Modern agriculture requires more accurate field management capability compared with traditional agriculture. The development of hyperspectral remote sensing technology embodies rapid and non-destructive features in agricultural information monitoring, providing a technical guarantee for the scientific management of agricultural production. The mathematical model of inverse cotton leaf total nitrogen was established by decomposing and transforming the original cotton leaf spectrum using continuous wavelet analysis and traditional spectral transformation, taking the characteristic wavelet coefficients and spectral characteristic bands as independent variables, and using univariate, stepwise regression, and partial least squares methods. The correlation between the total nitrogen content of cotton leaves and the spectral reflectance, through different methods of spectral treatment, was improved to different degrees. For the conventional spectral transformation, the inverse logarithmic first-order differential lg′(1/R) improved the correlation of cotton leaf total nitrogen by 0.26. The continuous wavelet analysis outperformed the conventional spectral model regarding information noise reduction and mining of feature information. The established model with RPD>2 had good stability and predictive power for all sample data.

To study the feasibility of CROPGRO_cotton model in the optimization of cotton irrigation management under different climatic conditions, the empirical values corresponding to 25%, 50% and 75% of the empirical frequencies of precipitation were obtained by ranking the frequency fitness line of precipitation data during 20 years of cotton fertility at the experimental site, and then the years corresponding to three empirical frequencies were selected as typical years: 2015 (abundant water year), 2001 (flat water year), and 2006 (dry water year). By combining cotton fertility stages, irrigation frequency and irrigation amount, 21 irrigation regimes (T1~T21) were identified and simulated using the calibrated DSSAT model for cotton irrigation regimes under the three precipitation year types, and the results showed that under the best combination of irrigation usage, yield and water utilization, 2015 (abundant water year), 2001 (flat water year), and T20 irrigation management should be selected in 2006 (dry water year).Under the three typical years, the effect of temperature change on cotton yield and water utilization was investigated, and it was found that the increase of temperature would reduce cotton yield and water utilization, but a reasonable irrigation management would reduce the negative effect of climate change on cotton yield and water utilization,According to the simulation results under different situations, T20 irrigation management can minimize the yield variation range under temperature change, and has a high water utilization rate, which has good applicability. Keywords: Cotton; DSSAT model; Irrigation management; Typical years; Applicability

The design issues of special clothing against high temperature operation was studied. In accordance with the relationship of temperature difference between layers, using the relevant knowledge of heat transfer, heat conduction and the law of energy conservation, the heat relation model of each layer per unit area was established, and the temperature distribution of each layer of fabric materials was obtained, and the temperature distribution results of three layers of fabric materials were analyzed and verified. By using MATLAB software, the four parameter exponential model of skin temperature was obtained. The model can also be extend to the study of the optimal selection of insulation materials.

Light interception plays an important role in cotton (Gossypium hirsutum L.) production, there are few researches on the cotton’s light interception response on irrigation strategy on semi-arid region. Cotton2K is a simulation model for cotton growth developed by Marani (2004), successor of CALGOS (CALifornia GOSsym) (Marani et al.,1992) which derived from GOSSYM. The modified Cotton2K model reimplemented photosynthesis sub procedures compared with the original one, calibrated using field experiments data during the two cotton growing seasons of 2019 and 2020.The normalized root mean squared error (NRMSE) in above ground biomass over the irrigation treatments was 17.44%, showing high simulation accuracy.