Building Climate-Resilient Cotton Production System for Changing Climate Scenarios Using the DSSAT Model

Awan, Zoia Arshad; Khaliq, Tasneem; Akhtar, Muhammad Masood; Imran, Asad; Irfan, Muhammad; Ahmed, Mushtaq; Ahmad, Ashfaq

doi:10.3390/su131910495

Cited by 12 publications

(4 citation statements)

References 35 publications

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“…The water footprint can be recognized as a comprehensive indicator to evaluate the sustainable utilization of agricultural water under climate change, which reflects water volumes, water sources, and the amount of water required to eliminate agricultural water pollution [13][14][15][16]. Assessing the impact of climate change on the yield and water footprint of winter wheat could improve our understanding of the vulnerability of agricultural water systems to climate change, and also provide advice with respect to protecting water and food security in case of future climate change [17,18].…”

Section: Introductionmentioning

confidence: 99%

Impact of Climate Change on the Yield and Water Footprint of Winter Wheat in the Haihe River Basin, China

et al. 2022

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Climate change can impact the yield and water footprint of crops. Therefore, assessing such impacts carries great significance for regional water and food security. This study validated and verified the variety parameters of winter wheat for the Decision Support System for Agrotechnology Transfer (DSSAT) model, using the long-term (1993–2013) growth and yield data observed from six agricultural experiment stations in the Haihe River Basin (HRB), China. The growth process was simulated under three representative concentration pathways (RCPs), named RCP2.6, RCP4.5, and RCP8.5—climate scenarios driven by the HadGEM2-ES model. The variety parameters of winter wheat showed high accuracy in the simulation of the anthesis and maturity dates, and could be used for long-term prediction of the growth process. The trends of climate change had positive impacts on the water footprint of winter wheat but adverse impacts on the yield. The growing period was shortened by 3.6 days, 4.7 days, and 5.0 days per decade in the RCP2.6, RCP4.5, and RCP8.5 scenarios, respectively, due to the rapid accumulation of heat. The yield would be increased in lower emissions scenarios (17% in RCP2.6), but decreased in high-emissions scenarios due to high temperatures, which may restrict the growth of wheat. The water footprint was decreased by 10%, 11%, and 13% in the RCP2.6, RCP4.5, and RCP8.5 scenarios, respectively, indicating that the water-use efficiency could be improved in the future. The results showed broad application prospects of the DSSAT model in simulating the response of crop growth to climate change.

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Section: Introductionmentioning

confidence: 99%

Impact of Climate Change on the Yield and Water Footprint of Winter Wheat in the Haihe River Basin, China

et al. 2022

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“…Physicsbased methods employ a combination of soil moisture movement and crop water demand equations, integrated with meteorological, soil, and crop parameters, to simulate the dynamic fluctuations of soil moisture. Exemplified by sophisticated models such as HY-DRUS [7], SWAT (Soil and Water Assessment Tool) [8], DSSAT (Decision-support System for Agrotechnology Transfer) [9], and EPIC (Erosion Productivity Impact Calculator) [10], these methods adeptly mirror the physical underpinnings of soil moisture dynamics. However, they are hampered by their requirement for extensive parameter inputs, computational intricacy, and the need for highly precise parameter values, rendering them less viable for broad-scale and long-duration predictive applications [11].…”

Section: Introductionmentioning

confidence: 99%

GRU–Transformer: A Novel Hybrid Model for Predicting Soil Moisture Content in Root Zones

Zheng,

Gao

et al. 2024

Agronomy

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The accurate measurement of soil moisture content emerges as a critical parameter within the ambit of agricultural irrigation management, wherein the precise prediction of this variable plays an instrumental role in enhancing the efficiency and conservation of agricultural water resources. This study introduces an innovative, cutting-edge hybrid model that ingeniously integrates Gated Recirculation Unit (GRU) and Transformer technologies, meticulously crafted to amplify the precision and reliability of soil moisture content forecasts. Leveraging meteorological and soil moisture datasets amassed from eight monitoring stations in Hebei Province, China, over the period from 2011 to 2018, this investigation thoroughly assesses the model’s efficacy against a diverse array of input variables and forecast durations. This assessment is concurrently contrasted with a range of conventional machine learning and deep learning frameworks. The results demonstrate that (1) the GRU–Transformer model exhibits remarkable superiority across various aspects, particularly in short-term projections (1- to 2-day latency). The model’s mean square error (MSE) for a 1-day forecast is notably low at 5.22%, reducing further to a significant 2.71%, while the mean coefficient of determination (R2) reaches a high of 89.92%. Despite a gradual increase in predictive error over extended forecast periods, the model consistently maintains robust performance. Moreover, the model shows exceptional versatility in managing different soil depths, notably excelling in predicting moisture levels at greater depths, thereby surpassing its performance in shallower soils. (2) The model’s predictive error inversely correlates with the reduction in parameters. Remarkably, with a streamlined set of just six soil moisture content parameters, the model predicts an average MSE of 0.59% and an R2 of 98.86% for a three-day forecast, highlighting its resilience to varied parameter configurations. (3) In juxtaposition with prevalent models such as Support Vector Regression (SVR), K-Nearest Neighbors (KNN), Gradient Boosting Decision Tree (GBDT), XGBoost, Random Forest, and deep learning models like Deep Neural Network (DNN), Convolutional Neural Network (CNN), and standalone GRU-branch and Transformer-branch models, the GRU–Transformer framework demonstrates a significant advantage in predicting soil moisture content with enhanced precision for a five-day forecast. This underscores its exceptional capacity to navigate the intricacies of soil moisture data. This research not only provides a potent decision-support tool for agricultural irrigation planning but also makes a substantial contribution to the field of water resource conservation and optimization in agriculture, while concurrently imparting novel insights into the application of deep learning techniques in the spheres of agricultural and environmental sciences.

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“…Because cotton is a subtropical plant, it is well adapted to survive with dry and hot environment [3]. Despite this, cotton nevertheless reacts to an environmental change such as temperature and rainfall in instance.…”

Section: Introductionmentioning

confidence: 99%

Cotton Breeding in the View of Abiotic and Biotic Stresses: Challenges and Perspectives

Kamburova¹,

Salakhutdinov²,

Abdurakhmonov³

2022

Cotton

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Global climate change manifested in average annual temperature rise and imbalance of most natural factors, such as changes in annual mean rainfall, air humidity, average temperature of cold and warm months, soil quality, etc., lead to climatic zones displacement. All these have a significant impact on agricultural production in total, including cotton growing. Cotton is one of the most important technical crops in the world. However, it is very sensitive to environmental changes. The influence of abiotic stresses (high temperature, changes in the mean rainfall and soil salinity) causes a dramatic decrease yield of this crop. Moreover, temperature anomalies and climatic zones displacement cause a change in the area of pathogens and pests distribution, which also reduces the cotton yield. One of the possible ways to increase the cotton yield under the influence of abiotic and biotic stresses is the development of new resistant varieties, using both classical breeding methods and genetic engineering achievements.

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Building Climate-Resilient Cotton Production System for Changing Climate Scenarios Using the DSSAT Model

Cited by 12 publications

References 35 publications

Impact of Climate Change on the Yield and Water Footprint of Winter Wheat in the Haihe River Basin, China

Impact of Climate Change on the Yield and Water Footprint of Winter Wheat in the Haihe River Basin, China

GRU–Transformer: A Novel Hybrid Model for Predicting Soil Moisture Content in Root Zones

Cotton Breeding in the View of Abiotic and Biotic Stresses: Challenges and Perspectives

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