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Article

Best Linear Unbiased Predictions of Environmental Effects on Grain Yield in Maize Variety Trials of Different Maturity Groups

by
Marina Zorić
1,
Jerko Gunjača
2,3,
Vlatko Galić
4,
Goran Jukić
1,
Ivan Varnica
1 and
Domagoj Šimić
3,4,*
1
Croatian Agency for Agriculture and Food, Vinkovacka cesta 63c, 31000 Osijek, Croatia
2
Faculty of Agriculture, University of Zagreb, Svetosimunska cesta 25, 10000 Zagreb, Croatia
3
Centre of Excellence for Biodiversity and Molecular Plant Breeding, Svetosimunska 25, 10000 Zagreb, Croatia
4
Agricultural Institute Osijek, Juzno predgradje 17, 31000 Osijek, Croatia
*
Author to whom correspondence should be addressed.
Agronomy 2022, 12(4), 922; https://doi.org/10.3390/agronomy12040922
Submission received: 15 March 2022 / Revised: 9 April 2022 / Accepted: 11 April 2022 / Published: 12 April 2022
(This article belongs to the Special Issue Predictions and Estimations in Agricultural Production under a Changing Climate)
Browse Figures
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Abstract

:
Development of new cultivars and agronomic improvements are key factors of increasing in future grain yield in maize grown in environments affected by climate change. Assessment of value for cultivation and use (VCU) reflects the results of latest breeding efforts showing yield trends, whereby external environmental covariates were rarely used. This study aimed to analyze several environmental effects including stress degree days (SDD) on grain yields in Croatian VCU trials in three maturity groups using linear mixed model for the estimation of fixed and random effects. Best linear unbiased predictions (BLUPs) of location-year interaction showed no pattern among maturity groups. SDD showed mostly non-significant coefficients of regression on location BLUPs for yield. Analyzing location BLUPs, it was shown that the effect became consistently stronger with later maturity, either positive or negative. The effects of management might play more critical role in maize phenology and yield formation compared with climate change, at least in suboptimum growing conditions often found in Southeast Europe. To facilitate more robust predictions of the crop improvement, the traditional forked approach dealing with G × E by breeders and E × M by agronomists should be integrated to G × E × M framework, to assess the full gradient of combinations forming the adaptation landscape.

1. Introduction

Long-term trials for assessment of value for cultivation and use (VCU) [1,2] applying during maize registration i.e., maize variety trials are valuable source of information on general maize yield trends [3,4] and across maturity groups [5]. However, the genetic values of genotypes in multi-environment VCU trials is hidden by variation caused by complex genotype by environment interaction effects [6]. In order to disentangle this complexity in typically unbalanced VCU trials, mixed-effect models were used, which allow attributing yield variability to randomly distributed independent effects. In this context, the means can be analyzed adjusting by best linear unbiased predictions (BLUPs) and best linear unbiased estimators (BLUEs) [7]. BLUP is a method with favorable feature of shrinkage of estimators towards the mean, reducing its variance, but increasing its predictive accuracy [8]. The method was first developed in animal breeding for predicting the animal breeding values [9], receiving afterwards considerable attention in plant multi environment trial analysis [10,11,12]. In contrast to the BLUE methodology calculated using fixed-effect linear models, mixed models with fixed and random effects allow calculation of the random effect matrix needed to extract BLUPs [13].
Yield information on varieties and environments in VCU trials can be analyzed applying different models aiming at describing as much of the yield variation between varieties and their interactions with the environments as possible using external environment-specific covariates which can detect (a) biotic stresses. Several environmental (climatic) indices were proposed incorporating weather variables to enable reliable within-season predictions in new environments [6,14]. In Southeast Europe, an aridity index [15] and an improved Palmer Drought Severity Index [16] were used among others for quantifying drought stress in maize. It was demonstrated though, that extreme heat as a stressor could have more critical role for maize production than drought in the US [17] corroborating previous statistical studies of rainfed maize yields showing a strong negative yield response to accumulation of extreme temperatures (>30 °C) and relative weak response to seasonal rainfall. The concept of Stress Degree Days (SDD), ref. [18] utilized for decades to measure heat stress, is also appearing in recent studies e.g., [19,20]. Buhiniček et al. [20] used a long-term pre-registration yield trials in Croatia and official weather data to show that irregular climatic conditions during maize growing season are becoming more prominent in the last three decades. In that study based on experimental and simulation data, it was demonstrated that choosing right maturity group should play even more important role in the future. However, genotype by environment interaction effects were not captured.
Globally, hybrid relative maturity in maize, i.e., plant cycle duration is becoming more important in the context of climate change to maximize yield, whereby farmers should continuously adapt maize cycle duration and planting dates to the diversity of environmental conditions [21,22]. Several systems for rating of hybrid maize maturity are employed around the world. Various thermal units are used in North America including relative maturity (RM) and growing degree days (GDD) [23], whereas the uniform method recommended by Food and Agriculture Organization of the United Nations (FAO) [24] is still used in Europe [25]. Similar to RM, FAO maturity groups represent the length of time necessary for a hybrid to reach harvest-ready moisture determined relatively to a standard hybrid. This maturity classification is an important component for maize VCU evaluation as well as for recommended or national lists of varieties released for commercial use [24].
The objectives of this study were to determine environmental effects on grain yield using BLUPs and BLUEs across three maturity groups in Croatian VCU maize trials over the last two decades, and to evaluate the use of SDD as a climatic covariate to determine the impact of climate change on grain yield in maize.

2. Materials and Methods

Yield data from official Croatian variety trials assessing VCU of maize in three maturity groups (FAO 300, FAO 400 and FAO 500) for the period 2001–2019 were used in this study. Data were collected from five locations in northern (continental) part of Croatia: Zagreb (Zg: N 45.7; E 16.3), Kutjevo (Ku: N 45.3; E 17.9), Osijek (Os: N 45.5; E 18.5), Beli Manastir (Bm: N 45.7; E 18.6) and Vukovar (Vu: N 45.3; E 19.1). The adjacent yield trials of the three maturity groups were set on marsh gley partly hydroameliorated (Zg), acid brown soil over clastite and metamorphite (Ku), semigley on leached loess (Os) and chernozem on loess (Bm and Vu) [26]. In total, there were 37–49 trials per maturity group in the 19-year period (Supplementary Table S1). The number of genotypes included all entries in all trials: control cultivars, the genotypes entering the first trial year, subsequent withdrawn genotypes by breeders, as well the genotypes (cultivars) finally released. All trials were set as randomized complete block designs with four replicates and were machine-planted on 11.2 m2 sized plots. Grain yield data were calculated on the 14% moisture basis. Standard cultural practices (fertilization, weed, pest, and disease control) for maize high-yielding production were used before and during crop growing. The data sets were unbalanced due to a number of genotypes leaving or entering the trials from one year to another.
Variance components were assessed within three maturity groups, FAO 300, FAO 400 and FAO 500. The general three-way mixed model used for modelling of grain yield variance was set as:
yijk = μ + (Gi) + (Lj) + (Yk) + (LYjk) + (GLij) + (GYik) + (GLYijk) + εijk
in which yield yijk was observed for i genotypes in j locations over k consecutive years. Main effect of year Yk was treated as fixed, whereas the effects of genotypes (Gi) locations (Lj) and interaction terms of genotype-by-location (GLij), genotype-by-year (GYik), genotype-by-location-by-year (GLYijk) and the error εijk were assumed to be independent and have constant variances over levels of effects. Locations were assumed to be crossed with years (LYjk) as most of the locations were used in most of the years. Interactions of genotypes by years and locations were calculated on the basis that most of the genotypes were screened for at least two years, whereas some genotypes (checks) are present in the dataset over more than seven years. All models were set using the restricted maximum likelihood (REML) in R [27] library lme4 [28]. The intraclass correlation coefficients (ICC) and Marginal R2/Conditional R2 values of the models were calculated in the sjPlot library [29] based on the methodology proposed by Nakagawa et al. [30,31]. The ICC quantifies the proportion of variance explained by random factors in multi-level or hierarchical datasets. The best linear unbiased predictions (BLUP) [12] of random effects and best linear unbiased estimates (BLUE) [32] of year effects (Figure S2) were extracted using the R’s coef() function. In our study, the focus was on environmental random effects and their BLUPs denoted as L_BLUPs and LY_BLUPs for L and LY effects, respectively.
Daily weather data for precipitation, air temperature (minimum and maximum) and solar radiation was obtained from the AGRI4CAST database [33] using information for the grids from west to east: 79,129 Zg (N 45.68793; E 16.30936); 78,134 Ku (N 45.35677; E 17.87238); 79,136 Os (N 45.53097; E 18.54105); 80,136 Bm (N 45.75453; E18.57620) and 78,138 Vu (N 45.25416, E 19.13910) for the period from April to October of each year. The impact of heat stress on grain yield was estimated using stress degree days (SDD) concept [18,19,20] calculated as
SDD ∞ 30 = ∑ t = 1 N D D t ,   D D t = { 0 ,   w h e n   T a < 30 T a − 30 ,   w h e n   T a ≥ 30 }
where t represents the daily time step, N is the total number of days in each growing period, DD is degree days, and Ta is air temperature. The SDD index was chosen as an environmental covariate in this study due to highest correlations with grain yield (data not shown). Correlations between some other climate covariates (growing degree days, precipitation, vapor pressure deficit) and grain yield were similar to those presented by Buhiniček et al. [20]: they were mostly weak and non-significant. A simple linear regression model was used for fitting the data for grain yield on SDD values across the five locations.
Simulations were performed using the APSIM platform [34] for the same period in three geographically distant locations included in the VCU trials (Zg, Ku, Os) selecting default variety options. Details were presented by Stepinac et al. [35]. Briefly, the maize genotypes representing three FAO maturity groups were Pioneer® cultivars P38H20 (FAO 300), P34K77 (FAO 400) and P33M54 (FAO 500) (Pioneer, Johnston, IA, USA). An uncalibrated model of APSIM evaluations was used assuming unchanged (invariant) genotypes with optimum planting density of 7.1 plants m−2 (FAO 300), 6.4 plants m−2 (FAO 400) and 6.0 plants m−2 (FAO 500) similar to the VCU trials. The intention of APSIM simulations was to assess relationships of observed/simulated average grain yields and BLUPs with SDD values across FAO groups in the locations where regression coefficients were not significant.

3. Results

The magnitudes of main variance component of genotype (G) were similar for all three maturity groups, whereby the G component for late maturity group of FAO 500 was somewhat smaller. The main variance component of location (L) was the largest for FAO 500 maturity group having generally high standard errors. The largest variance components in all three maturity groups were estimated for the environmental location × year (LY) interaction followed by the main G and L effects in FAO 300 and FAO 400 (Table 1). In FAO 500, the second greatest variance component was from the L effect. On the other hand, the GL effect was negligible in all maturity groups. The proportion of variance explained by the G effect was 8.82% averaged over the FAO groups.
ICC values between 0.76 and 0.84 indicate good repeatability, whereas heritability estimates were notably lower, between 0.40 and 0.53 due to relatively large size of interaction effects compared with the genotypic (G) effects. According to the ratio Marginal R2/Conditional R2, proportion of variance explained by the fixed factor Y ranged from 0.19 to 0.29. Overall, proportion of variance explained by both the fixed and random factors is >0.8.
Unadjusted mean values for grain yield over the 19-year period grouped in three FAO maturity groups were given in Supplementary Figure S1. Generally, mean values ranged broadly from 2.94 to 15.33 t/ha with similar results across the maturity groups. There were no significant yield trends over the period. Caterpillar dot plots of random LY effects showed the most positive values for the Os location in 2018 and the most negative values for Vu location in 2012 in all three FAO groups (Figure 1). When compared with other locations, Os and Vu locations varied the most in yield over the years having similar pattern across the maturity groups.
SDD values fluctuated considerably during the two-decade period ranging from around 30 in 2014 to 250 in 2012 for the Vu location (Figure 2). Generally, SDD values were alike at all five locations in a particular year, except for the period 2007–2009 and in 2012 when SDD values diverged to some extent. However, the observed SDD values were consistently higher in the locations of Eastern Croatia than those in west: Bm and Os in 2003 and Vu in very hot years of 2012, 2015 and 2017.
Regression analysis revealed mostly non-significant slope coefficients of the L_BLUPs on SDD values (Table 2). The exceptions were the coefficients in Bm and Vu locations at most instances: they were significantly positive in Bm and significantly negative in Vu. Thus, higher values of SDD at these two locations had an impact on grain yield particularly in Vu where the significance level of regression coefficient for all maturity groups was p ≤ 0.056. On the other hand, no discernible differences were found among the estimates across the maturity groups.
Average grain yield was the highest in Zg and Bm locations for all three maturity groups (Figure 3). In both locations, the highest yielding FAO group was the latest one (FAO 500) gradually followed by two earlier ones (FAO 400 and 300, respectively). No such pattern was observed for the FAO groups in other three lower yielding locations (Ku, Os and Vu). In the high-yielding location Bm, unadjusted means for grain yield between the maturity groups FAO 300 and FAO 500 were significantly different. There were no significant differences among maturity groups in all other locations.
By analyzing best linear unbiased predictions, it was revealed that L_BLUPs are somewhat different compared with unadjusted means over the locations. Markedly, the L_BLUPs became stronger with later maturity, either positive or negative (Figure 4). It was consistent across all five locations and three maturity groups. There is no geographic pattern though, indicating that the effect of location is discrete. Due to large standard errors of all L_BLUPs, there were no significant differences among maturity groups within a location.
Predicted grain yield according to APSIM simulated data averaged over the period 2001–2019 (Figure 5) was considerably lower in Zg and Ku locations than observed unadjusted means obtained at the same locations (Figure 3). Although non-significant, the highest simulated grain yields were in genotypes of early FAO 300 group in these locations. Observed unadjusted and simulated means for Os location were similar in all three maturity groups together with larger standard errors than those in Zg and Ku.
Correlations between grain yield and SDD were consistently negative across the three locations and the three maturity groups (Figure 6). Significant negative correlation coefficients were estimated in genotypes of all three FAO groups for average grain yield obtained by observed unadjusted means and APSIM simulated means, but not obtained by L_BLUPs. Notably, the respective correlations with grain yield estimated by observed unadjusted means and APSIM simulated means were similar. Correlations between grain yield obtained by L_BLUPs and SDD were mostly weak.

4. Discussion

By estimating the components of variance and analyzing their magnitude, it is possible to decouple the complexity of genotype by environment interactions. This is important for planning VCU experiments to determine optimum resource allocation [36,37] particularly in large areas where diverse climatic and geographical conditions with different cycle duration take place. In India [38], variance components for all effects used also in our study were quite close for all investigated maize maturity groups (extra early, early, medium and late). In our VCU trials where geographic conditions were not notably different, the greatest estimated variance component was the LY interaction for all three maturity groups. In the FAO 300 and 400 maturity groups, it is followed by a considerably lower proportion of the G effect, whereas in the FAO 500 group the next component of variance by magnitude is the L effect. In the German VCU maize trials [37], the prevailing variance for grain yield was from the L effect, followed by LY and G effects in all three (early, medium, late) maturity groups. Comparable results were presented for maize yield trials conducted in African [39,40] and Asian environments [41]. In yield trials of late maturing groups (FAO 500, 600 and 700) in different environments in Greece [42] the highest percentage of yield variation was explained by the main environmental effect (68.89%) when used a simple two-way genotype by environment model with no decoupling of L and Y effects, whereby the proportion of G effect was 3.95%. In our study, the proportion of variance explained by genotype was in average 8.82% compared with 11.56% in our previous research [5] and 6.65% obtained from the German VCU trials [37].
The means in our study were analyzed in the context of BLUPs and not of BLUEs as recommended by Robinson [8] giving several arguments for using it: (1) smaller expected mean square errors, (2) it is suitable in variety trials when the aim is to predict the future variety performance, and (3) it is appropriate for small-area estimation. As a rule of thumb, negative L_BLUPs may be declared as sites with low soil yield potential, whereas sites with positive L_BLUPs are sites with high soil yield potential. To our knowledge, our study is the first attempt to apply BLUPs of environmental effects to evaluate appropriateness of individual maturity groups of maize for particular growing area. However, soil yield potential can be evaluated precisely when a set of soil properties are known, such as rooting depth, topsoil structure or soil compaction [43]. The same is true for APSIM simulations which should be calibrated to generate results according to local soil data [44]. This is particularly important when soil type (chernozem) is the same such as in Bm and Vu, but soil yield potential seems to be different.
Over longer periods in VCU trials, yield increases are expected (as well as increases in other important economic traits) due to the trend of improvement of new cultivars through breeding programs [4,5]. However, in our study no considerable yield increase was observed for any of the maturity groups (Supplementary Figure S1). This general trend may be more or less noticeable due to the action of different climatic and agronomic factors, especially in the context of the growing impact of climate change on all major agricultural plant species [45]. Bönecke et al. [43] determined the effects of several agrometeorological factors on the development of German winter wheat yield in the period between 1958 and 2015 using 298 nitrogen (N) fertilization trials. For this purpose, they separated climatic from genetic and agronomic yield effects using several linear mixed effect models and estimating climatic impact based on the coefficient of determination for those models. The results indicate a general and strong effect of climatic changes on yield development, especially due to the increase in mean temperatures and heat stress during the grain-filling period. Except on days of heat stress with more than 31 °C, yields in locations with higher yield potential were less prone to adverse weather conditions than in places with lower yields.
In maize, it was observed that high temperatures above 30 °C play a more critical role than drought due to increased air moisture deficit and increased evapotranspiration [17,20,46]. The increase in air temperature had a negative effect on yield even under irrigated conditions [46]. Also, high temperatures can limit yields by reducing cycle duration [47]. In general, global maize yields are declining with climate change due to increasing in air temperatures. Zhu et al. [19] combined crop models, satellite observations, and field data to investigate how heat stress affects maize yield in the U.S. Midwest. When the effects of warming were decomposed into direct heat stress and indirect water stress, observational data suggest that the yield was reduced more by direct heat stress than by indirect water stress. They suggest that adaptation strategies should focus on heat stress during grain formation as it poses a marked threat to cause the decline in maize yields and that changes in management should be designed to adequately assess the effects of heat stress during different developmental stages.
Our results corroborate previous findings that SDD values had similar amplitudes for locations in Croatia for a given year [20]. The exceptions were the years 2007, 2008, 2009 and 2012 when SDDs differed slightly among locations. This could be relevant for interpreting genotype by environment in VCU yield trials. However, regression analysis showed mostly non-significant regression coefficient for yield on SDD value for all three maturity groups due to shrinkage property of BLUPs. On the other hand, correlation coefficients between SDD values and observed unadjusted/simulated means for grain yield demonstrated that relationship between the environmental covariate of SDD and grain yield did exist. Nevertheless, in two locations with the highest SDD values in east the regression coefficient was mostly significant.
Climate change had the effect on maize cropping system in Europe allowing earlier planting and/or growing early maturity cultivars/hybrids. These avoiding strategies are commonly applied in maize where stress can be circumvented by earlier planting dates or planting earlier hybrids to avoid assumed adverse weather conditions mostly during flowering. However, the global trends in temperature and precipitation indicate that extreme weather events may occur at any time throughout the growing season. Thus, the negative effect of temperature during the growing season of maize can be mitigated by selecting later hybrid maturities that require a longer thermal time period to complete development [46]. Generally, a full-season hybrid can take more advantage of available heat units and performs better when everything else is balanced. However, recent climate change considerably impacts maize production causing various (a) biotic stresses [48] and multiple interactions among them, both documented or new [49]. Moreover, our study indicates that late hybrids seem to be more prone to adverse climatic conditions in the sites with low soil yield potential.
Nevertheless, investigations in France [50], India [38] and China [51,52] showed that one of the ways to mitigate the effect of heat stress is the detection and selection of the optimal maturity group for each breeding area that can prevent a reduction in maize yield. However, Abendroth et al. [22] stated that factors other than thermal availability are more important in the US Midwest when choosing maize hybrids, such as drying costs, field operability, labor constraints or crop genetics availability. Moreover, it seems that the contribution of crop management to maize phenology, i.e., life cycle is larger than climate change [53]. Ultimately, the increase in maize yield is equally influenced by improved management and the development of new genotypes [54].
The negative effect of the location can be mitigated by applying different agro-technical measures [55,56]. However, in Southeast Europe, there are inadequate (suboptimum) cultural practice and crop management due to economic reasons [5,57,58] resulting in complex and mostly unknown multiple stress growing conditions in field environments. Altogether, investigating simultaneous genotype × environment × management (G × E × M) interactions should be employed [59,60] to overcome traditional crop improvement approach seeking firstly genotypes adapted broadly under a standard management regime, and then manipulation of management regionally in response to average local environmental conditions. Traditional discipline-centered approaches that dealt with separate components of G × E × M, usually as G × E by breeders and E × M by agronomists should be integrated in order to search the full spectrum of G × E × M combinations forming the adaptation landscape.

Supplementary Materials

The following are available online at https://www.mdpi.com/article/10.3390/agronomy12040922/s1, Table S1: Overview of Croatian VCU maize trials grouped in three FAO maturity groups evaluated in the period 2001–2019, Figure S1: Mean values for grain yield from Croatian VCU maize trials grouped in three FAO maturity groups evaluated in the period 2001–2019 in several locations (grey dots) and their average for the particular year (red dots), Figure S2. Best linear unbiased estimators (BLUEs) of the year effect with corresponding standard errors for grain yield from Croatian VCU maize trials grouped in three FAO maturity groups evaluated in the period 2001–2019 in several locations (Table S1).

Author Contributions

Conceptualization, J.G. and D.Š.; methodology, J.G., V.G. and D.Š.; software, V.G.; formal analysis, V.G. and D.Š.; investigation, M.Z. and I.V.; resources, G.J.; data curation, G.J.and I.V.; writing—original draft preparation, M.Z.; writing—review and editing, J.G., V.G. and D.Š.; funding acquisition, G.J. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

1. Publicly available weather datasets were analyzed in this study. This data can be found at https://agri4cast.jrc.ec.europa.eu/DataPortal/Index.aspx (accessed on 4 October 2021), 2. The detailed grain yield data are available upon request from Croatian Agency for Agriculture and Food, Vinkovacka cesta 63c, 31000 Osijek, Croatia; goran.jukic@hapih.hr (G.J.).

Conflicts of Interest

The authors declare no conflict of interest.

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Agronomy 12 00922 g001 550
Figure 1. Dot plots of relative best linear unbiased predictions of random LY effects (Year:Location) (LY_BLUPs) in VCU trials in maize genotypes belonging to three maturity groups (a) FAO 300; (b) FAO 400; and (c) FAO 500.
Figure 1. Dot plots of relative best linear unbiased predictions of random LY effects (Year:Location) (LY_BLUPs) in VCU trials in maize genotypes belonging to three maturity groups (a) FAO 300; (b) FAO 400; and (c) FAO 500.
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Figure 2. Stress Degree Days (SDD) for five locations in the continental part of Croatia in the two-decade period from 1999 to 2019.
Figure 2. Stress Degree Days (SDD) for five locations in the continental part of Croatia in the two-decade period from 1999 to 2019.
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Figure 3. Unadjusted means for grain yield with corresponding standard errors in VCU trials across five locations in maize genotypes belonging to three maturity groups FAO 300, FAO 400, and FAO 500.
Figure 3. Unadjusted means for grain yield with corresponding standard errors in VCU trials across five locations in maize genotypes belonging to three maturity groups FAO 300, FAO 400, and FAO 500.
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Figure 4. The best linear unbiased predictions of random Location effects (L_BLUPs) with corresponding standard errors in VCU trials in maize genotypes belonging to three maturity groups FAO 300, FAO 400, and FAO 500.
Figure 4. The best linear unbiased predictions of random Location effects (L_BLUPs) with corresponding standard errors in VCU trials in maize genotypes belonging to three maturity groups FAO 300, FAO 400, and FAO 500.
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Figure 5. Predicted grain yield according to APSIM simulated data averaged over the period 2001–2019 with corresponding standard errors in maize genotypes belonging to three maturity groups FAO 300, FAO 400, and FAO 500 at three geographically distinct locations in Croatia.
Figure 5. Predicted grain yield according to APSIM simulated data averaged over the period 2001–2019 with corresponding standard errors in maize genotypes belonging to three maturity groups FAO 300, FAO 400, and FAO 500 at three geographically distinct locations in Croatia.
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Figure 6. Correlation coefficients between grain yield estimated by observed unadjusted means, APSIM simulated means, L_BLUPs for maize genotypes of three maturity groups and SDD values across three geographically distinct locations in Croatia in the two-decade period. The dashed vertical line denotes significance level at p < 0.05.
Figure 6. Correlation coefficients between grain yield estimated by observed unadjusted means, APSIM simulated means, L_BLUPs for maize genotypes of three maturity groups and SDD values across three geographically distinct locations in Croatia in the two-decade period. The dashed vertical line denotes significance level at p < 0.05.
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Table
Table 1. Estimated variance components and associated standard errors (s.e.) for yield of maize from official Croatian variety trials in the period 2001–2019 along with intraclass correlation coefficients (ICC), values of Marginal R2/Conditional R2 (MargR2/CondR2) and heritability estimates.
Table 1. Estimated variance components and associated standard errors (s.e.) for yield of maize from official Croatian variety trials in the period 2001–2019 along with intraclass correlation coefficients (ICC), values of Marginal R2/Conditional R2 (MargR2/CondR2) and heritability estimates.
Effect
/Parameter		FAO 300FAO 400FAO 500
Variances.e.Variances.e.Variances.e.
Gi0.4180.0420.4090.0390.3280.041
Lj0.2750.3350.3880.3310.7590.233
LYjk2.2510.1541.7630.1382.3690.133
GLij0.0570.0390.0270.0430.0000.060
GYik0.0910.0320.1460.0280.5390.026
GLYijk0.2110.0260.2890.0190.3630.026
εijk1.0340.0090.8830.0070.8110.009
ICC0.760.770.84
MargR2/CondR20.29/0.830.28/0.840.19/0.87
Heritability0.480.530.40
Table
Table 2. Coefficients of regression of the L_BLUPs on SDD values estimated by a simple linear model. SE, t, and p designate the standard error, the t statistic and p-value of the fitted parameter, respectively. p-values lower than 0.05 are shown in bold.
Table 2. Coefficients of regression of the L_BLUPs on SDD values estimated by a simple linear model. SE, t, and p designate the standard error, the t statistic and p-value of the fitted parameter, respectively. p-values lower than 0.05 are shown in bold.
LocationMaturity groupTermEstimateSEtp
ZgFAO300Intercept10.7030.70315.2180.000
Slope0.0110.0071.5560.138
FAO400Intercept11.3170.56320.1130.000
Slope0.0080.0061.3850.184
FAO500Intercept11.3340.66717.0030.000
Slope0.0080.0071.1620.261
KuFAO300Intercept11.6020.61918.7320.000
Slope−0.0030.006−0.4460.663
FAO400Intercept11.7220.51922.6010.000
Slope0.0010.0050.0970.924
FAO500Intercept12.1720.62619.4530.000
Slope−0.0040.006−0.6870.504
OsFAO300Intercept11.7910.64518.2880.000
Slope−0.0060.005−1.1160.281
FAO400Intercept12.0380.65218.4510.000
Slope−0.0040.005−0.6820.505
FAO500Intercept11.4030.75115.1840.000
Slope0.0030.0060.4340.670
BmFAO300Intercept10.4070.36628.4410.000
Slope0.0110.0043.0390.008
FAO400Intercept11.1570.43325.7650.000
Slope0.0080.0041.8850.079
FAO500Intercept10.9400.40327.1810.000
Slope0.0120.0042.8720.012
VuFAO300Intercept12.3850.64919.0740.000
Slope−0.0100.005−2.0560.056
FAO400Intercept12.9330.54123.9250.000
Slope−0.0110.004−2.5640.020
FAO500Intercept13.4540.58323.0770.000
Slope−0.0150.005−3.3160.004
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Zorić, M.; Gunjača, J.; Galić, V.; Jukić, G.; Varnica, I.; Šimić, D. Best Linear Unbiased Predictions of Environmental Effects on Grain Yield in Maize Variety Trials of Different Maturity Groups. Agronomy 2022, 12, 922. https://doi.org/10.3390/agronomy12040922

AMA Style

Zorić M, Gunjača J, Galić V, Jukić G, Varnica I, Šimić D. Best Linear Unbiased Predictions of Environmental Effects on Grain Yield in Maize Variety Trials of Different Maturity Groups. Agronomy. 2022; 12(4):922. https://doi.org/10.3390/agronomy12040922

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Zorić, Marina, Jerko Gunjača, Vlatko Galić, Goran Jukić, Ivan Varnica, and Domagoj Šimić. 2022. "Best Linear Unbiased Predictions of Environmental Effects on Grain Yield in Maize Variety Trials of Different Maturity Groups" Agronomy 12, no. 4: 922. https://doi.org/10.3390/agronomy12040922

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