Yield Data Provide New Insight into the Dynamic Evaluation of Maize’s Climate Suitability: A Case Study in Jilin Province, China

Real-time hazard assessment of maize based on the chilling injury process -- Using a standard curve to establish a daily cumulative assessment method

Cold damage caused by low temperatures is known as chilling injury (CI), and it has consistently been one of the primary meteorological disasters affecting maize. With ongoing global climate change, the issue of chilling injury is becoming more prominent, exhibiting new characteristics and presenting new challenges. Consequently, understanding the disaster process and conducting a more refined real-time chilling injury identification have become significant challenges. In this study, we divided maize planting areas into seven maturity types based on the accumulated temperature, constructed a standard curve of the daily accumulated temperature from 1991 to 2020, proposed real-time identification indicators based on the CI process, and developed a real-time CI hazard assessment model. The results indicated that the model can capture independent CI events and rapidly determine the location, intensity, duration and scope of CIs, thereby providing a basis for accurately understanding the impact of chilling injury and taking timely countermeasures. The combination of accumulated temperature standard curves for seven maturity types of maize and the CI curve was used to construct the CI daily scale identification indicator, ΔEAT. Judgment thresholds for the CI identification indicators at various maturity levels were obtained by correlating them with historical disaster data. The frequency and intensity of maize CI gradually increased from the extremely late-maturing zone to the extremely early-maturing zone, with the seeding and emergence periods being the peak periods for CI. The spatiotemporal evolution characteristics of the three different degrees of CI events in 1992, 2004, and 2017 were consistent with the historical disaster records. Northeastern Inner Mongolia and most of Heilongjiang were found to be high-hazard areas for maize CIs. The constructed daily CI identification indicators can accurately and rapidly identify maize CIs, providing practical and targeted guidance for combating these injuries.

Crop Mapping Using the Historical Crop Data Layer and Deep Neural Networks: A Case Study in Jilin Province, China

Machine learning combined with satellite image time series can quickly, and reliably be implemented to map crop distribution and growth monitoring necessary for food security. However, obtaining a large number of field survey samples for classifier training is often time-consuming and costly, which results in the very slow production of crop distribution maps. To overcome this challenge, we propose an ensemble learning approach from the existing historical crop data layer (CDL) to automatically create multitudes of samples according to the rules of spatiotemporal sample selection. Sentinel-2 monthly composite images from 2017 to 2019 for crop distribution mapping in Jilin Province were mosaicked and classified. Classification accuracies of four machine learning algorithms for a single-month and multi-month time series were compared. The results show that deep neural network (DNN) performed the best, followed by random forest (RF), then decision tree (DT), and support vector machine (SVM) the least. Compared with other months, July and August have higher classification accuracy, and the kappa coefficients of 0.78 and 0.79, respectively. Compared with a single phase, the kappa coefficient gradually increases with the growth of the time series, reaching 0.94 in August at the earliest, and then the increase is not obvious, and the highest in the whole growth cycle is 0.95. During the mapping process, time series of different lengths produced different classification results. Wetland types were misclassified as rice. In such cases, authors combined time series of two lengths to correct the misclassified rice types. By comparing with existing products and field points, rice has the highest consistency, followed by corn, whereas soybeans have the least consistency. This shows that the generated sample data set and trained model in this research can meet the crop mapping accuracy and simultaneously reduce the cost of field surveys. For further research, more years and types of crops should be considered for mapping and validation.

Comprehensive climatic suitability evaluation of peanut in Huang-Huai-Hai region under the background of climate change

The climate changes influence the growing suitability of peanut, an important oil crop. Climatic suitability evaluation in the Huang-Huai-Hai region, the main peanut producing region of China, which can optimize peanut planting structure and provide basis for increasing output. In this study, the temperature, precipitation, sunshine and comprehensive suitability models were established by using the climatic suitability function in different growth periods of peanut. In this study, the climate suitability function of peanut in different growth periods was used to establish the temperature, precipitation, sunshine and comprehensive suitability model. Combined with the meteorological data after Anusplin interpolation, the spatial distribution and chronological change of peanut climate suitability were analyzed. The results show that with climate change, the overall climate becomes warmer and drier and the temperature and precipitation suitability increase, but the sunshine suitability decreases. Based on the comprehensive suitability model, the suitability evaluation results are divided into four levels: the most suitable, suitable, sub-suitable and unsuitable. Among them, the most suitable peanut planting areas in the Huang-Huai-Hai region are concentrated in the west of the Haihe River Basin and the Huaihe River Basin. The data from the next 30 years show that both the most suitable and suitable areas have been expanded. Through the verification of yield correlation analysis and spatial distribution of disaster frequency, it can be seen that the evaluation results have high accuracy, which can be used to guide and optimize peanut production practices.

Dynamic evaluation of college English writing ability based on AI technology

To accurately evaluate and improve college students’ English writing ability, this article proposes a dynamic evaluation method of college English writing ability based on artificial intelligence technology. First, a dynamic evaluation model of college English writing ability is constructed. Second, the index system of English writing dynamic evaluation model is established. Based on this, the dynamic evaluation of college English writing ability is realized. The experimental results show that the design method in this paper can effectively realize the dynamic evaluation of the writing process. After the application of the design method, the number of students interested in writing has increased by 37.8%, and the enthusiasm of students to participate in writing has been improved, with a view to providing some help to improve students’ English writing ability through this research.

Assessment of Seasonal Drought Impact on Potato in the Northern Single Cropping Area of China

Drought is one of the key limiting factors for potato yield in the northern single cropping area (NSCA) in China. To analyze the impact of drought on potato yield in the NSCA, this study first analyzed the variation of dry/wet conditions in the plantable areas on a seasonal scale using the standardized precipitation evapotranspiration index (SPEI). Secondly, the changes in yield structure in the last 36 years were systematically analyzed and divided the total yield change into planting area contribution and climate yield contribution. Finally, a regression model of the seasonal drought index and contributing factors of total yield change in different administrative regions was constructed. The results showed that the main factors affecting the total potato yield of the NSCA began to change from yield to planting area in the 1990s, while the barycenter of the output structure and population moved to the southwest, with grassland being the main source; dry/wet conditions (year i) had varying degrees of effect on contributing factors (year i, year i + 1) of total yield change in different administrative regions that were not limited to the growing season; the non-overlap of high-yield area, high-adaptability area and planting area was the urgent problem to be solved for the NSCA. The results of this study can provide a scientific basis for NSCA crop management and communication with farmers, providing new ideas for sustainable production in other agricultural regions in the world.

Leaf transcriptomic response mediated by cold stress in two maize inbred lines with contrasting tolerance levels

Maize (Zea mays L.) is a thermophilic plant and a minor drop in temperature can prolong the maturity period. Plants respond to cold stress through structural and functional modification in cell membranes as well as changes in the photosynthesis and energy metabolism. In order to understand the molecular mechanisms underlying cold tolerance and adaptation, we employed leaf transcriptome sequencing together with leaf microstructure and relative electrical conductivity measurements in two maize inbred lines, having different cold stress tolerance potentials. The leaf physiological and transcriptomic responses of maize seedlings were studied after growing both inbred lines at 5 °C for 0, 12 and 24 h. Differentially expressed genes were enriched in photosynthesis antenna proteins, MAPK signaling pathway, plant hormone signal transduction, circadian rhythm, secondary metabolites related pathways, ribosome, and proteasome. The seedlings of both genotypes employed common stress responsive pathways to respond to cold stress. However, the cold tolerant line B144 protected its photosystem II from photooxidation by upregulating D1 proteins. The sensitive line Q319 was unable to close its stomata. Collectively, B144 exhibited a cold tolerance owing to its ability to mediate changes in stomata opening as well as protecting photosystem. These results increase our understanding on the cold stress tolerance in maize seedlings and propose multiple key regulators of stress responses such as modifications in photosystem II, stomata guard cell opening and closing, changes in secondary metabolite biosynthesis, and circadian rhythm. This study also presents the signal transduction related changes in MAPK and phytohormone signaling pathways in response to cold stress during seedling stage of maize.