca2+me/L0–203.444
Magnesium Mg2+me/L0–55.076
Sodium Name/L0–400.718
Iron Femg/L0–50.12
Table
Table 3. Granular matrix sensors’ (GMSs’) calibration results.
Table 3. Granular matrix sensors’ (GMSs’) calibration results.
Average
Calibration 1Calibration 2Calibration 1Calibration 2
N2020cbarkgcbarkg
81.430.74674.510.763
Min.0.000.00Correlation coefficient
Max.199.00199.00
Std. Dev.55.8762.90−0.91−0.86
Table
Table 4. Amount and form of N fertilizers.
Table 4. Amount and form of N fertilizers.
Fertilizationb1 (0 kg N ha−1)b2 (100 kg N ha−1)b3 (200 kg N ha−1)Fertilizer
Basic/autumn033.566.5Urea (46% N)
Pre-sowing033.566.5Urea (46% N)
1st side dressing016.532.5CAN (27% N)
2nd side dressing016.532.5CAN (27% N)
Table
Table 5. Irrigation water use efficiency (IWUE) and fertilizer use efficiency (FUE) in different weather conditions, irrigation levels, and N fertilizer rates.
Table 5. Irrigation water use efficiency (IWUE) and fertilizer use efficiency (FUE) in different weather conditions, irrigation levels, and N fertilizer rates.
Growing SeasonExtremely WetExtremely DryAverage Extremely WetExtremely DryAverage
IWUE (kg ha−1 mm−1) FUE (kg ha−1 kg−1)
a2−3.519.5312.90b228.5031.909.30
a3−6.1911.877.68b330.6020.757.20
a2b2
b12.579.0511.60a133.2027.3011.70
b2−12.5711.438.57a227.9029.806.40
b3−10.5718.198.40a324.3038.309.70
a3b3
b10.384.6912.61a130.8017.208.75
b2−8.109.1811.80a232.2022.005.95
b3−7.249.3111.18a326.8022.857.00
IWUE = irrigation water use efficiency (kg ha−1 mm−1); FUE = fertilizer use efficiency (kg ha−1 kg−1); a1 = rainfed; a2 = 60–100% field capacity (FC); a3 = 80–100% FC; b1 = 0 kg N ha−1; b2 = 100 kg N ha−1; b3 = 200 kg N ha−1.
Table
Table 6. Significance of irrigation, N fertilizer rate, and sampling time for soil N level.
Table 6. Significance of irrigation, N fertilizer rate, and sampling time for soil N level.
Extremely WetExtremely DryAverage
FLSDFLSDFLSD
0.050.010.050.010.050.01
a0.054 n.s.0.02340.05401.001 n.s.0.02110.04871.714 n.s.0.00590.0135
b3.275 n.s.0.03230.04890.077 n.s.0.01200.018215.434 **0.00470.0071
c0.215 n.s.0.03150.049064.689 **0.00210.029028.012 **0.00750.0091
a—irrigation; b—N fertilizer rate; c—soil sampling time; n.s.—non-significant; **—p < 0.01.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Marković, M.; Šoštarić, J.; Josipović, M.; Atilgan, A. Extreme Weather Events Affect Agronomic Practices and Their Environmental Impact in Maize Cultivation. Appl. Sci. 2021, 11, 7352. https://doi.org/10.3390/app11167352

AMA Style

Marković M, Šoštarić J, Josipović M, Atilgan A. Extreme Weather Events Affect Agronomic Practices and Their Environmental Impact in Maize Cultivation. Applied Sciences. 2021; 11(16):7352. https://doi.org/10.3390/app11167352

Chicago/Turabian Style

Marković, Monika, Jasna Šoštarić, Marko Josipović, and Atilgan Atilgan. 2021. "Extreme Weather Events Affect Agronomic Practices and Their Environmental Impact in Maize Cultivation" Applied Sciences 11, no. 16: 7352. https://doi.org/10.3390/app11167352

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop