Using crop modeling to evaluate the impacts of climate change on wheat in southeastern turkey

Vanlı, Ömer; Ustundag, Burak Berk; Ahmad, Ishfaq; Hernández-Ochoa, Ixchel M.; Hoogenboom, Gerrit

doi:10.1007/s11356-019-06061-6

Cited by 28 publications

(12 citation statements)

References 42 publications

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“…Based on these results, Özdoğan stressed the importance of taking necessary precautions, such as developing more resilient cultivars to alleviate the harmful effects of environmental degradation. In their recent study, Vanli et al (2019), using farmer field-level data, confirmed previous research findings that ecological problems have a detrimental effect on agriculture. Using a paired biophysical and economic analysis, Dellal et al (2011) demonstrated that climate change is expected to decrease yield for five major crops (i.e., wheat, barley, corn, sunflower, and cotton) up to 10.1% in Turkey.…”

Section: Area Productionsupporting

confidence: 74%

Addressing the long- and short-run effects of climate change on major food crops production in Turkey

Chandio

Gökmenoğlu

Ahmad

2021

Environ Sci Pollut Res

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This study assessed the long-run (LR) and short-run (SR) impacts of climatic and non-climatic factors, i.e., CO 2 emissions (CO 2 e), average level of temperature (ALT), average level of precipitation (ALP), area harvested of wheat and rice crops (AHW and (AHR), domestic credit (DCR), and agricultural labor (ALB) on wheat and rice production (WP and RP) in Turkey by using annual time series data ranging from 1980 to 2016 and by employing several econometric techniques. The autoregressive distributed lag-bounds (ARDL) approach and the Johansen and Juselius cointegration (JJC) test confirmed a valid long-term connection among underlying variables. The estimation results from the ARDL model reveal that climatic factors such as CO 2 emissions and temperature adversely affected wheat production in the long run as well as in the short run, whereas precipitation positively improved wheat production in both periods. Further results indicate that non-climatic factors like area harvested of wheat and domestic credit positively and significantly enhanced wheat production in the long run and short run. Similarly, CO 2 emissions also adversely affected rice production in both periods, while temperature and precipitation positively contributed towards rice production in both cases. In addition, area harvested of rice positively and significantly boosted rice production in the long run as well as in the short run, while domestic credit negatively influenced rice production in the long run but in the short run positively improved rice production. Additionally, the outcomes of the VECM Granger Causality for both rice and wheat production confirm that both climatic and non-climatic variables have a strong influence on the production of both crops. This study found that climate change has a deleterious influence on both wheat and rice production; therefore, the study suggests that temperature-resistant varieties of both crops should be developed and introduced by agricultural research institutions. In addition to this, up-to-date information is more needed related to climate change, and in the farming communities, it should be provided by agricultural extension workers.

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Section: Area Productionsupporting

confidence: 74%

Addressing the long- and short-run effects of climate change on major food crops production in Turkey

Chandio

Gökmenoğlu

Ahmad

2021

Environ Sci Pollut Res

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“…There are many papers in which the authors simply tested the model before application (e.g. Guarin et al., 2020; Martinez‐Feria et al., 2018; Baum et al., 2020; Farre, Robertson, Asseng, French, & Dracup, 2004; Vanli, Ustundag, Ahmad, Hernandez‐Ochoa, & Hoogenboom, 2019), while others split data into calibration and validation before application (e.g. Basche et al., 2016; Puntel et al., 2016, 2018).…”

Section: Methodsmentioning

confidence: 99%

Simulating winter rye cover crop production under alternative management in a corn‐soybean rotation

et al. 2020

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The Agricultural Production Systems sIMulator (APSIM) was used to evaluate two alternative approaches for extending the cover crop growing window into corn (Zea mays L.) and soybean (Glycine max L.) crop rotations in Nebraska, USA. We evaluated how: (i) shifting corn planting dates (mid-April to early-June) and (ii) altering comparative relative maturity (CRM) corn hybrids (80 to 115 days) influence cover crop biomass and corn yields over a 30-year period. The APSIM model was tested using experimental data and was then used to simulate a range of cover crop planting and termination scenarios. Our results showed no significant yield differences within the same corn relative maturity when planted on April 20 and May 13 but that yield declined when planted in June. During a six week fall cover crop planting window (September 15-October 31), every day before October 31 that the cover crop was planted resulted in additional 62 kg ha −1 of biomass. We also simulated a one month spring termination window (April 1-April 30) and, every day delay in cover crop termination resulted in per day additional 35 kg ha −1 of biomass. Cover crop biomass accrual was highly dependent on weather, where for identical fall planting dates, a warm wet season accrued approximately four times more biomass than a cool dry season. Although we found significant yield differences between early, medium and late season CRMs, earlier fall cover crop planting associated with either earlier spring corn planting or planting an early to medium season variety leads to tenfold greater cover biomass. Delayed corn planting by mid-May had no yield penalty relative to April planting, and could facilitate four-fold greater cover crop biomass (cover crop terminated April 30 instead of April 1). Our results demonstrate that earlier cover crop planting in fall or later cover crop termination in spring can result in significantly more biomass which can be balanced with yield goals.

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“…The impact of climate change is becoming harsh due to frequent extreme weather events [23,24]. It is imperative to evaluate crops for resilience against harsh climatic conditions by sustaining yields and soil fertility with efficient nutrient management [25][26][27][28][29]. Many researchers highlighted the role of nutrient management (right source of nutrient, right quantity, right placement at the right time) in improving the soil properties [30].…”

Section: Introductionmentioning

confidence: 99%

Optimizing Management Options through Empirical Modeling to Improve Pearl Millet Production for Semi-Arid and Arid Regions of Punjab, Pakistan

et al. 2020

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Low planting density and deficient nitrogen application are factors that significantly decrease the yield of pearl millet in Pakistan. Optimizing their management is imperative in increasing millet production efficiency, especially with N fertilization, which can strongly affect hybrid millet response. Therefore, a field experiment was conducted at the Agronomic Research Area, University of Agriculture, Faisalabad (semi-arid) and the Agronomic Research Station, Karor Lal Eason, District Layyah (arid) over two summer seasons (2015 and 2016). The experiment consisted of three intra-row spacings (10, 15, and 20 cm) as main plots, while four nitrogen rates (0, 150, 200, and 250 kg ha−1) were randomized in subplots. The treatments were triplicated each year at both locations. The results depicted a significant change in millet crop development with a change in planting density and nitrogen rate in semi-arid and arid environments. The decrease in planting density resulted 1–2 day(s) delay in 50% flowering, milking, and maturity in semi-arid and arid region during both years of study. Higher dry matter accumulation was observed at medium planting density (15 cm intra-row spacing) and higher levels of nitrogen (250 kg ha−1) at both locations and growing seasons. The yield and attributed yield performed well with 15-cm plant spacing coupled with N application from 150–200 kg ha−1, and resulted in high nitrogen use efficiency (NUE). The results of the quadratic relationship and economic analysis linked with yield and nitrogen levels at 15-cm spacing showed 176 and 181 kg N ha−1 optimum levels (mean of years) against the economic N levels of 138 and 137 kg N ha−1 for Faisalabad and Layyah, respectively. The benefit–cost ratio (BCR) showed 31% and 45% mean excessive N at 200 and 250 kg N ha−1, in Faisalabad and Layyah, respectively. So, it is concluded that the optimum economic level of N should be sought out according to the soil and climate of an area for the production of hybrid pearl millet on a sustainable basis.

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Using crop modeling to evaluate the impacts of climate change on wheat in southeastern turkey

Cited by 28 publications

References 42 publications

Addressing the long- and short-run effects of climate change on major food crops production in Turkey

Addressing the long- and short-run effects of climate change on major food crops production in Turkey

Simulating winter rye cover crop production under alternative management in a corn‐soybean rotation

Optimizing Management Options through Empirical Modeling to Improve Pearl Millet Production for Semi-Arid and Arid Regions of Punjab, Pakistan

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