Estimating maize evapotranspiration based on hybrid back-propagation neural network models and meteorological, soil, and crop data.

Crop evapotranspiration is a key parameter influencing water-saving irrigation and water resources management of agriculture. However, current models for estimating maize evapotranspiration primarily rely on meteorological data and empirical coefficients, and the estimated evapotranspiration contains uncertainties. In this study, the evapotranspiration data of summer maize were collected from typical stations in Northern China (Yucheng Station), and a back-propagation neural network (BP) model for predicting maize evapotranspiration was constructed based on meteorological data, soil data, and crop data. To further improve its accuracy, the maize evapotranspiration model was optimized using three bionic optimization algorithms, namely the sand cat swarm optimization (SCSO) algorithms, hunter-prey optimizer (HPO) algorithm, and golden jackal optimization (GJO) algorithm. The results showed that the fusion of meteorological, soil moisture, and crop data can effectively improve the accuracy of the maize evapotranspiration model. The model showed higher accuracy with the hybrid optimization model SCSO-BP compared to the stand-alone BP neural network model, with improvements of 2.7-4.8%, 17.2-25.5%, 13.9-26.8%, and 3.3-5.6% in terms of R2, RMSE, MAE, and NSE, respectively. Comprehensively compared with existing maize evapotranspiration models, the SCSO-BP model presented the highest accuracy, with R2 = 0.842, RMSE = 0.433 mm/day, MAE = 0.316 mm/day, NSE = 0.840, and overall global evaluation index (GPI) ranking the first. The results have reference value for the calculation of daily evapotranspiration of maize in similar areas of northern China.

Impacts of climate change on winter wheat net primary production: the regulatory role of crop management.

BACKGROUND: The Huang-Huai-Hai Plain (3HP) is the main agricultural area in China. Although climate change (CC) and crop management (CM) are considered factors affecting the winter wheat net primary production (NPP) in this region, their effects remain unclear. In the present study, we evaluated the relative contributions of CC and CM to winter wheat aboveground NPP (ANPP) in the 3HP and the relationships between climatic factors and ANPP using the first-order difference method from 2000 to 2020. RESULTS: CM had a greater influence on the ANPP of winter wheat than did CC. However, the relative contribution of CM to ANPP gradually decreased in humid and dry sub-humid regions with the development of winter wheat. Furthermore, in areas characterized by low temperatures and limited precipitation, CC became the dominant factor contributing to ANPP, indicating that varieties resilient to drought and cold should be selected in these regions. Minimum and average temperatures were the dominant factors driving spatiotemporal variations in ANPP during the early stage of winter wheat growth, whereas maximum temperature constrained growth throughout the winter wheat growth cycle. When winter wheat entered the vigorous growth stage, precipitation and solar radiation replaced temperature as the driving factors influencing winter wheat growth. CONCLUSION: The results of the present study provide guidance for optimizing winter wheat crop management in the 3HP. © 2023 Society of Chemical Industry.

Applicability of hybrid bionic optimization models with kernel-based extreme learning machine algorithm for predicting daily reference evapotranspiration: a case study in arid and semiarid regions, China.

The accurate prediction of daily reference crop evapotranspiration (ETO) enables effective management decision-making for agricultural water resources; this is crucial for developing water-efficient agriculture. To improve the accuracy of ETO forecasts in data-deficient areas, this study uses a decision tree algorithm (classification and regression tree [CART]) to obtain the effects of various factors on ETO at typical stations in arid and semiarid regions of China. A combination of factors with considerable influence on the model was selected as the input for constructing a kernel-extreme-learning-machine (KELM) daily reference evapotranspiration prediction model, and three bionic optimization algorithms (i.e., sparrow search optimization algorithm, Harris Hawks optimization algorithm, and lion swarm optimization algorithm) were integrated to optimize KELM prediction model parameters and improve the accuracy of daily reference evapotranspiration prediction. The results indicate that temperature (maximum or minimum temperature) is the primary factor influencing ETO, and the range of importance is 0.399-0.554. RH and Ra are also key factors influencing ETO; the hybrid model optimized using the bionic optimization algorithm provides advantages over the independent KELM model, and the SSA-KELM model has the highest accuracy among hybrid models, with a root-mean-square error of 0.408-1.964, R2 of 0.545-0.982, mean absolute error of 0.273-1.086, and Nash-Sutcliffe efficiency coefficient of 0.658-0.967. The top five factors extracted using the CART algorithm are recommended as inputs for constructing the SSA-KELM model for ETO estimation in arid and semiarid regions of China, and this model can also serve as a reference for ETO forecasting in similar regions.

[Effects of attapulgite addition on soil evaporation and crack characteristics]. / å‡¹å‡¸æ£’åœŸæ·»åŠ å¯¹åœŸå£¤è’¸å‘åŠè£‚ç¼ç‰¹å¾çš„å½±å“.

Water is one of the most important factors limiting vegetation recovery and agricultural development in arid and semi-arid areas. The reduction of ineffective soil evaporation can improve soil water use efficiency. As a kind of clay mineral, attapulgite (ATP) plays a critical role in limiting soil evaporation due to its hydrophilicity and adsorption. In this study, three typical soils with different textures (dark loessial soil, cultivated loess soil, and sandy soil) were selected from the arid and semi-arid area of the Loess Plateau, and five ATP additions (0%, 1%, 2%, 3% and 4%) were set for conducting soil evaporation experiments under natural conditions using micro-evaporators to investigate the effects of ATP addition on different soil evaporation processes and the characteristics of evaporation surface cracks. The results showed that the cumulative evaporation and evaporation loss ratio of the same soil decreased with the increases of ATP addition when the ATP addition was <3%. When ATP was added at 3%, the cumulative evaporation and evaporation loss ratio of dark loessial soil and sandy soil decreased, while those of cultivated loess soil increased. When ATP was added at 4%, the cumulative evaporation decreased and the evaporation loss ratio increased for dark loessial soil, the cumulative evaporation increased and the evaporation loss ratio decreased for sandy soil, and the cumulative evaporation and evaporation loss ratio decreased for cultivated loess soil. The average cumulative evaporation of different soils followed an order of dark loessial soil > cultivated loess soil > sandy soil. Soil water content of ATP treatment was consistently higher than that of control throughout the whole evaporation process in the same soil. Simulations of cumulative evaporation versus the square root of time indicated that the amount of water released from the ATP-treated soil samples at the end of evaporation was higher than that of the control. After the addition of ATP, the crack area density of dark loessial soil and cultivated loess soil increased significantly, and the crack area density of sandy soil increased with the increase of ATP addition. The crack area density of all three soils reached the maximum at 4% of ATP addition. In summary, ATP addition of 3% could minimize the ineffective evaporation of soil water.

Transgenic tobacco plants expressing atzA exhibit resistance and strong ability to degrade atrazine.

Atrazine chlorohydrolase (AtzA) catalyzes hydrolytic dechlorination and can be used in detoxification of atrazine, a herbicide widely employed in the control of broadleaf weeds. In this study, to investigate the potential use of transgenic tobacco plants for phytoremediation of atrazine, atzA genes from Pseudomonas sp. strain ADP and Arthrobacter strain AD1 were transferred into tobacco. Three and four transgenic lines, expressing atzA-ADP and atzA-AD1, respectively, were produced by Agrobacterium-mediated transformation. Molecular characterization including PCR, RT-PCR and Southern blot revealed that atzA was inserted into the tobacco genome and stably inherited by and expressed in the progenies. Seeds of the T(1) transgenic lines had a higher germination percentage and longer roots than the untransformed plants in the presence of 40-150 mg/l atrazine. The T(2) transgenic lines grew taller, gained more dry biomass, and had higher total chlorophyll content than the untransformed plants after growing in soil containing 1 or 2 mg/kg atrazine for 90 days. No atrazine residue remained in the soil in which the T(2) transgenic lines were grown (except 401), while, in the case of the untransformed plants, 0.91 mg (81.3%) and 1.66 mg (74.1%) of the atrazine still remained in the soil containing 1 and 2 mg/kg of atrazine, respectively, indicating that the transgenic lines could degrade atrazine effectively. The transgenic tobacco lines developed could be useful for phytoremediation of atrazine-contaminated soil and water.