A new regional cotton growth model based on reference crop evapotranspiration for predicting growth processes

Abstract

Meteorological conditions and irrigation amounts are key factors that affect crop growth processes. Typically, crop growth and development are modeled as a function of time or growing degree days (GDD). Although the most important component of GDD is temperature, it can vary significantly year to year while also gradually shifting due to climate changes. However, cotton is highly sensitive to various meteorological factors, and reference crop evapotranspiration (ETO) integrates the primary meteorological factors responsible for global dryland extension and aridity changes. This paper constructs a cotton growth model using ETO, which improves the accuracy of crop growth simulation. Two cotton growth models based on the logistic model established using GDD or ETO as independent factors are evaluated in this paper. Additionally, this paper examines mathematical models that relate irrigation amount and irrigation water utilization efficiency (IWUE) to the maximum leaf area index (LAImax) and cotton yield, revealing some key findings. First, the model using cumulative reference crop evapotranspiration (CETO) as the independent variable is more accurate than the one using cumulative growing degree days. To better reflect the effects of meteorological conditions on cotton growth, this paper recommends using CETO as the independent variable to establish cotton growth models. Secondly, the maximum cotton yield is 7171.7 kg/ha when LAImax is 6.043 cm2/cm2, the corresponding required irrigation amount is 518.793 mm, and IWUE is 21.153 kg/(ha·mm). Future studies should consider multiple associated meteorological factors and use ETO crop growth models to simulate and predict crop growth and yield.

Effects of Combined Application of Organic Fertilizer on the Growth and Yield of Pakchoi under Different Irrigation Water Types

“…The logistic equation is characterized by a typical S-shaped curve composed of piecewise exponential, linear, and convex functions that vary with independent variables, and its eigenvalues are primarily used to describe the linear accumulation period [58,59]. However, its simulation accuracy may be reduced, particularly during periods of fast growth.…”

Prediction Models of Growth Characteristics and Yield for Chinese Winter Wheat Based on Machine Learning

“…The statistical approaches include multiple linear regression models [4,5], factor analysis linear regression methods [6] and gray prediction models. They are used to obtain the simple functional relationships between the yields and the influencing factors of water and fertilizer [7][8][9]. But these methods usually ignore the environmental and meteorological factors and are thus hard to use widely.…”

Effects of sowing dates and phosphorus levels on cotton growth and yield: soil analysis and implications

“…Smallholder farmers predominantly cultivate cotton in these regions, with approximately 99% of the world's cotton farmers residing there. Asian countries (i.e., India, China, Pakistan, Uzbekistan, Turkmenistan, and Tajikistan) account for about 66% of cotton farmers, while West Africa, Egypt, South America (particularly Brazil), and other regions house the remaining 33% (EJF, 2007;Lin et al, 2023). These farmers typically work in rural settings and cultivate cotton Tlatlaa et al 10.3389/fsufs.2023.1298459 Frontiers in Sustainable Food Systems 02 frontiersin.org on small plots, often less than 0.5 hectares, as a means of supplementing their income (EJF, 2007;Riello, 2022).…”

“…It affects root penetration, vegetative growth, nutrient uptake, and solar energy utilization (Khan et al, 2017). However, selecting the optimal sowing time can be complex due to environmental variables such as air and soil temperatures and solar radiation (Iqbal et al, 2020;Tuttolomondo et al, 2020;Lin et al, 2023). Various crop husbandry challenges, such as improper plant population, water scarcity, inadequate seed rates, fertilizer mismanagement, weed infestations, insect pests, and diseases, contribute to low cotton yields (Lin et al, 2023).…”

“…However, selecting the optimal sowing time can be complex due to environmental variables such as air and soil temperatures and solar radiation (Iqbal et al, 2020;Tuttolomondo et al, 2020;Lin et al, 2023). Various crop husbandry challenges, such as improper plant population, water scarcity, inadequate seed rates, fertilizer mismanagement, weed infestations, insect pests, and diseases, contribute to low cotton yields (Lin et al, 2023). Phosphorus deficiency is particularly detrimental, reducing leaf expansion and photosynthesis rates.…”