Water and heat resource utilization of cotton under different cropping patterns and their effects on crop biomass and yield formation

Wu, Fengqi; Qiu, Yurong; Huang, Weibin; Guo, Simeng; Han, Yingchun; Wang, Guoping; Li, Xiaofei; Lei, Yaping; Yang, Beifang; Xiong, Shiwu; Xin, Minghua; Chen, Jie; Wang, Zhanbiao; Feng, Lu; Li, Yabing

doi:10.1016/j.agrformet.2022.109091

Cited by 15 publications

(5 citation statements)

References 46 publications

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“…Crop biomass accumulation and plant height are important indexes to measure the cotton’s growth status. They generally respond to the soil water deficiency first and then are exhibited in the crop yield [ 44 , 45 ]. Liu et al [ 46 ] found that the crop’s vegetative growth was limited to a certain extent by deficit irrigation, which was manifested in the decline of plant height and a reduction of biological yield.…”

Section: Discussionmentioning

confidence: 99%

Soil Moisture Regulation under Mulched Drip Irrigation Influences the Soil Salt Distribution and Growth of Cotton in Southern Xinjiang, China

et al. 2023

Plants

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Water deficiency, together with soil salinization, has been seriously restricting sustainable agriculture around the globe for a long time. Optimal soil moisture regulation contributes to the amelioration of soil water and salinity for crops, which is favorable for plant production. A field experiment with five soil water lower limit levels (T1: 85% FC, T2: 75% FC, T3: 65% FC, T4: 55% FC, and T5: 45% FC, where FC is the field capacity) was conducted in southern Xinjiang in 2018 to investigate the responses of soil water–salt dynamics and cotton performance to soil moisture regulation strategies. The results indicated that in the horizontal direction, the farther away the drip irrigation belt, the lower the soil moisture content and the greater the soil salinity. In the vertical direction, the soil moisture and soil salinity increased first and then decreased with an increase in soil depth after irrigation, and the distribution was similar to an ellipse. Moreover, the humid perimeter of soil water and the leaching range of soil salt increased with a decrease in the soil moisture lower limit. Though more soil salt was leached out for the T5 treatment at the flowering stage due to the higher single irrigation amount, soil salinity increased again at the boll setting stage owing to the long irrigation interval. After the cotton was harvested, soil salt accumulated in the 0–100 cm layer and the accumulation amount followed T3 > T5 > T1 > T2 > T4. Moreover, with a decline of soil moisture lower limit, both plant height and nitrogen uptake decreased significantly while the shoot–root ratio increased. Compared with the yield (7233.2 kg·hm−2) and water use efficiency (WUE, 1.27 kg·m−3) of the T1 treatment, the yield for the T2 treatment only decreased by 1.21%, while the WUE increased by 10.24%. Synthetically, considering the cotton yield, water–nitrogen use efficiency, and soil salt accumulation, the soil moisture lower limit of 75% FC is recommended for cotton cultivation in southern Xinjiang, China.

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Section: Discussionmentioning

confidence: 99%

Soil Moisture Regulation under Mulched Drip Irrigation Influences the Soil Salt Distribution and Growth of Cotton in Southern Xinjiang, China

et al. 2023

Plants

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“…Cotton (Gossypium hirsutum L.) plays a vital role in the national economy as it serves as an indispensable primary raw material for the textile sector (Huang et al, 2022). China boasts one of the highest cotton outputs in the world and is also the largest consumer, with significant demand for this commodity (Wu et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

“…Cotton ( Gossypium hirsutum L.) plays a vital role in the national economy as it serves as an indispensable primary raw material for the textile sector (Huang et al., 2022). China boasts one of the highest cotton outputs in the world and is also the largest consumer, with significant demand for this commodity (Wu et al., 2022). With global warming, the frequency of heat waves in China's major cotton‐producing regions is increasing, posing a significant natural disaster that impacts cotton production (Xu et al., 2019).…”

Section: Introductionmentioning

confidence: 99%

Comprehensive evaluation and screening identification indexes of heat‐resistance indices in cotton (Gossypium hirsutum L.)

Yang,

Liu,

Duan

et al. 2024

J Agronomy Crop Science

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Cotton (Gossypium hirsutum L.) cultivars exhibit varying responses to heat stress. To investigate the heat resistance of various cotton and establish an index system for evaluating their heat resistance, 21 cotton cultivars were selected and subjected to two temperature regimes (CK, average temperature 28°C, 32/24°C; HT, average temperature 38°C, 42/34°C). The results showed that under high temperatures, different changes occurred in individual indexes of cotton, reflecting the differences in heat resistance in cotton cultivars. A total of 21 cotton cultivars could be classified into four types: heat‐tolerant, moderate heat‐tolerant, moderate heat‐sensitive and heat‐sensitive cultivars by multivariate statistical analysis. Moreover, the indexes of net photosynthetic rate (Pn), leaf superoxide dismutase activity (LSOD), the maximum photochemical quantum yield (Fv/Fm), the actual photochemical quantum yield (ΦPSII), leaf malondialdehyde content (LMDA), leaf catalase activity (LCAT), dry matter weight of shoot (SDW) and root malondialdehyde content (RMDA) were determined to be useful for evaluating the cotton heat tolerance by stepwise regression analysis. The pot experiment showed that the reduction of boll number, boll weight and seed cotton yield was more remarkable under HT in the heat‐sensitive cultivar CCRI‐92 than in the heat‐resistant cultivar CCRI‐69, which further verified the screening results. In conclusion, the heat‐sensitive cultivars CCRI‐92 and heat‐resistant cultivar CCRI‐69 which are identified by seedling experiment could serve as ideal experimental materials for studying heat resistance in cotton. The physiological indices such as Pn, LSOD, Fv/Fm, ΦPSII, LMDA, LCAT, SDW and RMDA be employed for assessing the heat tolerance in cotton.

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“…Crop is sun-loving, temperature-loving, drought-resistant and have greater growth potential under abundant resource conditions (Lili Mao et al 2019), as depicted by strong correlation between yield and meteorological factors. In addition to yield, biomass formation is also closely related to the accumulation of resources such as photosynthetically active radiation (PAR), effective temperature accumulation, and soil water content (SWC) during the crop growth period (Wu et al 2022). In addition to variability in crop microclimate, variability in soil temperature, water and humidity impart the variable impact on root growth, which in turn affect below ground root growth, stem and leaf growth, and the reproductive organ development in cotton (Li-Song Tang et al 2010).…”

Section: Introductionmentioning

confidence: 99%

Comparative analysis of machine learning and time series models for cotton yield prediction in major growing districts of Karnataka

HUGGI

et al. 2024

Preprint

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Background Cotton is one of the most important commercial crop after food crops, especially in countries like India, where it’s grown extensively under rainfed condition. Because of its usage in multiple industries, such as textile, medicine and automobile industries, it has greater commercial importance. Cotton cultivation demands intensive management due to its explorative nature. The crop's performance is greatly influenced by prevailing weather dynamics. As climate change awareness grows, assessing how weather changes affect crop performance is essential. Crop models are a prominent tool for this purpose. Similarly, many techniques are vague and crop models are the dominant ones. Results Present study on statistical and machine learning models were compared to assess their ability to predict cotton yield across major producing districts based on long term (1990–2023) dataset on yield and weather factors. The results revealed superior performance of machine learning models such as Artificial Neural Networks (ANN) as they are iteratively trained and evaluated for higher accuracy and found that errors were within acceptable limit i.e., ± 10% and the actual and forecasted yields were in excellent agreement at both F1 and F2 stage and statistically evaluated for RMSE, nRMSE and EF, it showed good results having nRMSE value less than 10 per cent and considered as excellent for eight out of ten districts at F1 and seven districts at F2 stage because of ability of machine learning models such as ANNs to consider intricate interactive effects of weather factors. Furthermore, the tested ANN model was used to assess the importance of the dominant weather factor influencing evaluate crop performance in each district. Specifically, the use of morning relative humidity as an individual parameter and its interaction with maximum and minimum temperature had major influence on cotton yield in most of the yield predicted districts. These difference highlighted the differential interactions of weather factors in each district, which in turn affected the crop productivity. Conclusions Outcomes of the study aid in understanding the weather-related yield variability and planning crop management practices and in achieving yield sustainability under changing climatic scenarios of rainfed condition in India.

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Water and heat resource utilization of cotton under different cropping patterns and their effects on crop biomass and yield formation

Cited by 15 publications

References 46 publications

Soil Moisture Regulation under Mulched Drip Irrigation Influences the Soil Salt Distribution and Growth of Cotton in Southern Xinjiang, China

Soil Moisture Regulation under Mulched Drip Irrigation Influences the Soil Salt Distribution and Growth of Cotton in Southern Xinjiang, China

Comprehensive evaluation and screening identification indexes of heat‐resistance indices in cotton (Gossypium hirsutum L.)

Comparative analysis of machine learning and time series models for cotton yield prediction in major growing districts of Karnataka

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