Spatial Variability of Yield and Nitrogen Indicators—A Crop Rotation Approach

: The division of an arable ﬁeld into zones of di ﬀ erent productivity requires a reliable, discriminatory tool. This hypothesis was validated by analyzing the spatial variability of yield and N indicators in the crop rotation of winter oilseed rape (WOSR) / winter triticale (WTR) during 2016 / 2017 and 2017 / 2018 in a ﬁeld of 30 ha (Przeb˛edowo, Poland). The direct, measurable variables were: yield, N accumulated in—seeds / grain and crop residues, mineral N in spring, and harvest. The basic N indicators were total N uptake (TN), N-partial factor productivity, and N balance (N b ). The attainable yields of WOSR and WTR were 4.93 and 6.51 t ha − 1 , and a yield gap of − 2.04 and − 2.10 t ha − 1 . The management of 50 kg of the non-used N by crops, i.e. nitrogen gap (NG) could cover 36% and 65% of the yield gap (YG), respectively. The N b , based on N input (N in = N min + N f ) and TN, was the key ﬁeld indicator, deﬁning both yield and NG. Geostatic parameters, i.e., the nugget to sill ratio, spatial dependence range, and mean correlation distance, were very stable ( ≤ 0.2–0.17; 94–100 m; 28 m for WOSR and WTR). The spatial stability of N b , irrespective of the crop and growing conditions, corroborates its suitability for discriminating high and low-productivity ﬁeld zones.


Introduction
The continuous growth in the human population requires an adequate food supply, whose delivery depends on increased yields of the main crop plants [1][2][3]. The absolutely basic food production factor is breeding progress, resulting in new, efficient varieties [4,5]. The primary agronomic factor is nitrogen (N); its available amount in the soil/plant system is necessary to exploit the potential of the currently grown variety. The yield trends of main crops during the last century corroborate this conclusion, showing high similarities with trends of N use in agriculture [1,6]. As stressed by numerous authors, the success of the Green Revolution was due to interaction between the yield potential of new varieties and the simultaneous increase in the consumption of N fertilizer, which provided crops with strong protection control against diseases and pathogens [1,3]. The consumption of N will also be the key yield driver of crop plants in the coming decades [7,8]. Due to its very complex impact on crop growth and the development of its yield components, N fertilization requires deep scientific knowledge on the one side and high practical skills by farmers on the other. In spite of considerable progress in understanding the N uptake process and its transformation pathways during the vegetative crop [29][30][31]. The third N pool is its amount released from soil organic pools during the growing season [29,32]. Hence, far, the knowledge of this pool size is a typical black-box for both scientists and farmers. Research efforts to determine the size of this pool require the achievement of two objectives, i.e., (i) a reliable estimation of the N quantity released from soil resources, and (ii) the implementation of data obtained into fertilizer recommendations. In spite of hundreds of laboratory tests and models, these efforts have not been satisfactory [27,33]. The best way to overcome this limitation is to divide a field into high-and low-productive zones based on N supply. The main target of the field zonation is to recognize the in-season potential of different field zones to N release. It can be assumed that N release from soil resources and its subsequent supply to the currently grown crop differs between high-(yield gain) and low-yielding field zones (potential yield loss) [19,22].
In fields with a highly variable N min content, both vertical and spatial, the uniform N management (UNM) strategy, which still dominates in crop plant fertilization, leads to the under or over-fertilization of some areas of a field [19,26]. The outcome of this strategy for farmers is a self-created risk, both economic and environmental. Understanding the spatial variability in N min supply from the soil during the growing season to the currently grown crop is a very important step in the development and effective in-season N management strategy.
The objectives of the study were (i) to identify spatial variability in yields of winter oilseed rape (WOSR) and winter triticale (WTR) grown in a crop rotation, (ii) to identify the size of the nitrogen gap (NG), (iii) to select the best set of N indicators, i.e., those with the potential to discriminate N field zones differing in N productivity.

Site Description
The experimental object was a field of 30 ha, located near the village of Przebędowo, Poland (52 • 35 11.2 N and 17 • 00 8.7 E), lying on the 75 m ASL (Figure 1). The field has a flat topography with a relative difference of one m. The soil texture varies from sand to loamy sand in the topsoil and from loamy sand to sandy loam in the subsoil, classified as Albic Luvisol. The content of C org ranged from 0.92 to 2.78% and pH from 5.1 to 7.1. The content of available water ranged from 36.6 to 67.7 mm in the topsoil and from 82.3 to 200 mm in the subsoil. The content of available P, K, and Mg was in ranges suitable for winter oilseed rape production (Table 1).
The local climate, classified as intermediate between Atlantic and Continental, is seasonally variable ( Table 2). Precipitation during the period extending from January to July amounted to 574 mm in 2017, including 217 mm in July, and to 323 mm in 2018, including 93 mm in July. In May and June 2018, critical months for yield components of triticale development, the amount of rainfall was extremely low, amounting to 59 mm. Air temperatures in both years were higher in comparison to the respective long-term averages.    The local climate, classified as intermediate between Atlantic and Continental, is seasonally variable ( Table 2). Precipitation during the period extending from January to July amounted to 574 mm in 2017, including 217 mm in July, and to 323 mm in 2018, including 93 mm in July. In May and June 2018, critical months for yield components of triticale development, the amount of rainfall was extremely low, amounting to 59 mm. Air temperatures in both years were higher in comparison to the respective long-term averages.

Agronomic Operations
The field studies were based on a two-year cropping sequence: winter oilseed rape (WOSR)/winter triticale (WTR) conducted in two consecutive growing seasons: 2106/2017 and 2017/2018, respectively. Winter barley was a forecrop for WOSR. Standard tillage technology was applied for soil preparation for WOSR. Immediately after winter barley harvest, phosphorus and potassium fertilizers were applied on the entire field, and shallow stubble plowing (10-12 cm) + harrowing was done. Three weeks later, Agronomy 2020, 10, 1959 5 of 27 a standard plowing at a depth of 25 cm was carried out with simultaneous soil compaction with a Campbell roller. Seedbed preparation and seeding were conducted immediately after plowing. Brendy, a population variety characterized by a high-yielding potential for medium fertile soils, was sown on 22 August 2016. The number of seeds for sowing, based on 1000 seed weight, was adjusted, aimed to reach a plant density of 40-50 plants m 2 after emergence. Plants were harvested at the end of July from an area of 3 × 1 m 2 at each sampling point when the moisture content of seeds was 8% dry weight.
Field preparation for WTR begun directly after WOSR harvest, comprising a shallow stubble plowing (10-12 cm) + harrowing. A control of the postharvest emerging OSR seeds and weeds was done twice during summer by harrowing. In the middle of September, a standard plowing at a depth of 25 cm was carried out. Seedbed preparation and seeding were conducted two weeks later. The Rotondo variety, which is suitable for growing on medium fertile soils, was sown on 28 September 2017. The amount of grain for sowing, based on 1000 seed weight, was adjusted, reaching a plant density of 300-350 plants m 2 after emergence. Plants were harvested at the end of July/beginning of August from an area of 3 × 1 m 2 at each sampling point when the moisture content of seeds was 15% dry weight. A crop-specific program to control weeds, pests and diseases was conducted in accordance with standard farm practice for each of the tested crops, following integrated pest management (IPM) principles.

Collection of Study Materials and Chemical Analyses
The coordinates of samples were recorded using a handheld GPS device and then exported to a computer. The coordinates were then converted into a point feature class in ArcGIS Pro and connected with the data describing yield parameters and N indicators. The points, recorded originally in the World Geodetic Survey 1984 (WGS 84) coordinate system were projected onto the Poland CS92 format.
Composite soil samples were collected from each point of the field twice a year: (i) at the beginning of each spring season for winter crops; (ii) and after harvest of oilseed rape and triticale in July/August. The soil was sampled in triplicate from each observation point. Soil samples were taken at three depths: 0.0-0.3 m, 0.3-0.6 m, and 0.6-0.9 m. The total number of soil samples (observations) for WOSR and WTR totally equaled 660 (330 for each crop and 165 for each sampling date). The mineral forms of nitrogen, i.e., N min (NH 4 − and NO 3 − ), were determined in "fresh" soil samples within 24 h after sampling.
Twenty grams of soil were shaken for 1 h with 100 cm 3 of 0.01 M CaCl 2 solution (soil/solution ratio 5:1; m/v). Concentrations of NH 4 − and NO 3 − were determined by the colorimetric method using flow injection analyses (FIAstar5000, FOSS) after filtering through Munktell 3 h filter paper. The method of analysis for NO 3 − concentration consists of two basic steps: a reduction from nitrate to nitrite using a cadmium column and then colorimetric determination of nitrite, based on the Griess-Ilosvay reaction with N-(1-naphthyl)ethylene-diamine dichloride as a diazotizing agent. Color measurement was done at a wavelength of 540 nm. To determine NH 4 − , a special FOSS ammonia indicator (a mixture of cresol red, bromocresol purple and bromothymol blue) was applied. The measurement was made at a wavelength of 590 nm. The total soil mineral nitrogen concentration (N min ) was the sum of NH 4 − and Total N concentrations in plant tissues were measured by harvesting aboveground plant material for each crop at the BBCH 89 growth stage. The harvested plant sample was partitioned into subsamples of seeds/grain and harvest residues (straw + dead leaves + stubble) and dried (65 • C). Nitrogen concentrations were determined using a standard macro-Kjeldahl procedure, with an accuracy of 0.1 mg N. The total N content in plant materials was calculated based on the measured nutrient concentration and mass of each crop component, i.e., grain/seed or straw.

Nitrogen input efficiency
6. Total N input efficiency where: Y-seed yield, seed yield, t ha −1 or kg ha −1 ; TN-total N uptake, kg ha −1 ; N a , N r -amount of N in seeds/grain, and harvest residues at BBCH 89, kg ha −1 , respectively; N min -the amount of mineral N at the WOSR spring regrowth, kg ha −1 ; N minr -the amount of mineral N after WOSR harvest, kg ha −1 . N f -N fertilizer rate, kg ha −1 ; TN-total amount of N in WOSR at harvest.
C. Yield gap, (YG) and nitrogen gap (NG) calculation The following set of equations was applied to calculate both indices: 1. Partial factor productivity of N in PFP Nin = Y/N in (kg seeds kg −1 N Nin ) 2. Maximum attainable yield Y att = cPFP Nin ·N in (t, kg ha −1 ) 3. Yield gap 4. Nitrogen gap NG = YG/cPFP Nin (14) where PFP Nin (kg CUs kg −1 N) is the unit nitrogen productivity as a function of total nitrogen input in the system at the onset of spring vegetation (N in ) and the actual yield (Y; kg CUs). To delineate the role of PFP Nin on yield, the critical value of PFP Nin was defined. In this study, the critical PFP Nin (cPFP Nin ) was calculated as the average of the third quartile (Q 3 ) of PFP Nin values measured for each crop in the studied year. To determine the cPFP Nin , the calculated PFP Nin values were ranked in ascending order. The third quartile comprises values above the 75th percentile, i.e., representing 12.5% observations with the highest PFP Nin values. The cPFP Nin is the average of the PFP Nin values lying between the 75th percentile and the highest value of the considered data set.

Statistical Analyses
Three groups of statistical methods were used to evaluate the parameters of yield and indicators of N management. In the first step, the original data sets were analyzed for parameters of descriptive statistics, including mean, minimum, maximum, standard deviation, and coefficient of variation (CV). The normality of distribution of particular characteristics was evaluated based on the Kolmogorov-Smirnov (K-S) test, the skewness and kurtosis, and coefficient of variation (CV). Pearson's correlation coefficients for the studied yield and N characteristics were calculated to generate the correlation coefficient matrix. The relationships between variables representing soil properties were analyzed by principal component analysis, PCA (StatSoft, Inc., Tulsa, OK, USA, 2013).
The raw and recalculated data were checked for normality, and the logarithmic transformation was applied for attributes that did not have a normal distribution. An experimental semi-variogram of every attribute listed in Tables 5, 6, 9, and 10 was computed using the ArcGIS Pro Geostatistical Analyst module. The semi-to the variograms were calculated according following formula: (15) where h is the lag distance, N(h) is the number of pairs for distance h and X i and X i + h relate to the value of the variable at locations separated by the distance h. For each property that had a semi-variogram calculated, a model was fitted. The analyzed properties were not found to be anisotropic; therefore, the computed semi-variograms were omnidirectional. These fitted models were described by three major parameters, the nugget (C 0 ), the sill (C + C 0 ) and the range A 0 . The medium correlation distance (MCD) for each attribute was also calculated, according to the formula: Classes of spatial dependence for the soil attributes were defined based on the ratio of the nugget to the sill. Values of ratio below 0.25 signify strong spatial dependence; those between 0.25 and 0.75 were considered moderately spatially dependent, while those over 0.75 have weak spatial dependence.
Mapping of the variability of analyzed parameters was performed using kriging interpolation techniques. The parameters of models obtained from the variograms were used with the data. Four types of models were used, Spherical, Exponential, Circular and Gaussian. The prediction errors were estimated based on root mean square error (RMSE), medium error (ME), average standard error (ASE), relative root mean square error (rRMSE) and mean square deviance ratio (MSDR), calculated according to the formulae: where n is the number of samples, Z(X i ) are observed values at the location X i ,Ẑ(X i ) are values at the same location from prediction,σ 2 is the variance of the prediction and is the mean value of attribute x.
Values of these errors were calculated based on the leave one out cross-validation method.

Winter Oilseed Rape-Crop N Indicators
The Kolmogorov-Smirnov (K-S) normality test of yield and crop N indicators did not show the normal distribution of original data. The normality of distribution was also evaluated based on the distance between means and medians and for the range of skewness and kurtosis (Table 3). Kim [35] proposed a z-test for evaluation of the normality of raw data. This test is based on the ratio of the skewness/kurtosis to the standard error of a particular variable. According to Ghasemi and Zahediasl [36], the absolute threshold z-score for the normal distribution for a medium-sized sample (51 < n < 175) is ±<2.58. The z-score of 2.58 corresponds to prediction with a significance level (α) of ≤0.05. In the conducted study, for the skewness, this assumption was not fulfilled for N minr and Y att or for the kurtosis for unit nitrogen accumulation (UNA) and unit nitrogen productivity (UNP). Evaluation of yield and N variables based on the coefficient of variation (CV) was conducted using ranges proposed by Wilding and Drees [37]. According to the proposed ranges, CV < 15% is considered as low; 15% < CV < 35% as moderate, and >35% as high sample distribution. In this study, a low spatial distribution was recorded for Nitrogen Harvest Index (NHI), UNA, UNP, and the maximum attainable yield (Y att ). A moderate level of variability was recorded for total nitrogen accumulation by WOSR at harvest (TN). The CV values for three variables, such as oilseed yield (expressed in cereals units, Y-OSR-CUs), nitrogen accumulation in seeds (N a ), and nitrogen accumulation in harvest residues (N r ) , were only slightly higher than 35%, creating a borderline group between the moderate and the high class of CV. The highest CV, which exceeded 70%, was obtained for the yield gap (YG). This variable had, however, low skewness and kurtosis, fulfilling the assumption of a z-score of <2.58, in fact, indicating its normal distribution [36]. Yield in cereals units; N in -nitrogen input; N a -N accumulated in seeds; N r -N accumulated in harvest residues; TN-total N uptake by WOSR at harvest; NHI-nitrogen harvest index; UNA-Unit N accumulation; UNP-unit N productivity; PFP Nin -unit productivity of N in ; Y att -maximum attainable WOSR yield; YG-yield gap; K-S-Kolmogorov-Smirnov test.
In order to evaluate the relationships between the yield and crop N indicators, a principal component analysis (PCA) was applied. Three PCs with an eigenvalue above 0.70 (R 2 > 0.50) explained 91.7% of total variance. The first principal component (PC1) had the largest variance (55.6%) and significant loadings with six of ten variables. The highest loading was exerted by yield (Table A1). PC2 was associated with indicators of N productivity, i.e., UNA, which was positively, and UNP negatively correlated. PC3 had the highest loadings with NHI. The eigenvectors for the examined variables were broadly scattered on the first two PC axes ( Figure 2a). The closest to the absolute of 1 was Y-OSR-CUs, followed by a set of direct, measurable variables, such as N a , N r , and TN. These three variables, being significantly correlated to each other, exerted the strongest impact on yield (Table S1).
The key crop parameters of N productivity, such as partial factor productivity of N input (PFP Nin ) and YG, showed significant relationships with the direct, measurable variables (Table S1). They exerted the strongest and at the same time positive impact on Y (r = 0.86 and 0.81, respectively).

Winter Oilseed Rape-Soil N Indicators
Soil N indicators, evaluated on the basis of the K-S test, analogically as in the case of yield and crop N indicators, were not normally distributed (Table 4). The threshold z-core of ±<2.58 with respect to the skewness was fulfilled for Y-OSR-CUs, N min , N in , efficiency of total N input (NE int ), and NG. For kurtosis, this assumption was fulfilled for all variables, indicating a normal distribution [36]. The lowest spatial variability, as results from the analysis of CV, was found for N in and NE int . The significantly lower CV for N in as compared to N min was due to the application of 162 kg ha −1 of N fertilizer. The highest variability, exceeding 100%, was found for two N indicators, i.e., N balance (N b ) and N mineralized during the growing season (N gain ) (289% and 195%, respectively). The first one, i.e., N b, ranged from −235 to +178 kg ha −1 of N. The application of PCA showed that three PCs explained 98.2% of the total variance (Table A2). PC1, explaining 65.2% of the total variance, was associated with six of nine variables, from which N b had a positive loading. The eigenvector for N b was equal to the absolute of 1. However, it showed the opposite direction to the other five variables with negative loadings (Y-OSR-CUs, N gain , total N input (N int ), efficiency of N input (NE in ), and NG ( Figure 2b). As shown in Table S2, N b was significantly but negatively correlated with this set of variables. The highest correlation coefficient was recorded for NE in (r = −0.99). PC2, accounting for 21.7% of the total variance, was negatively associated with N minr and positively with NE int . Both variables showed an opposite direction on the PC2 axis. PC3, accounting for 11.3% of the total variance, was associated with N in , but it was not significantly correlated with yield.
Agronomy 2020, 10, x FOR PEER REVIEW 9 of 30 significantly but negatively correlated with this set of variables. The highest correlation coefficient was recorded for NEin (r = −0.99). PC2, accounting for 21.7% of the total variance, was negatively associated with Nminr and positively with NEint. Both variables showed an opposite direction on the PC2 axis. PC3, accounting for 11.3% of the total variance, was associated with Nin, but it was not significantly correlated with yield.

Spatial Distribution of Yield and N Management Indicators
The studied variables, as shown in Table 5, best fitted four types of semi-variogram models. The spherical model best described the spatial correlation structure of YG, N a , TN, NG, but the circular one of NHI. The Gaussian model, as reflecting Y-OSR-CUs, and N b indicates regular and smooth changes in the spatial structure of both variables. In contrast, the exponential model, as achieved for PFP Nin and N in , indicates the irregular, i.e., patchy distribution of both variables [38]. The scale of spatial dependence degree (SDD) was evaluated based on the ratio of structural variance, i.e., nugget (C 0 ), over the total variance (C 0 + C). Based on Cambardella et al. [39], two classes of spatial dependence for the variables shown in Table 5 were distinguished. A strong SDD, i.e., below 0.25, was found for NHI, N b , and N in . The latter two variables, representing output and input of N in the balanced equation, respectively, are the key indicators of N management for the cultivated crop in the given growing season [40]. The range of SDD within the studied field was the lowest for N a (82.3 m), and almost the same was recorded for TN, PFP Nin , and N in . The first three mentioned variables were significantly correlated with Y-OSR-CUs, in spite of a nearly 40-percentage higher SDD (Table S1). Spatial maps developed for Y-OSR-CUs, N a , TN, NHI were obtained by ordinary kriging, and for YG, PFP Nin , N b , N in , NG by simple kriging ( Table 6). The cross-validation results of kriged maps for the studied variables were well predicted as indicated for Mean Prediction Error (MPE), which ranged from 0.047 for N b to −0.070 to TN. The accuracy of prediction was also corroborated by Average Standard Error (ASE) and Root Mean Square Error (RMSE), which were very close to each other, as recorded for most of the studied variables. The prediction error for N b , as shown by Mean Squared Deviance Ratio (MSDR), was above the threshold value of 1.0 (1.3) [31]. The relative RMSE (rRMSE) for all studied variables was very low, i.e., below the threshold of 25%, clearly corroborating the accuracy of the conducted prediction for all studied variables [41]. The highest rRSME was recorded for Y-OSR-CUs (+14.01%) and the lowest for YG (−1.17%). YG-yield gap, t, kg ha −1 ; N a -N accumulated in seeds, kg ha −1 ; TN-total N uptake, kg ha −1 ; NHI-nitrogen harvest index,%; PFPN in -partial factor of productivity of N in , kg seeds kg −1 of N ni ; N b -N balance, kg ha −1 ; N in -nitrogen input (N min + N f = N fertilizer), kg ha −1 ; NG-nitrogen gap, kg ha −1 .
Yield, considered as the result of the interaction of numerous growth factors, should reflect both the N supply, i.e., N in and its use efficiency, as measured by the amount of N accumulated in the final yield expressed as TN [29]. The spatial distribution of yield was significantly affected by natural soil factors, as indicated by the high nugget because the sill was at almost the same value ( Table 5). The relationship between these two semi-variogram parameters of 1.0 indicates the lack of spatial variability for the WOSR yield [42]. This conclusion, in spite of a spatial range (SDD) of 120 m, is supported by the extremely low MCD (mean correlation distance), which reached 2 m.
The spatial distribution of WOSR-CUs yield showed, in spite of low statistical parameters, a presence of high and low-productive zones, extending in the SE-NW direction of the field (Figure 3a). The lack of spatial yield variability cannot, however, be explained by the impact of intrinsic soil properties because N min did not show a significant relationship with yield. The key reason for the sudden changes between yield zones was the N released during the growing season, as corroborated by a significant correlation of N gain with yield (Table S1). The spatial distribution of N in showed a high heterogeneity within the field. The N in variability was significantly related to N min , which contributed significantly to the total amount of N in the soil/plant system at the onset of WOSR growth. However, this basic N supply indicator was not correlated with yield. In addition, N in showed a patchy distribution on the field area (Figure 3b). The spatial distribution of N b showed the presence of parallel lying zones with a high and low N balance, extending in the SE-NW direction of the field (Figure 3c). The clearly determined N b zones can be explained by the nugget to sill ratio, which was 0.2. The high sill can be explained by two factors. The first one was a high amount of N min released from soil resources, indirectly stressing the effect of the intrinsic soil factor on yield [43]. On the other hand, the sill was probably affected by the difference in the sink strength of WOSR distribution within the field [17]. Both factors resulted in a gentle, smooth spatial distribution of N b , as supported by a reasonably high SDD of 100 m and MCD of 28 m. OK-ordinary kriging; SK-Simple kriging; Y-OSR-CUs-yield of WOSR recalculated in cereals units, t, kg ha −1 ; YG-yield gap, t, kg ha −1 ; Na-N accumulated in seeds, kg ha −1 ; TN-total N uptake, kg ha −1 ; NHI-nitrogen harvest index,%; PFPNin-partial factor of productivity of Nin, kg seeds kg −1 of Nni; Nb-N balance, kg ha −1 ; Nin-nitrogen input (Nmin + Nf = N fertilizer), kg ha −1 ; NG-nitrogen gap, kg ha −1 .

Winter Triticale-Crop N Indicators
The z-scores for the skewness of all variables fulfilled the absolute threshold of ±<2.58 [36], indicating the normal distribution of original data sets ( Table 7). The lowest CV of 6% was recorded

Winter Triticale-Crop N Indicators
The z-scores for the skewness of all variables fulfilled the absolute threshold of ±<2.58 [36], indicating the normal distribution of original data sets ( Table 7). The lowest CV of 6% was recorded for NHI, followed by UNA, UNP (10%). The maximum attainable yield (Y att ), in spite of a 5-fold magnitude of PFP Nin variability, showed low spatial variability (13%), ranging from 5.0 to 8.5 t ha −1 . The grain yield of triticale of around 5.0 t ha −1 ranged over 3.5-fold, i.e., from 2.24 to 8.07 t ha −1 . The highest variability of an absolute 79.1% was achieved for yield gap (YG), which ranged from −6.3 to +1.9 t ha −1 . For WTR, two PCs explained 81.6% of the total variance variability (Table A3). PC1 was associated with five of 10 variables, and all had positive loadings. These variables were significantly correlated Agronomy 2020, 10, 1959 14 of 27 with each other and with yield (Table S3). The highest score of 0.97 was obtained for Y-WTR and PFP Nin . The eigenvectors of these two variables on the PC1 axis were the same, but they showed the opposite direction on the PC2 axis (Figure 2c). The yield was significantly correlated with N a and TN (r = 0.95) and PFP Nin with YG (0.98) (Table S3). PC2 explained 26.2% of the total variance and was positively associated with UNP, and as expected negatively, with UNA and with Y att . Both these N indicators were significantly but weakly correlated with both Y-WTR and Y att . NHI, with a score of 0.66, was much closer to PC1 than to PC2. It was significantly, but only moderately, correlated with N a and Y-WTR, although not with Y att (r = 0.63, 0.58, 0.12, respectively).

Winter Triticale-Soil N Indicators
The threshold z-score of the absolute <2.58 was exceeded, but only with respect to the skewness, for N minr and NE in (Table 8). A low range of CV was observed only for N in . High variability was noticed for N b , N gain , and NG. The highest variability of 115%, ranging from −55.6 to 106.4 kg ha −1 , was found for N gain . In spite of the high CV, this variable showed an extremely low skewness (0.01) and kurtosis.  For soil N indicators, three PCs explained 98.7% of the total variance (Table A4). PC1 accounted for 52.5% of the total variance and had positive loadings with N b and negative with Y-WTR, N gain , NE in , and NG. The variance explained by PC2 was 26% and had negative loadings for N minr and N int . PC3 accounted for 20.3% of the total variance and was positively associated with N in . The eigenvectors for Y-WTR and NE in were close to the absolute of 1.0 on the PC1 axis, being significantly correlated with each other (Figure 2d; Table S4). On the opposite direction on the PC1 axis was N b , which was negatively correlated with Y-WTR (r = −0.79), but extremely strongly with NE int (r = −0.96) and NG (r = −0.96) (Table S4, Figure 2d). Yield showed the highest positive relationship with NE in (r = 0.89), followed by NE inT (r = 0.79). Two directly measured variables, i.e., N in and N minr did not show any significant relationship with Y-WTR.

Spatial Distribution of Yield and N Management Indicators-WTR
In general, spatial variability of yield and N management indicators for WTR was less differentiated as compared to WOSR (Tables 5 and 9). The spherical model of the semi-variogram fitted best to the spatial distribution of three variables, YG, N b , and N in , and the exponential to the other variables. For both crops in the studied crop rotation (WOSR/WTR), the spherical model was found to be typical for YG and exponential for PFP Nin . The first one indicates smooth changes in the YG spatial distribution (A 0 ). In contrast, the exponential model suggests a patchy distribution of this particular variable, as corroborated by the highest values of spatial dependence (A 0 ) and Mean Correlation Distance (MCD). The nugget to sill ratio was, in general, narrow. A strong spatial dependence (A 0 ≤ 0.25) was recorded for YG, NHI, PFP Nin , N b , and NG. All other presented variables were in the moderate class, but with the exception of TN, the evaluated ratios were close to the threshold of 0.25. The range of A 0 for Y-WTR was at the same level as recorded for WOSR. The MCD for Y-WTR was 11-fold higher as compared to Y-OSR-CUs, and together with the exponential form of semi-variogram, corroborates the patchy distribution of this variable. Table 9. Semivariogram parameters of yield and selected plant and soil nitrogen indicators-WTR.

Variables
Best  Spatial distribution maps for Y-WTR, N a , TN, NHI, and PFP Nin were obtained by ordinary kriging, and for YG, N b , N in , NG by simple kriging (Table 10). The cross-validation results of kriged maps for the studied variables showed a much better prediction as compared to WOSR. The MPE ranged from 0.010 for NG to −0.041 to N in . The high accuracy of prediction was corroborated by ASE and RMSE. The accuracy of prediction was confirmed by MSDR, which for most variables was close to 1.0. The relative RMSE for all studied variables was below 25%, clearly corroborating the accuracy of the predicted results. As in the case of WOSR, the highest value was for yield (+18.21%) and the lowest for YG (−1.75%). OK-ordinary kriging; SK-simple kriging; Y-WTR-yield of triticale, t, kg ha −1 ; YG-yield gap, t, kg ha −1 ; N a -N accumulated in seeds, kg ha −1 ; TN-total N uptake, kg ha −1 ; NHI-nitrogen harvest index,%; PFPN in -partial factor of productivity of N in , kg seeds kg −1 of N ni ; N b -N balance, kg ha −1 ; N in -nitrogen input (N min + N f = N fe rtilizer), kg ha −1 ; NG-nitrogen gap, kg ha −1 .
The spatial distribution of WTR yield showed the presence of distinct production zones, extending in an S-N direction in the east part of the field and to the SE-NW in the west part of the field (Figure 4a). The nugget to sill ratio of 0.34 indicates a moderate variability in the spatial distance, which was fully corroborated by the extensive areas of high and low productive zones lying next to each other. However, yield variability, as in the case of WOSR, was weakly related to the key intrinsic soil variable, i.e., N min . Yield showed a significant response to N gain , but not as strong as in the case of WOSR (Tables S2 and S4). The spatial distribution of N in , a variable related to the size of the N min pool at the onset of spring WTR growth, did not show large spatial changes in the studied field (Tables 9 and 10). In the entire field, two low N in zones can be distinguished (Figure 4b). The first one, localized in the west-central part of the field, extended from SE to NW, and the second one, localized in the west part of the field, extended from east to west. The second variable of the N input-output equation, i.e., N b showed a high nugget to sill ratio, indicating a strong spatial dependence. The spatial distribution of N b shows a gentle structure in changes of the high and low exploited N zones (Figure 4c).

Discussion
The spatial variability of N management in winter oilseed rape (WOSR)/winter triticale (WTR) crop rotation was evaluated based on crop yield parameters and plant and soil indicators of N management.

Yield-A Diagnostic Based on Crop Nitrogen Indicators
The yield of WOSR and WTR grown in the WOSR/WTR crop rotation, based on principal component analysis (PCA), was a variable with the highest factor loading for PC1. The dominance of yield corroborates the well-recognized fact that the yield of a currently grown crop is the final result of the interactional effect of three main growth factors, i.e., weather conditions during the growing season, soil conditions, and N management [27,43,44]. All yield parameters for WOSR were much higher compared to WTR. The mean yield of WOSR, expressed in cereal units (CUs), was higher by 38% in comparison to WTR. The difference in the maximum attainable yield (Yatt) for both crops was even higher, reaching 55%. The yield gap (YG) for WOSR was almost 2-fold wider compared to WTR. The main reason for these large differences was the completely different course of weather in consecutive growing seasons. In 2016/2017, water supply to plants, resulting from the total amount and in-season distribution of precipitation was optimal (Table 1). According to Berry and Spink [45], 300 mm of precipitation in the period, extending from the onset of flowering to the physiological maturity of WOSR, is a prerequisite of a high yield. This condition was fulfilled for WOSR, and this crop yielded at a high level. An optimal supply of water is necessary for reaching the full expression of yield components, decisive for the final yield, such as the number of seeds per unit area (seed density, SD), and seed weight (thousand seed weight, TSW) [17,44,46,47]. The full expression of basic yield components, as affected by favorable weather conditions, in fact, depends on the supply of N during the post-flowering WOSR growth [16]. In contrast to WOSR, the growth of WTR in the 2017/2018 growing season underwent under quite different weather. The total sum of precipitation

Discussion
The spatial variability of N management in winter oilseed rape (WOSR)/winter triticale (WTR) crop rotation was evaluated based on crop yield parameters and plant and soil indicators of N management.

Yield-A Diagnostic Based on Crop Nitrogen Indicators
The yield of WOSR and WTR grown in the WOSR/WTR crop rotation, based on principal component analysis (PCA), was a variable with the highest factor loading for PC1. The dominance of yield corroborates the well-recognized fact that the yield of a currently grown crop is the final result of the interactional effect of three main growth factors, i.e., weather conditions during the growing season, soil conditions, and N management [27,43,44]. All yield parameters for WOSR were much higher compared to WTR. The mean yield of WOSR, expressed in cereal units (CUs), was higher by 38% in comparison to WTR. The difference in the maximum attainable yield (Y att ) for both crops was even higher, reaching 55%. The yield gap (YG) for WOSR was almost 2-fold wider compared to WTR. The main reason for these large differences was the completely different course of weather in consecutive growing seasons. In 2016/2017, water supply to plants, resulting from the total amount and in-season distribution of precipitation was optimal (Table 1). According to Berry and Spink [45], 300 mm of precipitation in the period, extending from the onset of flowering to the physiological maturity of WOSR, is a prerequisite of a high yield. This condition was fulfilled for WOSR, and this crop yielded at a high level. An optimal supply of water is necessary for reaching the full expression of yield components, decisive for the final yield, such as the number of seeds per unit area (seed density, SD), and seed weight (thousand seed weight, TSW) [17,44,46,47]. The full expression of basic yield components, as affected by favorable weather conditions, in fact, depends on the supply of N during the post-flowering WOSR growth [16]. In contrast to WOSR, the growth of WTR in the 2017/2018 growing season underwent under quite different weather. The total sum of precipitation in May and June, i.e., during the critical months for the development of yield components, amounted to only 59 mm (Table 1).
The growth conditions of both crops can be evaluated on the basis of three crop N indices; the nitrogen harvest index (NHI), unit nitrogen accumulation (UNA) and unit nitrogen productivity (UNP). These indices, parametrizing the utilization efficiency (NUtE) of the supplied N, showed low spatial variability (coefficient of variation, CV < 15%). The extremely low CV for NHI indicates that N partitioning between seeds/grain and the vegetative parts of WOSR and WTR, irrespective of the course of weather during the period extending from the onset of flowering and maturity, was almost the same for the entire field. The conservative trait of the NHI indirectly indicates the occurrence of significant spatial differences in N amount accumulated by both crops in the period just before the onset flowering. It indirectly stresses differences in the spatial distribution of the sink strength, i.e., seed/grain density, as a basic component, defining yield [17]. The presented explanation is strengthened by two N efficiency indices, i.e., UNA and UNP. The conservative behavior of these sets of NUtE indices of N management was strongly expressed for WOSR, being, however, weakly correlated with yield. This result corroborates the opinion of Grzebisz et al. [17], who documented for WOSR that a higher N accumulation in seeds resulted in a higher seed density, which consequently leads to higher yield. This hypothesis was supported by the negative relationship of UNA, but a positive one of UNP with both NHI and yield. The lack of significant relationships of NHI, UNA and UNP with WOSR yield stresses a balanced partitioning of N taken up by plants during the growing season between seeds and vegetative plant parts, irrespective of the field zone. The observed net N uptake by both crops, but especially by WOSR from the soil N pool, released during the growing season, indirectly indicates a continuous supply of N to the growing pods and seeds during the post-flowering stages of WOSR growth. The spatial differences in WOSR yield were probably a result of N status during the phase of inflorescence development [48]. The seed density, as defined at the early stages of pod and seed growth, subsequently affects the plant requirement for N [16].
The NHI for WTR, in contrast to WOSR, exerted a strong and positive impact on yield. This means that the higher the N concentration in grain, the higher the obtained yield was. The negative relationship between UNA and WTR yield can be explained by the N dilution effect. The spatial differences in WTR yield were probably defined during the heading phase of plant growth. In contrast to WOSR, N supply to plants was limited due to drought, and as a result, N accumulated in grains underwent dilution. The dilution effect, which was revealed for WTR, indicates the post-flowering phase as crucial for N partitioning between grain and straw [49].

Nitrogen Gap
The main objective of the study was to determine the amount of N in the soil/plant system, which was or was not transformed during the growing season into yield. In fact, the key question is to define the extent of the nitrogen gap (NG), i.e., the amount of N which was not taken up by the currently grown crop [29]. The evaluation procedure was based on the assumption that the farmer's target is to achieve an attainable yield (Y att ) of 87.5% of the maximum yield in the studied field. It is quite clear that Y att under given soil/weather conditions depends on three main factors: (i) supply of water to plants, (ii) supply of N to plants during the critical stages of yield development, (iii) other soil and agronomic factors responsible for the efficiency of water and N [46,50,51]. This assumption is in agreement with the opinion that in rain-fed agriculture, the attainable level of yield gap closing is in the range of 70-85% of the water-limited yield (Y w ), i.e., the maximum yield in the given soil conditions [52,53].
The WOSR attainable yield, recalculated into cereals units (CUs), was 9.858 t ha −1 , which is equal to 4.929 t ha −1 of seeds ( Figure 5). The Y att obtained under a balanced N supply was at the potential level for WOSR in 2017 in Poland. In the studied field, it covered only 1% of the field area (Figure 3a). The average yield of WOSR of 3.455 t ha −1 (=6.909 CUs t ha −1 ) was high, covering 51% of the field area. This value can be treated as a borderline between high and low productive zones. The average harvested yield was 17% higher as compared to the national average (2.95 t ha −1 ) [54]. The efficient management of 50 kg ha −1 of the N present in the soil/WOSR system could have been the result of the increase of Y att by 1.45 t ha −1 of CUs (=0.725 t ha −1 of WOSR seeds). This value could cover 36% of the total yield gap (YG) of 4.084 t ha −1 , and therefore, it can be treated as a challenge for the farm. The excessive frequency of high-yielding spots, as determined by the mean correlation distance (MCD) of 2 m, was the main disadvantage is the reliable determination of high-, and low-production zones. The principal reason for their appearance was the weather, which created very favorable conditions for WOSR growth in the 2016/2017 growing season.
Agronomy 2020, 10, x FOR PEER REVIEW 22 of 30 efficient management of 50 kg ha −1 of the N present in the soil/WOSR system could have been the result of the increase of Yatt by 1.45 t ha −1 of CUs (=0.725 t ha −1 of WOSR seeds). This value could cover 36% of the total yield gap (YG) of 4.084 t ha −1 , and therefore, it can be treated as a challenge for the farm. The excessive frequency of high-yielding spots, as determined by the mean correlation distance (MCD) of 2 m, was the main disadvantage is the reliable determination of high-, and low-production zones. The principal reason for their appearance was the weather, which created very favorable conditions for WOSR growth in the 2016/2017 growing season. The average Yatt for WTR was 6.514 t ha −1 , i.e., it was lower by 34% with respect to Y-OSR-CUs, but higher by 30.5% with respect to the average for the field of 4.991 t ha −1 ( Figure 6, Table 3). The average WTR yield covered 49% of the total field area. It was by 57.4% higher as compared to the national average (3.17 t ha −1 ; [55]. The yield gap of 2.096 t ha −1 was substantial, constituting 32% of the Yatt. The efficient management of 50 kg ha −1 of the N present in the soil/WTR system could have been a result of the increase of Yatt by 1.37 t ha −1 . This value could cover 65% of the total yield gap (YG) of 2.086 t ha −1 . The postharvest content of mineral N (Nmin), i.e., Nminr, was by 20 kg ha −1 higher in comparison to WOSR (99.2 vs. 79.8 kg ha −1 ). The production zones of WTR yields, as shown by the exponential model of the semi-variogram, and a high MCD show were theoretically useful tools to distinguish high-, and low-production zones. However, the applicability of the WTR yield map with respect to the one for WOSR was biased by two factors. The first was drought, which significantly affected plant growth during the spring vegetation ( Table 2). The exponential model of yield variability could also be a result of excessive soil N mining by WOSR in highly productive zones, as indicated by significant differences in the distribution of Nin (Figures 3b and 4b).
The reduction in the N fertilizer (Nf) rate is one of the practical options to decrease the residual Nmin content after harvest [56]. The simulation conducted for WOSR, assuming both the lower Nf rate of 50 kg ha −1 , but the same yield, resulted in a higher NUE, consequently leading to N losses lower by 20% (82 vs. 102 kg ha −1 ). The strategy of Nf reduction should not, however, be applied to field zones of high N mineralization potential and at the same time of high fertility with respect to the content of available nutrients, responsive to both water and N-use efficiency [26]. Another solution to increase the efficiency of both the indigenous mineral N (Nmin) but especially Nf is to differentiate the sink strength of plants with respect to the productivity of field zones. It is well The average Y att for WTR was 6.514 t ha −1 , i.e., it was lower by 34% with respect to Y-OSR-CUs, but higher by 30.5% with respect to the average for the field of 4.991 t ha −1 ( Figure 6, Table 3). The average WTR yield covered 49% of the total field area. It was by 57.4% higher as compared to the national average (3.17 t ha −1 ; [55]. The yield gap of 2.096 t ha −1 was substantial, constituting 32% of the Y att . The efficient management of 50 kg ha −1 of the N present in the soil/WTR system could have been a result of the increase of Y att by 1.37 t ha −1 . This value could cover 65% of the total yield gap (YG) of 2.086 t ha −1 . The postharvest content of mineral N (N min ), i.e., N minr, was by 20 kg ha −1 higher in comparison to WOSR (99.2 vs. 79.8 kg ha −1 ). The production zones of WTR yields, as shown by the exponential model of the semi-variogram, and a high MCD show were theoretically useful tools to distinguish high-, and low-production zones. However, the applicability of the WTR yield map with respect to the one for WOSR was biased by two factors. The first was drought, which significantly affected plant growth during the spring vegetation ( Table 2). The exponential model of yield variability could also be a result of excessive soil N mining by WOSR in highly productive zones, as indicated by significant differences in the distribution of N in (Figures 3b and 4b).
The reduction in the N fertilizer (N f ) rate is one of the practical options to decrease the residual N min content after harvest [56]. The simulation conducted for WOSR, assuming both the lower N f rate of 50 kg ha −1 , but the same yield, resulted in a higher NUE, consequently leading to N losses lower by 20% (82 vs. 102 kg ha −1 ). The strategy of N f reduction should not, however, be applied to field zones of high N mineralization potential and at the same time of high fertility with respect to the content of available nutrients, responsive to both water and N-use efficiency [26]. Another solution to increase the efficiency of both the indigenous mineral N (N min ) but especially N f is to differentiate the sink strength of plants with respect to the productivity of field zones. It is well recognized that there is a strong dependency between the supply of N and the expression of yield components, as documented for OSR [29,48,57]. This practical solution is based on differentiation in oilseed rape seed density (SD) with respect to the production potential of a particular field zone. As documented by Yang et al. [58], differentiation in SD with respect to the production potential of field zones resulted in an increase of OSR productivity by 32% in low-yielding zones and by 20% in high-yielding zones. This solution clearly indicates that SD should be adjusted to the water and nitrogen capacity of the soil in the respective field zone [1,59,60].
Agronomy 2020, 10, x FOR PEER REVIEW 23 of 30 recognized that there is a strong dependency between the supply of N and the expression of yield components, as documented for OSR [29,48,57]. This practical solution is based on differentiation in oilseed rape seed density (SD) with respect to the production potential of a particular field zone. As documented by Yang et al. [58], differentiation in SD with respect to the production potential of field zones resulted in an increase of OSR productivity by 32% in low-yielding zones and by 20% in high-yielding zones. This solution clearly indicates that SD should be adjusted to the water and nitrogen capacity of the soil in the respective field zone [1,59,60].

Yield-A Diagnostic Based on Soil Nitrogen Indicators
The projected yield of a crop plant, provided there is a good water supply, depends on the amount of available N in the given soil/crop system and its production efficiency [1,11]. In fact, primary sources of N to the currently grown crop are both (i) indigenous N (Ni), i.e., N mineral measured mostly in spring, and (ii) the amount of applied Nf [29]. Spatial variability of Ni should be considered as a core of the implementation of any technology of N management [31]. In spite of available data and knowledge of spatial Ni variability within a field, in practice, a uniform N management (UNM) strategy of Nf application frequently dominates [19,30].
In order to obtain an answer on the worth of the primary N data for yield projection, i.e., referring to the amount of N introduced into the given soil/crop system (N input, Nin) and its quantity removed from the system (N output, Nout = TN), a N balance (Nb) was applied as a diagnostic tool [16,31]. As shown in Tables S2 and S4, both Nmin and Nin (Ni + Nf) were not significantly correlated with the yields of either crop, but significantly with the nitrogen gap. The significant impact of the residual Nmin on the yield of the succeeding crop was suggested by Baxter et al. [31]. In the studied case, however, no significant relationship was found between WTR yield and Nmin left by WOSR. On the other hand, the amount of N in seeds/grain and total N uptake showed significant relationships with the yields of both crops (Tables S1 and S3). As a rule of thumb, the lower the N balance (Nb), the higher the expected seed/grain yield may be. The yield of both crops increased in accordance with the decreasing Nb as shown by the developed equations:

Yield-A Diagnostic Based on Soil Nitrogen Indicators
The projected yield of a crop plant, provided there is a good water supply, depends on the amount of available N in the given soil/crop system and its production efficiency [1,11]. In fact, primary sources of N to the currently grown crop are both (i) indigenous N (N i ), i.e., N mineral measured mostly in spring, and (ii) the amount of applied N f [29]. Spatial variability of N i should be considered as a core of the implementation of any technology of N management [31]. In spite of available data and knowledge of spatial N i variability within a field, in practice, a uniform N management (UNM) strategy of N f application frequently dominates [19,30].
In order to obtain an answer on the worth of the primary N data for yield projection, i.e., referring to the amount of N introduced into the given soil/crop system (N input, N in ) and its quantity removed from the system (N output, N out = TN), a N balance (N b ) was applied as a diagnostic tool [16,31]. As shown in Tables S2 and S4, both N min and N in (N i + N f ) were not significantly correlated with the yields of either crop, but significantly with the nitrogen gap. The significant impact of the residual N min on the yield of the succeeding crop was suggested by Baxter et al. [31]. In the studied case, however, no significant relationship was found between WTR yield and N min left by WOSR. On the other hand, the amount of N in seeds/grain and total N uptake showed significant relationships with the yields of both crops (Tables S1 and S3). As a rule of thumb, the lower the N balance (N b ), the higher the expected seed/grain yield may be. The yield of both crops increased in accordance with the decreasing N b as shown by the developed equations: The constant of both equations indicates an input/output balance. Therefore, N b can be used as the key indicator of N management in the given field. It is worth stressing that in spite of the different weather in the studied growing seasons, both threshold values differed by only 10%. For both crops, the highest yields were obtained with the most negative N b values, indicating the net N gain by plants, i.e., those mining the intrinsic N pool. The higher R 2 , as achieved for WOSR, clearly documents that N mining covered a slightly larger area of the field (0.27 ha) as compared to WTR (0.04 ha) (Figure 4c). For triticale, the negative N b was incidental, without any impact on the dominant trend (Figure 4c). The yield increase, in spite of N in exploitation, was possible due to a net N release during the growing season from N resources (N gain ). The relationship between N b and the amount of N mineralized during the growing season (N gain ) showed the same direction as observed for yield: These four equations clearly show that the pool of available N was much better balanced in soil cropped by WOSR than by WTR. One kg of N released from soil resources during the WOSR growing season had the same production value as one kg of N present in the soil/plant system at the onset of spring vegetation. For WTR, its efficiency, as indicated by the direction coefficient of Equation (25), was lower by 20%.
The next aspect of N indicator evaluation is the relationship between N b and NG. Both N indicators are calculated in different ways. The studied crops showed a quite different impact on the relationship between both N indices. As shown in Figure 7, the negative NG, explicitly indicating the non-exploited N min pool, began at N b equal to −99.1 kg ha −1 . The balanced N b ( 0.0 ) resulted in an NG of −77.8 kg ha −1 , indicating a surplus of N in . Triticale showed a significantly different relationship between N b and YG. As shown in Figure 8, negative NG began at N b equal to +16.2 kg ha −1 of N, clearly indicating a surplus of N in in the soil/crop system. The balanced N b resulted in a net N min gain of 18.6 kg ha −1 . Under favorable weather conditions, as recorded in the 2016/2017 growing season, strong mining of the N min released during the growing season does not mean its full depletion in the high-yielding zones of the field. A high rate of N min release also took place in the low-yielding field zones. In the case of WOSR, the NG pool was dominated by N released during the growing season. Under quite the opposite weather conditions, as recorded in the 2017/2018 growing season, the N min release during the growing season was of secondary importance. The increase in the NG pool was high due to the low exploitation of N present in the crop/soil system at the onset of spring vegetation. The main reason for the increase of this pool was not drought, as suggested in numerous papers [1,32], because N release from soil resources did not stop, as shown in Table 6. The key reason for the low exploitation of N in was a shortage of N supply to plants during the development of yield components, subsequently leading to a decrease of sink capacity. This conclusion is supported by almost a double reduction in total N uptake by WTR as compared to WOSR (Tables 3 and 7).
The relationship obtained in this study is, to some extent, contradictory to the option that assumes an N f rate reduction, which is suggested as the best management N solution, leading to the decrease in the amount of the residual N in the soil/crop system [10,27]. The dependency obtained indicates this option is suitable for the low-yielding field zones. A productive-oriented strategy should, however, rely on a sink strength increase by the currently cultivated crop [17,58]. Winter oilseed rape is an excellent example of a crop sensitive to this production strategy, provided reasonable high fertility of soil [17]. WOSR production success depends on the ability of the plants to take a set of nutrients during the post-flowering growth, supporting the growth of pods and seeds, consequently increasing the seed density [16,17,48]. The problem of reasonable high soil fertility does not, in fact, refer to the content of P, K or Mg in the topsoil. As it has been recently documented, not only N but also the abovementioned nutrients are taken up from the entire soil profile [24,25,61]. Processes responsible for the N min pool size are quantitatively associated with active pools of other nutrients [24,25]. It can, therefore, be concluded that the determination of effective production zones in a given field cannot be conducted independently of the other nutrients determining N use efficiency.

Conclusions
The N balance (N b ), based on N input (N min + N f ) and N output (TN), was the key N indicator, defining both the yield and nitrogen gap of both crops. Its significant impact on these two characteristics was due to the high accuracy of the prediction of low and high-yielding zones. The applicability of N b as the key N indicator was not defined by the amount of N min in the soil/plant system at the onset of spring vegetation. Neither N min nor N in significantly affected the yield of either crop. The net N b was deeply related to the amount of N in the crops at harvest, indirectly indicating the importance of the sink strength, i.e., the number of seeds/grain per unit area as the driving factor of yield. The usability of N b as a tool for determining arable field zones of different productivity was corroborated by PCA, which indicated yield and N b as dominant loadings in the soil/plant system. The geostatic parameters, such as the nugget to sill ratio, spatial dependence range), and mean correlation distance for N b were very close or the same (≤0.2-0.17; 94-100 m; 28 m, respectively for WOSR and WTR), clearly indicating both a strong spatial dependence and at the same time the spatial stability of this N indicator, irrespective of the crop and the growing conditions. Supplementary Materials: The following are available online at http://www.mdpi.com/2073-4395/10/12/1959/s1, Table S1. Matrix of Pearson's correlation coefficients between yield and plant indices of N management by WOSR. n = 55. Table S2. Matrix of Pearson's correlation coefficients between yield and soil indices of N management by WOSR. n = 55. Table S3. Matrix of Pearson's correlation coefficients between yield and plant indices of N management by triticale, n = 52. Table S4. Matrix of Pearson's correlation coefficients between yield and soil indices of N management by triticale, n = 52.
Funding: This research received no external funding.

Conflicts of Interest:
The authors declare no conflict of interest. Table A1. Pearson's correlation matrix between yield selected plant nitrogen variables and PCA factors for winter oilseed rape, n = 55.