Rainfed wheat (Triticum aestivum L.) yield prediction using economical, meteorological, and drought indicators through pooled panel data and statistical downscaling

Salehnia, Narges; Salehnia, Narges; Torshizi, Ahmad Saradari; Kolsoumi, Sohrab

doi:10.1016/j.ecolind.2019.105991

Cited by 31 publications

(9 citation statements)

References 47 publications

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“…A future study could further improve DSI by effectively considering this time lag in order to more reliably evaluate and monitor drought. Second, the dryness/wetness condition is normally considered as the dominant factor that impacts crop yields in semi-arid areas, but other factors, such as fertilizer, climatic anomalies, or economics also play roles in the agricultural production (Chen et al, 2015;Salehnia et al, 2020). The use of a single factor only permits the assessment of its impact on yield, rather than yield prediction.…”

Section: Limitation and Outlookmentioning

confidence: 99%

Assessment of drought and its impact on winter wheat yield in the Chinese Loess Plateau

Wang

Fu²,

Liang³

et al. 2022

J. Arid Land

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Drought has pronounced and immediate impacts on agricultural production, especially in semi-arid and arid rainfed agricultural regions. Quantification of drought and its impact on crop yield is essential to agricultural water resource management and food security. We investigated drought and its impact on winter wheat (Triticum aestivum L.) yield in the Chinese Loess Plateau from 2001 to 2015. Specifically, we performed a varimax rotated principal component analysis on drought severity index (DSI) separately for four winter wheat growth periods: pre-sowing growth period (PG), early growth period (EG), middle growth period (MG), and late growth period (LG), resulting in three major subregional DSI dynamics for each growth period. The county-level projections of these major dynamics were then used to evaluate the growth period-specific impacts of DSI on winter wheat yields by using multiple linear regression analysis. Our results showed that the growth period-specific subregions had different major DSI dynamics. During PG, the northwestern area exhibited a rapid wetting trend, while small areas in the south showed a slight drying trend. The remaining subregions fluctuated between dryness and wetness. During EG, the northeastern and western areas exhibited a mild wetting trend. The remaining subregions did not display clear wetting or drying trends. During MG, the eastern and southwestern areas showed slight drying and wetting trends, respectively. The subregions scattered in the north and south had a significant wetting trend. During LG, large areas in the east and west exhibited wetting trends, whereas small parts in south-central area had a slight drying trend. Most counties in the north showed significant and slight wetting trends during PG, EG, and LG, whereas a few southwestern counties exhibited significant drying trends during PG and MG. Our analysis identified close and positive relationships between yields and DSI during LG, and revealed that almost all of the counties were vulnerable to drought. Similar but less strong relationships existed for MG, in which northeastern and eastern counties were more drought-vulnerable than other counties. In contrast, a few drought-sensitive counties were mainly located in the southwestern and eastern areas during PG, and in the northeastern corner of the study region during EG. Overall, our study dissociated growth period-specific and spatial location-specific impacts of drought on winter wheat yield, and might contribute to a better understanding of monitoring and early warning of yield loss.

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Section: Limitation and Outlookmentioning

confidence: 99%

Assessment of drought and its impact on winter wheat yield in the Chinese Loess Plateau

Wang

Fu²,

Liang³

et al. 2022

J. Arid Land

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“…While, cotton production has decreased because of various factors the most significant of which being environmental concern (Ahmad et al, 2016). Thus, Salehnia et al (2020); Ahmad et al (2020); Arshad et al (2021) who indicated that drought is one of the most serious climatic challenges that may harm agricultural productivity. Most of field crops, particularly cotton, need a large amount of water and additional irrigation for optimal cultivation and growth in dry climates.…”

Section: Introductionmentioning

confidence: 99%

Straw Mulching and Plant Spacing Enhance Yield and Some Quality Traits of Cotton (Gossypium hirsutum L.) Varieties

Ezaddin

Salih

2023

Preprint

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Background Intensive research globally requested on climate change and increase the risk of drought. Organic mulch applications may be right way of agricultural management strategy for right now. This experiment conducted by using wheat straw mulch (M0 as control and M1= 4.2 t. ha-1), two plant spacing of (S1= 25cm and S2= 35cm) with three varieties Lachata, Coker 310 and Stoneville 213, that symbolled by (V1, V2 and V3), which were for determining its impact on yield, yield components and some quality parameters in two locations of Erbil Governorate. Results Thereby, the results showed that maximum number of bolls. plant-1, lint index and ginning out turn were by Lachata variety. As well as, maximum number of seeds. boll-1 and earliness index were (27.06 and 82.36 %) by Coker 310. Mulching increased number of bolls. plant-1, boll weight, number of seeds. boll-1, and seed cotton yield as compared to no mulch treatment at both locations. There more, the interaction of factors in which V1S1M1 and V2S2M0 recorded the highest and lowest number of bolls. plant-1 which were (37.3 and 17.1), and seed cotton yield were (5900.0 and 2222.2 kg. ha-1), respectively at Grdarasha location. However, in fiber quality parameters such as longer staple length and single fiber length noted in mulched plots as compared to no mulch. Conclusion Therefore, the use of wheat straw mulch has an impact on cotton yield and quality.

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“…There are some commonly used meteorological drought indices, such as Percentage of precipitation anomaly [13], Palmer drought severity index [14], Standardized precipitation index [15], Compound index, Relative humidity index, Reconnaissance drought index [16], Standardized precipitation evapotranspiration index (SPEI) [17], etc. Among them, SPEI not only has multi-timescale characteristics of a standardized precipitation index but also reflects the effects of global temperature change on drought, which is not only a good index for monitoring meteorological drought [18] but has also been widely used to monitor agricultural drought and analyze the impacts of crops due to drought [10,19,20]. For example, Pena-Gallardo et al [21] found that there were significant correlations between wheat yields and the SPEI at timescales ranging from 1 to 18, particularly over the second half of the year in the counties of Eastern United States.…”

Section: Introductionmentioning

confidence: 99%

Better Drought Index between SPEI and SMDI and the Key Parameters in Denoting Drought Impacts on Spring Wheat Yields in Qinghai, China

Hou¹,

Yao²,

Li³

et al. 2022

Agronomy

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Drought has great negative impacts on crop growth and production. In order to select appropriate drought indices to quantify drought influences on crops to minimize the risk of drought-related crops as much as possible, climate and spring wheat yield-related data from eight sites in the Qinghai Province of China were collected for selecting better drought index between standardized precipitation evapotranspiration index (SPEI, denoting meteorological drought) and soil moisture deficit index (SMDI, denoting agricultural drought) as well as the key parameters (timescale and month) in denoting drought impacts on spring wheat yields. The spring wheat yields during 1961–2018 were simulated by the DSSAT–CERES–Wheat model. Pearson correlations were used to investigate the relationship between SPEI and SMDI and between spring wheat yields and drought indices at different timescales. The results showed that: (1) SMDI reflected more consistent dry/wet conditions than SPEI when the timescales changed and (2) There were one- and two-month lags in SMDI compared to SPEI (with the higher correlation coefficients values of 0.35–0.68) during May to August and (3) May (the jointing period of spring wheat) and the two-month timescale of SMDI0–10 (with the higher correlation coefficients values of 0.21–0.37) were key parameters denoting drought influences on spring wheat yield and (4) The correlations between the linear slopes of spring wheat yield reduction rate and linear slopes of SMDI0–10 in May at the studied eight sites were considerable between 1961–2018 (r = 0.85). This study provides helpful references for mitigating the drought risk of spring wheat.

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Rainfed wheat (Triticum aestivum L.) yield prediction using economical, meteorological, and drought indicators through pooled panel data and statistical downscaling

Cited by 31 publications

References 47 publications

Assessment of drought and its impact on winter wheat yield in the Chinese Loess Plateau

Assessment of drought and its impact on winter wheat yield in the Chinese Loess Plateau

Straw Mulching and Plant Spacing Enhance Yield and Some Quality Traits of Cotton (Gossypium hirsutum L.) Varieties

Better Drought Index between SPEI and SMDI and the Key Parameters in Denoting Drought Impacts on Spring Wheat Yields in Qinghai, China

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