The agricultural meteorological service is the largest and most complete professional meteorological service in China's meteorological industry, and plays an important role in meteorological support of agricultural production. To promote the development of large−scale planting in China's agricultural production As China's agricultural production transitions towards large−scale farming, the constructionof high−standard farmland and its rapid developmentthe continuous advancement and rapid development of high−standard farmland construction. This study proposed a path to improve the meteorological guarantee capacity of high−standard farmland through field investigations and sorting out existing service products, analyzing the needs of high−standard farmland meteorological guarantee services, and combining the current supply−side service status analysis, in order to provide a reference for improving the effectiveness of meteorological guarantee services for high standard farmland construction. The results showed that some provinces (autonomous regions and municipalities) meteorological departments had laid out automated meteorological observation equipment of multiple types such as farmland microclimate and automatic soil moisture, and had initially built an agricultural meteorological automatic observation station website on high standard farmland plots. Some meteorological service products for high−standard farmland had been formed. There were still some problems with the meteorological support services for high−standard farmland, such as the lack of systematic planning for the layout of meteorological observation stations, inconsistency of data collection standards, the failure of service products to meet the refined needs, and the imperfect data management mechanisms. In light of this, it is recommended to strengthen top−level design, enhance data value, focus on technological innovation and implement socialized services to improve the meteorological support service capacity for high−standard farmland.

High−standard farmland in Xinxiang, Henan province, was divided into a fully responsive area, a partially responsive area and a non−responsive area in October 2023. Within the partially responsive area, two treatments were randomly implemented: one without controlling fusarium head blight (FHB) and the other without controlling dry hot wind (DHW). This resulted in a total of five experimental treatments. During the trial, two issues of Henan agricultural meteorological disaster warning information (for DHW and FHB) and two important issues of Henan agricultural meteorological service weekly reports were released. The experimental site followed the trial design requirements for two irrigation treatments (at the green−up and jointing stages), FHB control (at the heading stage), and DHW control (at the grain filling stage). Combined with additional surveys and observations, the impact of different response treatments to agricultural meteorological service information on major growth factors and economic benefits of winter wheat was quantitatively analyzed. The results indicated that: (1) promptly responding (within 1−14d) to early warning information on wheat scab and dry−hot wind disasters in winter wheat, and implementing relevant control measures, could effectively mitigate the damage. In some response areas, the percentage of diseased ears of winter wheat fell by 1.50 percentage points in fields where wheat scab control measures were in place. In some response areas, the thousand-grain weight of winter wheat in fields where dry-hot wind control measures were implemented increased by 1.42g, and the grain filling rate increased by 0.12g·d⁻¹. (2) Significant differences were observed in the main growth conditions of winter wheat under different response treatments during the heading and grain filling stages. At the head stage, the dry weight of the ears, stems and leaves in the fully responsive treatment was 2.73g, 6.57g and 2.74g respectively, which was overall better than in the non-responsive treatment. At the grain filling stage, the dry weights of ears and leaves, and the number of grains per ear were 21.34g, 2.15g, and 41.93 grains, respectively, showing a trend of fully responsive treatment>partially responsive treatment> non-responsive treatment. (3) Timely response to agricultural meteorological service information could significantly increase production and income. Compared with the partially responsive treatment, the fully responsive treatment increased the yield of winter wheat by 666.67kg per hectare and the revenue by 1690.00yuan. Compared to the non−responsive treatment, it increased the yield by 2310.81kg per hectare and increased the revenue by 4435.95yuan. The partially responsive treatment resulted in an increase in yield of 1644.14kg per hectare compared to the non−responsive treatment and an increase in revenue of 2745.95yuan. This study provides important scientific and technological support and production guidance for improving winter wheat yields by quantitatively evaluating the benefits generated by different responses to agricultural meteorological service information.

Taking the quality evaluation of meteorological support engineering for High−standard farmland (MSEWFF) as the research object, this thesis utilized the Delphi method to identify determinants of project quality. A comprehensive system of evaluation indices for the quality of the MSEWFF was established, and the Analytic hierarchy process was employed to determine the individual indicator weights. Based on the fuzzy comprehensive evaluation method, a quantifiable model for assessing MSEWFF quality was developed, allowing for the assessment of both overall and individual indicators (including grading and scoring) for targeted projects. It identified strengths and deficiencies in construction quality, thereby providing a reference basis for quality evaluation, planning and enhancement of such engineering projects. The results showed that the project quality evaluation system comprised five first−level indicators−observation station network project, forecast & early warning system, information release system, disaster prevention and mitigation project, and benefit assessment of the meteorological support−which were further divided into eight second−level and 25 third−level indicators. The weight ranking of the five first−level indicators, from highest to lowest, was as follows: observation station network (0.261) > forecast and early warning (0.251)>disaster prevention & mitigation (0.228) > information release (0.154)>benefit assessment (0.106). Notably, the top three higher−weighted primary indicators collectively accounted for 74% of the total weight, indicating their critical role in the project quality evaluation. Although the remaining two first−level indicators (information release and benefit assessment) constituted only 26% of the total weight, they served as important connecting links within the agricultural meteorological service chain. Among the second−level indicators, three pairs of indicators under the same first−level category exhibit nearly comparable weights. For the third−level indicators, irrigation and drainage projects, disaster forecast and warning and timeliness of observational data ranked as the top three highest−weighted indicators. The proposed model was applied to evaluate a case project in Dancheng county, Henan province, yielding a comprehensive quality score of 86.3, which corresponds to a Good rating. Additionally, all five first−level indicators achieved Good ratings, though variations existed among the tertiary indicators. The project demonstrated strong performance in areas such as observation station network construction, meteorological information dissemination systems, and infrastructure and capacity building for disaster prevention and mitigation. However, deficiencies were identified in forecast & early warning accuracy and satisfaction with agricultural meteorological services. The case study validates the effectiveness and applicability of the model and provides a scientific basis for quality assessment and management of such projects.

 From the perspective of nature−society interaction, this paper constructed a theoretical framework of meteorological environment−land quality−household response−disaster prevention and loss reduction. Then, based on the provincial panel data of China from 2004 to 2022, this paper employed a staggered difference−in−differences (DID) model to analyze the effects and mechanisms of high−standard farmland construction on disaster prevention and mitigation in the context of agricultural meteorological disasters. The results showed that: (1) high−standard farmland construction significantly and sustainably enhanced the resilience of agriculture to meteorological disasters, exhibiting remarkable disaster prevention and mitigation effects. Specifically, the construction of high−standard farmland had reduced the disaster incidence by 12.7 percentage points and crop loss by 16.5% in treatment regions. (2) While these effects were positive across different types of disasters, levels of post−construction management, and agricultural functional areas, the impact was most significant in drought prevention. (3) The disaster prevention and loss reduction effect of high−standard farmland construction was primarily achieved through improving agricultural insurance, disaster monitoring capabilities, agricultural socialized services and the development of facility agriculture. Therefore, in advancing the new round of high-standard farmland construction, it is crucial to adopt a phased and differentiated approach, establish a long−term mechanism that integrates construction, management and maintenance, and guide the shift in its utilization towards modernization and intensification. These measures will provide fundamental support for building a strong agricultural sector.

Cosmic−ray neutron sensing (CRNS) method is a new technology enabling soil moisture detection at hectometer scales, bridging the gap between traditional point measurements and remote sensing. This study evaluated the applicability of CRNS in a typical oasis farmland ecosystem using in−situ data collected at the Wulanwusu Agro−meteorological Experiment Station of the China Meteorological Administration from May to November 2020, including CRNS measurements, single−point oven−drying gravimetric data, frequency−domain reflectometry (FDR) data and distributed gravimetric data. The study focused on the relationship between the regional soil moisture content obtained by the CRNS using the N₀ parameter method and the soil moisture content measured by the single−point oven−drying gravimetric method, the FDR method and the distributed oven−drying gravimetric method. The results indicated that the soil moisture content measured by CRNS correlates well with that measured by the single−point oven−drying gravimetric method, especially reflecting the moisture variation characteristics of the surface soil layer (0−30cm) in the experimental site, with a maximum correlation coefficient of 0.66. Due to the influence of the FDR sensor installation depth, CRNS exhibited higher sensitivity to precipitation events than the FDR method. The multilayer mean values of soil moisture content measurements by the FDR method and the CRNS method exhibited a significant linear relationship. For the 0−30cm soil moisture layer, the coefficient of determination R² was 0.57, and the root mean square error (RMSE) was 0.018 cm³·cm⁻³. When compared with the distributed oven−drying gravimetric method, the coefficients of determination (R²) for CRNS were 0.61, 0.61, 0.52 and 0.30 for the 0−5cm, 0−10cm, 0−20cm and 0−35cm soil layers, respectively, with corresponding RMSE values of 0.015, 0.010, 0.011 and 0.017cm³·cm⁻³. In oasis farmland ecosystems, compared to the single−point oven−drying gravimetric method, the FDR method and the distributed oven−drying gravimetric method, the CRNS more accurately reflected the spatiotemporal variation of soil moisture at the regional scale and was more sensitive to minor or low−intensity prolonged precipitation events. It also achieves a more accurate measurement of the average surface soil moisture content.

The inversion of the fine spatial distribution of soil water content in the seedling stage of winter wheat based on multispectral remote sensing of UAV can be used as a reference to plan irrigation in agricultural and improve irrigation efficiency. This study took the soil water content in the top layer (5cm) of winter wheat seedlings in Zhengzhou and Xinxiang as the inversion object, based on multispectral data from drones, selected the optimal spectral features, compared and validated the simulation results of random forest (RF) and gradient boosting (GB) machine learning models, and performed grid inversion of soil water content in the experimental area based on the optimal model. The results showed that the GB and RF models had a better inversion effects on the topsoil water content in Zhengzhou and Xinxiang during the wheat seedling stage, with R² and nRMSE ranging from 0.926 to 0.983 and 5.6% to 14.4%, respectively. The modeling accuracy of GB and RF based on the aggregated data from both sites was good, with R² and nRMSE of 0.902, 0.787 and 6.9%, 10.2%, respectively. The simulation results of the GB model were better than the RF model. The spatial accuracy of soil water inversion during the winter wheat seedling stage was 2cm, which better revealed the spatial heterogeneity of soil water in farmland. Both models performed well for different underlying surfaces and weather conditions and had high model generalization. The results can provide theoretical and technical support for accurate inversion of the water content of farmland soil using multi−spectral remote sensing from UAVs, which can benefit the development of precision agriculture and smart agriculture. 

 This study utilized microclimate data from high−standard farmlands during wheat growing season (October to May) from 2021 to 2023. By investigating the lagged response of soil relative humidity (SRH) to microclimate factors, this study developed three machine learning models, Random Forest (RF), Backpropagation Neural Network (BPNN) and Support vector regression (SVR), using the Optuna framework for hyperparameter optimization. The models predicted SRH at three forecasting horizons (3−, 5− and 10−days) across five soil depths (10cm, 20cm, 30cm, 40cm and 50cm) to establish a predictive reference system for high−standard farmland. The results indicated that: (1) SRH exhibited a fluctuating decrease throughout winter wheat growth stages, with maximum values (90.4%) during sowing to emergence and minimum values (73.9%) at anthesis to maturity stage. (2) The response characteristics of SRH to microclimate factors varied significantly. SRH demonstrated the strongest yet slowest response to ground temperatures (r=0.32–0.57; 5–10d lag), and the weakest yet fastest response to air relative humidity (r<0.20; 1–3d lag). As soil depth increased, the correlation between SRH and precipitation, daily mean air temperature and daily maximum temperatures decreased, whereas correlations with maximum daily wind speed and soil temperatures (10cm, 20cm and 50cm depths) increased gradually. (3) Among the three simulation models, the RF model achieved superior performance across all prediction horizons (R²=0.87−0.98, RMSE=0.02−0.05, MAE=0.01−0.03), significantly outperforming SVR (R²=0.77−0.97, RMSE=0.03−0.07, MAE=0.02−0.04) and BPNN (R²=0.60−0.97, RMSE=0.04−0.07, MAE=0.01−0.06). A comprehensive evaluation showed that the RF model was better suited for short−term predictions of soil moisture in high−standard farmland, providing valuable technical support for precise water management in Henan. 

Accurate simulation of reference crop evapotranspiration (ET₀) can provide scientific guidance for agricultural water resources management of high−standard farmland. However, issues such as data quality of microclimate stations pose some challenges for ET₀ estimation. To solve above problems, this study selected the daily meteorological observation data of 16 farmland microclimate stations with complete data records and 13 nearby national meteorological stations in Henan province from 2020 to 2023, adopted 13 typical empirical models and 8 machine learning models to estimate ET₀, and took Penman−Monteith (PM) model as the benchmark. The accuracy of each model was evaluated, and 15 types of ET₀ estimation combination schemes based on the optimal model were given to find accurate, suitable and simple alternative models to estimate ET₀. The results showed that in addition to wind speed (WS), the fitting degrees of average temperature (T_ave), maximum temperature (T_max), minimum temperature (T_min), mean air relative humidity (RH), vapor pressure deficit (VPD) and net solar radiation (R_n) observed between microclimate stations and national stations were generally higher than 0.654 (P<0.05). The correlation between ET₀ calculated based on the above two datasets was also higher, with R² of 0.880 and RMSE of 0.588mm. Among the 13 empirical models, the Valiantzas3 (Val3) model considering temperature, radiation, relative humidity and wind speed exhibited the best effect (R²=0.933, RMSE=0.461mm), followed by Jensen−Haise (JH) model considering radiation and temperature factors (R²=0.916, RMSE=0.774 mm). The overall accuracy of the temperature−based Hargreaves−Samani (HS) model was high (R²=0.817, RMSE=0.713mm), while the simulation accuracy of the mass−transfer based Penman (Pen), WMO and Trabert (Tra) models was lower and there were not recommended as the choice of ET₀ simulation. Among the 8 machine learning models, the simulation accuracy of Multilayer Perceptron (MLP) model was the best (R²=0.998, RMSE=0.059mm), and the importance order of each input parameter was: R_n>VPD>WS>T_max>T_ave>T_min>RH. Among 15 different model input parameter combination schemes based on the MLP model, the simulation accuracy of the machine learning model was generally better than that of the empirical model under the same input parameter conditions, with the combined model of R_n+T_ave+RH+WS being the best.

Drought is one of the major meteorological disasters affecting winter wheat production in the Huang−Huai−Hai region. Water deficits during winter wheat growing season directly affect the final yield and grain quality. Based on meteorological data from 39 high−standard farmland stations, soil relative moisture data and historical climatic data from national meteorological stations in Henan province during 2020−2023, this study established daily−scale Standardized Precipitation Evapotranspiration Index (SPEI) and Crop Water Stress Index (CWSI) to evaluate their applicability in drought monitoring for high−standard farmlands. The results showed that: (1) for 84.6% of stations showed extremely significant (P＜0.01) positive correlations between SPEI and soil relative moisture index (Rsm), with correlation coefficients ranging from 0.15 to 0.73. Although SPEI overestimated regional drought severity compared to Rsm, it showed high consistency with actual disaster during the severe drought year 2022, when the grades difference was −0.60. (2) For 69.2% of stations exhibited extremely significant (P＜0.01) negative correlations between CWSI and Rsm, and the correlation coefficients were −0.47 to −0.11, with notable spatial variation. The percentage of days with a drought grades error ≤1 compared to Rsm at the station level was 70.0%, but there was a lagged response to the start and end of drought events. (3) SPEI and CWSI demonstrated good applicability for drought monitoring at regional and station level, respectively. It is recommended to optimize the baseline parameters of CWSI before wide application, and to establish a high−standard farmland drought monitoring system, which should integrate SPEI as an early−warning indicator for regional meteorological drought and CWSI as an decision−making tool for irrigation management at the station level.

In order to make full use of the observation data of high-standard farmland microclimate in Xinjiang, the difference observation data from the high-standard farmland microclimate stations and the national station were evaluated. It can be provided reference for adjusting the planting system and crop layout according to local conditions using heat resources. The daily temperature observation data of high-standard farmland agricultural microclimate stations and adjacent national stations in southern and northern Xinjiang from 2015 to 2024 were selected, and the characteristics of heat resources during winter wheat growing season in high-standard farmland and differences with national stations were clarified by means of mathematical statistical analysis methods such as scatter plot and linear regression. The results showed that: (1) the correlation coefficients of daily average temperature, maximum temperature and minimum temperature between agricultural microclimate stations in southern Xinjiang and northern Xinjiang and national stations were between 0.997 and 1.000 (P<0.001), with good consistency. The daily average temperature of winter wheat in different growth stages of high-standard farmland was 0.4−2.1°C lower than that of the corresponding national station, and the daily minimum temperature was 0.8−3.2°C lower, but the daily maximum temperature was generally 0.0−0.7°C higher. The temperature difference was larger during the seeding−emergence period, tillering period, jointing period, heading-flowering period and filling period, while the maximum difference was 3.2°C, but the difference was smaller in during wintering period and regeneration period with 1.6°C. (2) The average annual ≥0°C active accumulated temperature of winter wheat during the growing season of high−standard farmland agricultural microclimate stations in southern and northern Xinjiang was 8.0−61.0°C·d less with national stations, wiht the average annual ≥10°C active accumulated temperature was 0−59.3°C·d less. The average annual <0°C active accumulated temperature was 26.7−96.0°C·d more in the wintering period and 0.2−1.6°C·d less druing the regeneration period. (3) Compared with the corresponding national stations, the extreme maximum temperature of high-standard farmland agricultural microclimate station in southern Xinjiang was 0.4°C higher and the minimum temperature was 0.1°C lower. The extreme maximum and minimum temperature of the high-standard farmland agricultural microclimate station in northern Xinjiang were 0.5°C and 0.2°C lower, and the date of extreme temperature was inconsistent. The average annual ≥35°C high temperature days in the heading-flowering period and filling period of winter wheat in the high-standard farmland agricultural microclimate stations of southern Xinjiang were 0.7d more than those in northern Xinjiang, and 3.4d less than those in northern Xinjiang. The average number of low temperature days ≤-3°C in the regeneration period of winter wheat at the agricultural microclimate stations in southern and northern Xinjiang was more than 1.8d and 1.0d. The heat resources of winter wheat growing season in the high-standard farmland microclimate station were highly correlated with the adjacent national stations. There were certain differences between the two, which were affected by the crop growth environment and the underlying surface. The difference law can provide technical support for the use of national meteorological stations for winter wheat meteorological services. 

This study investigated the characteristics of temperature and humidity variations in winter wheat fields and their quantitative relationship with observations at meteorological station. Temperature and humidity recorders were installed at heights of 10, 30, 50, 70 and 150cm in the winter wheat experimental field of Shangqiu agrometeorological observation station from October 2022 to May 2023. The spatiotemporal characteristics of temperature and humidity in the field were analyzed across different growth stages of winter wheat and different weather types. Linear regression models were developed to simulate field temperature and humidity using thermometer screen data for both the entire growth stage and different weather types. Model evaluation was conducted to enhance weather services for winter wheat production and improve agricultural meteorological service quality. The results showed that: (1) the diurnal variation characteristics of temperature and humidity at different heights during tillering−overwintering, reviving−heading and flowering−maturity stage of winter wheat were consistent with thermometer screen observations. Field temperature at all heights were 0.1−4.1°C higher than thermometer screen temperature from 7：00 to 16：00, and 0.1−3.7°C lower during other periods. Temperature peaks occurred 1−2h earlier in the field, while troughs appeared 0−1 hour earlier compared to the thermometer screen. (2) During flowering−maturity stage, humidity at heights of 50−150cm was 0.1−3.7 percentage points lower than that the thermometer screen at specific morning hours. In all other growth stages, heights and times, it consistently exceeded the thermometer screen by 0.6~26.8 percentage point. Relative humidity troughs in the thermometer screen was delayed by 1−2h compared to field. (3) The amplitude of temperature and humidity changes followed the pattern: sunny>cloudy>overcast days. Temperature differences between thermometer screen and field were 0.4°C and 0.5°C greater on sunny days than cloudy and overcast days, respectively. Similarly, relative humidity differences were 1.8 and 2.1 percentage point greater on sunny days than cloudy and overcast days. (4) The developed simulation models were approved by 0.01 level significant test on daily and hourly scales. Daily−scale simulation effect outperformed hourly−scale, and temperature modeling achieved higher accuracy than humidity, and model accuracy improved with increasing height. Model performance acrossed weather types followed a consistent hierarchy: cloudy> overcast>sunny days. 

To address the challenges of small target size, dense distribution and occlusion among winter wheat ears in open field environments, this study focused on winter wheat captured by UAV imagery and proposed an improved detection method based on the YOLOv8 model. The SimAM attention mechanism was introduced into the Neck (Neck network) while the GhostNetV2 module was integrated into the C2f module within the Neck. These enhancements improved the representation of spatial and channel features, while maintaining efficient feature fusion and reducing model complexity. As a result, the detection network was better adapted to the complex conditions of open field winter wheat ear detection. In addition, the input image resolution was set to 1280px×1280px to maximize the preservation of critical visual features. The results showed that the improved YOLOv8 model achieved an average precision (AP) of 93.1% and an F1 score of 90.5%, with a model size of only 18.3MB and 9.4 million parameters. Compared to the original YOLOv8, the improved version yield increased of 0.5 percantage point and 0.8 percantage point in AP and F1 score, respectively, while reducing the model size and parameter counted by 3.3MB and 1.7 million parameters. The resulting model is more lightweight and efficient, outperforming the standard YOLOv8 in detecting small, densely distributed and highly occluded winter wheat ears under complex field conditions.

By comprehensively comparing the simulation accuracy, spatial distribution, and data distribution histograms of four machine learning algorithms (Lasso regression, Ridge regression, Gaussian process regression, and Random Forest regression), on the Leaf area index (LAI) of winter wheat under various feature combinations, a suitable unmanned aerial vehicle model for monitoring the LAI of winter wheat in the north China region was selected. Using the monitoring results from this model as ground truth, a Sentinel−2 winter wheat LAI monitoring model was developed to dynamically monitor and evaluate the LAI of winter wheat in Hebi city, based on the distribution of cultivated land. This study addressed the scale−up challenge in satellite remote sensing leaf area index (LAI) modeling, by innovatively introducing multispectral Unmanned aerial vehicles (UAV) as an intermediate scale. The results showed that: (1) among the four machine learning algorithms applied to UAV multispectral data, Lasso regression achieved the highest simulation accuracy (RMSE=1.472), followed by Ridge regression (RMSE=1.488), Gaussian process regression (RMSE=1.538) and Random forest regression (RMSE=1.582). The Ridge regression provided a balanced performance in both high and low values, while Random forest regression overestimates low values while underestimates high values, Lasso regression tended to overestimate low values and Gaussian process regression underestimated both extremes. The result histograms for Gaussian process regression, Lasso regression and Ridge regression exhibited a normal distribution, however, the histogram of Random forest regression displayed greater dispersion. Consequently, Ridge regression utilizing 18 features was confirmed to be the optimal model for monitoring LAI using UAV. (2) For Sentinel−2 based modeling, the algorithm performance ranked as Ridge regression > Lasso regression > Gaussian process regression > Random forest regression, and the Ridge regression utilizing 26 features was confirmed to be the optimal model for Sentinel−2 LAI monitoring. (3) The dynamic monitoring of cropland LAI in Hebi using Sentinel−2 data revealed that the average LAI values on March 28, April 27 and May 12 were 2.50, 3.22 and 2.92, respectively. This demonstrated stable monitoring with a higher spatial resolution than MODIS product.

This study systematically analyzed the coupling effects of planting and wind direction on field temperature and humidity of winter wheat during the jointing−maturity period in high−standard farmland in Henan province, based on wind speed and direction data, temperature and humidity data at 30cm height and real−life monitoring maps of planting directions from 41 high−standard farmland microclimate monitoring stations in Henan province from 2020 to 2023, combined with meteorological data from 41 neighboring national meteorological stations. The results showed that. (1) from 2020 to 2023, the average wind speed during the jointing−maturity stage of winter wheat in high standard fields in Henan province gradually decreased from west to east. The average wind speed in Sanmenxia, western Henan was 0.6m·s⁻¹ higher than that in Shangqiu, eastern Henan. The frequency of wind level 5 and above was higher in Hebi, Luoyang and Sanmenxia in northern Henan, Zhengzhou in central Henan (40%−51%). Strong winds mainly occurred during the jointing−heading or heading−flowering stages of winter wheat. (2) The planting in northern Henan was mainly oriented in east−west direction, generally corresponding to southerly winds. The east−west direction of planting fields in southern Henan cultivation corresponded to easterly winds, while the north−south direction corresponded to north−westerly winds. In eastern and central Henan, the dominant wind directions in north−south−oriented wheat fields were mainly southerly and northerly, and the planting directions were relatively consistent with the local dominant wind directions. (3) Planting and wind directions significantly affected field temperature and humidity. In fields at similar latitudes, winter wheat planted in east−west rows typically exhibited higher field temperatures than north−south rows at similar latitudes. When the wind directions coincided with the planting directions, the temperature and humidity were reduced to a certain extent.