| Sažetak | Tijekom posljednjih nekoliko desetljeća globalne klimatske promjene pred oplemenjivače stavljaju nove izazove u pogledu selekcije genotipova adaptabilnih na sušu i toplinski stres. Ovo istraživanje u prvom koraku istražuje upotrebljivost meteoroloških podataka iz javno dostupnih baza Državnog Hidrometeorološkog zavoda (DHMZ) i Agri4Cast (A4C) i iz njih izračunatih indeksa suše (scPDSI i VPD) i toplinskog stresa (SDD) u odnosu na podatke prikupljene izravno u pokusu (POK), a u svrhu njihovog korištenja prilikom tipizacije okoline. U narednom koraku istražena je primjena okolinskih indeksa u procesu detekcije tolerantnih genotipova. Pomoću različitih statističkih modela ANOVA-e te AMMI analize dalje je istraženo variranje agronomskih svojstava kao i složenost interakcije genotip×okolina (G×E) za 32 genotipa kukuruza različitih skupina zriobe u pogledu učinaka prouzročenih sušom i toplinskim stresom. Kao jedan od novih pristupa istražena je i mogućnost interpretacije G×E interakcija putem procesnog modeliranja pomoću APSIM programskog paketa temeljenog na višegodišnjim meteorološkim podacima. Vrijednosti prinosa višegodišnjih simulacija validirani su sa stvarnim podacima iz poljskih pokusa kako bi se utvrdila pouzdanost predikcije. U zadnjem koraku istražena je mogućnost implementacije tipizacije okoline u klasični oplemenjivački postupak, tj. detekcija genotipova kukuruza tolerantnih na sušu i toplinski stres temeljem rezultata stresnih i normalnih okolina. Validacija podataka o prosječnim dnevnim temperaturama i relativnoj vlazi zraka provedena u ovom istraživanju pokazala je vrlo visoku usporedivost bez obzira na izvor (POK, DHMZ i A4C) i vremenski interval (dan, dekada), a time i upotrebljivost za izračun indeksa okolinskih stresova koji zahtijevaju ove meteorološke podatke. Vrijednosti količine oborine pokazale su slabiju međusobnu povezanost, stoga je u izračunu indeksa suše koji zahtijevaju količinu oborine, poput scPDSI, uputno njezino mjerenje izravno na lokaciji provedbe pokusa. Unatoč manjim razlikama, svi indeksi slično su identificirali najviše i najmanje stresne okoline i pokazali se učinkovitim u tipizaciji okolina na stres suše i visokih temperatura zraka. Veće vrijednosti intervala između polinacije i svilanja (ASI), te niže vrijednosti visine biljaka na istoku (Beli Manastir, Kutjevo, Osijek i Tovarnik) za razliku od zapada zemlje (Rugvica i Šašinovec), kao i značajno smanjenje mase zrna na klipu i prinosa sugerira da su istočne lokacije tijekom 2017. godine pretrpjele stres prouzročen sušom i visokim temperaturama, dok to nije bio slučaj za lokacije smještene na zapadu zemlje. Modelima trofaktorske i dvofaktorske ANOVA-e te AMMI analizom utvrđena je značajnost utjecaja svih glavnih izvora na variranje prinosa, pri čemu je okolina imala najveći utjecaj, a znatno manji interakcija G×E i genotip. AMMI2 biplot ukazuje na nestabilnost okolina i značajan interakcijski doprinos genotipova FAO skupine 300. Genotipovi FAO skupine 400 pokazali su se široko adaptabilnima, pri čemu su tri genotipa pokazala značajan doprinos interakcijskim učincima svojom specifičnom adaptabilnošću spram tri različite okoline. Genotipovi FAO skupina 500 i 600 pokazali su široku adaptabilnost prema prinosnijim okolinama. Procesno modeliranje temeljeno na dugoročnim meteorološkim podacima pokazalo se prikladnim za tipizaciju ciljanih okolina i za simulaciju njihovih proizvodnih potencijala, iako nije uočen obrazac razlika prinosa među pojedinim skupinama zriobe. Povezanost analiziranih agronomskih svojstava i jačine stresa bila je najviša kada je okolinski stres procjenjivan VPD i SDD indeksom. Analizom indeksa tolerantnosti temeljenih na redukciji prinosa zrna između kontrastnih okolina u pogledu sušnog stresa, dvije grupe korištenih indeksa različito rangiraju genotipove spram tolerantnosti na sušu i toplinski stres. Prema prvoj grupi indeksa (GMP, STI, MP i HARM) kao tolerantni izdvojeni su genotipovi 29, 30, 31, dok su prema drugoj grupi indeksa (TOL, SSI, RDY) kao tolerantni izdvojeni 3, 11, 6. Ipak, ako se promatraju iznadprosječni genotipovi prema obje grupe indeksa, onda se mogu izdvojiti genotipovi 13, 15, 12, 14 i 21 kao tolerantni prema obje grupe indeksa. |
| Sažetak (engleski) | Introduction: Over the last few decades, we have witnessed global climate change and challenges for breeding programs to overcome those scenarios and detect and select drought and heat tolerant genotypes. Therefore, following the hypotheses and objectives, this study in the first part deals with the environment evaluation (envirotyping) using meteorological data for mean daily temperature, relative humidity, and precipitation collected directly in the field and from publicly available sources. They are used to compute several drought and heat indices with the purpose of estimation of the severity of drought and heath stress over six locations and two consecutive years (12 environments) and their impact on agronomic traits. The environmental stress level was also analyzed using several statistical models of ANOVA, as well as AMMI analysis. In addition to the genotype×environment (G×E interaction), it is important for crop production to investigate the three-factor interaction of G×E×M between genotype (G), environment (E), and management (M). Therefore, this research also analyzes the possibility of investigating the G×E×M interactions of four maturity groups (FAO300, FAO400, FAO500, and FAO600) through a process-based crop model. In its final stage, this study explores the possibility of implementing envirotyping in the classical breeding programs through application of drought tolerance indices based on grain yield reduction between contrast environments, as a significant factor in the process of detecting drought and heat-stress tolerant genotypes.
Materials and methods: Meteorological data in the daily and decade (10-day) timeframe were collected from three different sources. The first set of meteorological data was collected directly from the trial (POK), while the other two equivalent data sets were collected from the alternative data sources, Croatian Meteorological and Hydrological Service (DHMZ) and Agri4Cast (A4C). Meteorological data and drought indices calculated from those three data sets were validated by the Pearson correlation coefficient to assess their association. The similarity of the data derived from these three sources enables their usage in the breeding programs focused on drought and heat stress envirotyping scenarios. Envirotyping was conducted through drought (scPDSI and VPD) and heat stress (SDD) indices, calculated from three parallel data sets. The scPDSI index was calculated according to the methodology of Pandžić et al. (2022) and relies on the water balance model (WB) within the observed period (Palmer, 1965). The vapor pressure deficit (VPD) was calculated according to the methodology of Allen et al. (1998) and follows the Perfect Grower (2019) VPD level recommendations. The third one, stress degree days (SDD) index, was calculated according to the Idso et al. (1981). SDD index summarizes the impact of temperatures above 30°C, and unlike the previous two related to drought, SDD indicates the intensity and occurrence of heat stress. Initially, individual ANOVA was used to determine the variation of agronomic traits observed in this study (anthesis-silk interval - ASI, grain moisture, plant height, thousand kernel weight, kernel weight per ear, and grain yield) in 12 environments for 32 maize genotypes of different maturity groups regarding the effects caused by drought and heat stress. Further, three-factorial and two-factorial ANOVA models were used to demonstrate the significance of different structure of main and interaction effects. Additionally, an AMMI analysis with the help of methane software packages (Olivoto and Dal'Col Lúcio, 2019; R Core Team, 2023) was applied to explore the complexity of the G×E interaction for grain yield (AMMI2; Gauch et al., 2008; Gauch, 2013) in terms of the stress level derived from environmental effects in the form of drought and heat stress scenarios. A process-based crop model was conducted through the APSIM software package. APSIM model was calibrated according to long-term meteorological data values from the A4C database, while the agroecological aspect of the soil was adjusted according to the soil type of each experimental site according to the ISRIC (International Soil Reference and Information Centre) database with the help of apsimx software packages (Miguez, 2024; R Core Team, 2023). In the next step, the grain yield derived from simulations were compared with observed values to assess the similarity of the predicted and observed grain yield values. The validation test was made using Pearson's correlation coefficient and root mean square error (RMSE), a commonly used statistical test in terms of process-based crop models (Palsey et al., 2023). The drought and heat stress tolerance detection for 32 maize genotypes of four FAO maturity groups was conducted using the drought tolerance index concept. This study tested seven indices: GMP, STI, MP, HARM, TOL, SSI, and RDY. They are primarily based on measuring the grain yield reduction between stressed and non-stressed conditions. Regarding their interpretation, the panel of 32 genotypes was distributed from the most tolerant to the most sensitive ones according to the growing average values for GMP, STI, MP, and HARM (Fernandez, 1992; Rosielle and Hamblin, 1981; Kristin et al., 1997), and decreasing average values for TOL, SSI, and RDY (Rosielle and Hamblin, 1981; Fischer and Maurer, 1978; Farshadfar et al., 2013).
Results and conclusions: Data validation tests conducted over average daily temperatures and relative humidity values showed high similarity. The data set collected from alternative (DHMZ and A4C) sources show strong relationships with data gathered directly in the POK. Therefore, the study concludes that both alternative data sources can be used to calculate the environmental stress indices that require such meteorological elements as is the case with VPD and SDD. Precipitation values from alternative sources (DHMA and A4C) had a weaker correlation with real values measured directly in the field conditions, especially at the daily timescale, what can limit the reliability of the calculation of indices such as scPDSI. Therefore, to calculate drought indices that require precipitation amount values, it is advisable to provide measurements directly on the field. In 2017 this study observed higher ASI and lower plant height values, as well as significant reduction of kernel weight per ear and grain yield in the eastern (Beli Manastir, Kutjevo, Osijek, and Tovarnik) in contrast to the western part of the country (Rugvica and Šašinovec). This observation suggests that locations in the east part suffered from the stress caused by drought and high temperatures during 2017, while this was not the case for locations in the western part of the country. The models of three-factorial and two-factorial ANOVA and AMMI analysis determined the statistical significance for all major factors of variations to the grain yield. The environment has had the greatest influence, followed by the interaction of G×E and genotype. The AMMI2 biplot indicates environmental diversity and a significant interaction contribution of genotypes gathered in the FAO300. The FAO400 genotypes brought wider adaptability, but it is worth emphasizing that genotypes 9, 11, and 16 show a significant contribution to the interaction effects through their narrow adaptation to specified environments. FAO500 and FAO600 genotypes have shown wide adaptability to more productive environments. The process-based crop model based on long-term meteorological data is a useful tool for characterizing targeted environments and simulating their production potentials, but there was no clear pattern of yield difference among individual maturity groups. The functional association between the analyzed agronomic traits and the intensity of stress was the highest when the environmental stress was assessed by the VPD and SDD index. Through the analysis of drought tolerance indices based on the reduction of grain yield determined between contrasting environments in terms of drought stress, the two groups of indices rank the genotypes differently in terms of tolerance to drought and heat stress. According to the first group of indices (GMP, STI, MP and HARM) genotypes 29, 30, 31 were detected as tolerant, while according to the second group of indices (TOL, SSI, RDY) 3, 11, 6 were detected as tolerant. However, if observed above-average genotypes according to both index groups, then genotypes 13, 15, 12, 14 and 21 can be distinguished as tolerant according to both index groups. |
