Characterizing spatial and temporal trends in drought patterns of rainfed wheat (Triticum aestivum L.) across various climatic conditions: A modelling approach

Highlights

• Water stress was dominant around flowering of rainfed wheat (56.8 %).

• Half of the rainfed wheat agro-ecosystems are facing with 50 % of severe drought patterns.

• Severe drought stress reduced grain yield by1.9 t ha⁻¹.

• An extra early-maturity genotype reduced severe drought patterns by 44 %.

• Applying a late-maturity genotype is suggested for regions with high rainfall.

Abstract

Drought is considered one of the most common and challenging environmental stresses in the agricultural production worldwide. For improving the rainfed crop yields in the drought-prone environments, understanding the seasonal patterns of drought events in the target environments is crucial. This study applies the APSIM (Agricultural Production Systems sIMulator) crop model to interpret drought stress impact on a check genotype (early-maturity genotype) and mid- and late-maturity genotypes in 69 sites and 16 climate classes with climate data from over 37 years representing the rainfed wheat agro-ecosystems in Iran. Four drought patterns were recognized across the 16 regions. Water stress was dominant during grain filling (41 %) and around flowering (56 %) in studying regions. This stress varied over different genotypes and climate classes. Furthermore, results showed that under current management practices and using the check genotype, about half of the regions in the rainfed wheat agro-ecosystems of Iran (∼1.7 million ha) are frequently subject to severe drought patterns (50 %) resulting in a loss of grain yield of ∼2 t ha⁻¹. Replacing a common genotype with an extra early-maturity genotype, however, reduced by 44 % the occurrence of severe drought patterns which simultaneously increased the occurrence of light-moderate drought stress (i.e. DP1 and DP2) in the warm regions. In general, planting the extra early-maturity genotype had the advantage of producing higher yields in the driest regions. Sowing this genotype reduced the variability in grain yield and the frequency of severe drought patterns in the warm regions. On the contrary, in rainy and cold regions, the best-yielding genotypes were those with a longer growth period. Therefore, in rainy and cold regions with light-moderate stress conditions, the breeders should focus on early-maturity and mid-maturity genotypes. Besides, it is suggested that the late-maturity genotype should be used in rainy regions with a reduced risk of drought stress.

Introduction

Drought was recognized as one of the most common and challenging environmental stresses in the agricultural sectors worldwide (Hu et al., 2020; Lamaoui et al., 2018; Watson et al., 2017). Drought stress can result in substantially lower crop production -21 % in wheat and 40 % in maize (Daryanto et al., 2016), and globally 9–10 % in cereals (Lesk et al., 2016). By an annual production of ∼12 million tons, Iran is the second largest producer of wheat in the Middle East (FAO, 2017). Wheat is cultivated on 5.5 million ha, of which 62 % is rainfed and the rest irrigated. The average yield of rainfed wheat is 1.03 t ha-1 and that of irrigated wheat is 4.3 t ha-1 for the period of 2010–2016 (Anonymous, 2017). The huge difference between irrigated and rainfed systems due to the grain yield could be related to the lack of the knowledge of drought patterns (DP) that occurs across the rainfed agro-ecosystems in these regions. The drought pattern is not only related to the rainfall trends, but also to the rainfall concentration and monthly rainfall heterogeneity (Thomas and Prasannakumar, 2016).

Drought stress reduces the crop yield by reducing the stomatal conductivity, growth and grain development (Fahad et al., 2017; Farooq et al., 2014). Both severity and duration of drought stress also define the extent of yield loss by reducing the duration of growth and grain filling (Mohammadi-Ahmadmahmoudi et al., 2020; Fahad et al., 2017) and decreasing the yield components and seed set (Alqudah et al., 2011). To deal with drought stress, there are some strategies including drought escape or avoidance by which plants can alleviate the negative effects of drought stress by completing their life-cycle during the short period of favorable conditions (Shavrukov et al., 2017). However, using top-mentioned strategies requires a deep understanding of drought stress nature including the severity and duration of drought due to the spatial and temporal dimensions. Understanding the seasonal pattern of drought events in the target environments has focused by many researchers looking to improve the rainfed crop yields in drought-prone environments (Touzy et al., 2019; Chenu et al., 2013; Kholová et al., 2013; Heinemann et al., 2008). In one simulation study, for example, Chenu et al. (2013) identified four DPs (drought patterns) as the environment types for rainfed wheat across the Australian Wheatbelt, where severe drought patterns or DP4 (i.e. drought starts during the vegetative period and generally lasts until maturity) occurred 44 % of time. In another simulation study, Heinemann et al. (2019) analyzed the changes in drought effects on the upland rice cropping systems for cultivars representing three decades of breeding (1980s, 1990s, and 2000s) in the Brazilian savannas using ORYZA v3 rice crop model and observed a mean increase of 12 % in the drought effect (0.35 % per year) from the 1980s to the 2000s.

Characterizing the spatial and temporal trends in the DPs for any particular environment is essential for the breeding programs can deal with drought. It helps the researchers and breeders introduce the genotypes better adapted to each environment (e.g. Heinemann et al., 2019; Touzy et al., 2019; Chenu et al., 2013; Chapman et al., 2000). Some genotypes are well adapted to severe drought patterns while others are suitable for moderate to light drought patterns (i.e. no drought stress or drought stress occurring only at post-flowering) (Chenu et al., 2013). Moreover, depending on drought pattern type, the best combination of G × E (genotype × environment) could be chosen. For this purpose, modelling approaches were pragmatically applied to assess G × E combinations and to take advantage of the genetic and environmental resources more efficiently regarding drought patterns in each environment (Casadebaig et al., 2016). Hammer et al. (2014), for instance, indicated that the multi-year drought stress risk in sorghum for a given region could be decreased by the adoption of better G × M ("local G" and "local M") compared to using the combination of "global G" and "global M". Touzy et al. (2019) used modelling and statistical approaches to identify the specific drought tolerant QTLs in the breeding wheat and reported that the environmental clustering improved understanding of drought impact on wheat grain yields which explained 20 % of G × E interaction. Moreover, they claimed that their results enable the breeders to introduce drought-resistant genotypes to specific environmental conditions. In another modelling study, Lake et al. (2016) used the APSIM crop model to simulate the effect of 3905 E (71 locations × 55 years from 1958 to 2013) in order to assess the patterns of water stress and temperature for Australian chickpea production. The researchers identified four DPs accounting for 87 % of total variation. Hence, they concluded that the significant spatial variation in the drought patterns was highly associated to the efforts conducted in breeding programs for decades.

To date, neither short-term nor long-term country-wide assessments were conducted to characterize the spatial and temporal trends of drought patterns in rainfed wheat agro-ecosystems across the different climatic conditions in Iran. Moreover, an assessment was needed to quantify long-term seasonal drought patterns and their relationship with grain yield using a modelling approach. Considering these contexts, this study aimed (1) to cluster seasonal drought patterns across various rainfed wheat agro-ecosystems; (2) to analyze the frequency of drought patterns for each climate zone and selecting the representative genotypes of wheat differentiated by earliness; and (3) to identify the optimal wheat genotypes well adapted to each climatic region based on the drought patterns.