Crop yield and production responses to climate disasters in China

DNA Methylation Dynamics in Response to Drought Stress in Crops

Drought is one of the most hazardous environmental factors due to its severe damage on plant growth, development and productivity. Plants have evolved complex regulatory networks and resistance strategies for adaptation to drought stress. As a conserved epigenetic regulation, DNA methylation dynamically alters gene expression and chromosome interactions in plants' response to abiotic stresses. The development of omics technologies on genomics, epigenomics and transcriptomics has led to a rapid increase in research on epigenetic variation in non-model crop species. In this review, we summarize the most recent findings on the roles of DNA methylation under drought stress in crops, including methylating and demethylating enzymes, the global methylation dynamics, the dual regulation of DNA methylation on gene expression, the RNA-dependent DNA methylation (RdDM) pathway, alternative splicing (AS) events and long non-coding RNAs (lnc RNAs). We also discuss drought-induced stress memory. These epigenomic findings provide valuable potential for developing strategies to improve crop drought tolerance.

Energy difference-driven ROS reduction for electrochemical tracking crop growth sensitized with electron-migration nanostructures

Aiming for sustainable crop productivity under changing climate conditions, it is essential to develop handy models for in-situ monitoring of reactive oxygen species (ROS). Herein, this work reports a simple electrochemical sensing toward hydrogen peroxide (H2O2) for tracking crop growth status sensitized with electron-migration nanostructure. To be specific, Cu-based metal-organic frameworks (MOFs) with high HOMO energy level are designed for H2O2 reduction on account of Cu(I)/Cu(II) redox switchability. Importantly, the sensing performance is improved by electrochemically reduced graphene oxide (GO) with ready to use feature. To overcome the shortcomings of traditional liquid electrolytes, conductive hydrogel as semi-solid electrolyte exhibits the adhesive property to the cut plant petiole surface. Benefitting from the preferred composite models and conductive hydrogel, the electrochemical sensing toward H2O2 with high sensitivity and good anti-interference against the coexistent molecules, well qualified for acquiring plant growth status.

Combined biochar and water-retaining agent application increased soil water retention capacity and maize seedling drought resistance in Fluvisols

Global climate change has accelerated the occurrence of agricultural drought events, which threaten food security. Therefore, improvements in the soil water retention capacity (WRC) and crop drought resistance are crucial for promoting the sustainability of the agricultural environment. In this study, we explored the effects of applying biochar and water-retaining agent (WRA) on soil WRC and crop drought resistance in a Fluvisols, along with their potential mechanisms. We applied two types of biochar (based on wheat and maize straw) and two WRAs (polyacrylamide and starch-grafted sodium acrylate) to Fluvisols with different textures, and then evaluated soil water retention and crop drought physiological resistance. The combined biochar and WRA treatment increased the WRC in both the sandy loam and clay loam Fluvisols. Biochar and WRA increased the relative content of soil hydrophilic functional groups. Compared with the control (CK), the combined application of biochar and WRA increased the field capacity, reduced soil water volatilization under drought conditions, and slowed water infiltration into the Fluvisols. The soil WRC was higher with the wheat straw biochar (WBC) treatment than with the maize straw biochar (MBC) treatment. It was also higher with polyacrylamide treatment than with the starch-grafted sodium acrylate treatment. The combined application of biochar and WRA improved crop drought physiological resistance by significantly increasing the maize seedling potassium (K) and soluble sugar contents, increasing antioxidant enzyme activity, and reducing the malondialdehyde (MDA) content. The results indicate that the application of biochar and WRA alleviated drought stress by increasing the soil WRC and improving crop drought resistance in Fluvisols.

Future climate change impacts on wheat grain yield and protein in the North China Region

The threat of global climate change on wheat production may be underestimated by the limited capacity of many crop models to predict grain quality and protein composition. This study aimed to integrate a wheat quality module of protein components into the CROPSIM-CERES-Wheat model to investigate the impact of climate change on wheat grain yield and protein quality in the North China Region (NCR) using five Global Climate Models (GCMs) from CMIP6 under three shared socioeconomic pathways. The CERES-Wheat model with a quality module was developed and calibrated and validated using data from several sites in the NCR. The results of the calibration and validation showed that the modified CERES-Wheat model can accurately predict grain yield, protein content and its components in field experiments. Compared with the baseline period (1981-2010), the annual mean temperature and annual cumulative precipitation increased in the NCR in the 2030's, 2050's and 2080's. The radiation was higher under the SSP126 and SSP585 scenarios, and lower under the SSP370 scenario compared to the baseline period. The anthesis and maturity date occurred earlier under the three future scenarios. The average grain yield increased by 13.3-30.9 % under three future scenarios. However, the regional average grain protein content of winter wheat in the future decreased by 2.0 %- 3.5 %. The reduction in wheat grain protein at the regional was less pronounced under SSP370 than that under SSP126 and SSP585. The structural protein content of winter wheat decreased under future climate conditions compared with the baseline period, but the storage protein content showed the opposite tendency. The model provided a useful tool to study the effects of future climate on grain quality and protein composition. These findings are important for developing agricultural practices and strategies to mitigate the potential impacts of climate change on wheat production and wheat quality in the future.

Early Identification of Crop Type for Smallholder Farming Systems Using Deep Learning on Time-Series Sentinel-2 Imagery

Climate change and the COVID-19 pandemic have disrupted the food supply chain across the globe and adversely affected food security. Early estimation of staple crops can assist relevant government agencies to take timely actions for ensuring food security. Reliable crop type maps can play an essential role in monitoring crops, estimating yields, and maintaining smooth food supplies. However, these maps are not available for developing countries until crops have matured and are about to be harvested. The use of remote sensing for accurate crop-type mapping in the first few weeks of sowing remains challenging. Smallholder farming systems and diverse crop types further complicate the challenge. For this study, a ground-based survey is carried out to map fields by recording the coordinates and planted crops in respective fields. The time-series images of the mapped fields are acquired from the Sentinel-2 satellite. A deep learning-based long short-term memory network is used for the accurate mapping of crops at an early growth stage. Results show that staple crops, including rice, wheat, and sugarcane, are classified with 93.77% accuracy as early as the first four weeks of sowing. The proposed method can be applied on a large scale to effectively map crop types for smallholder farms at an early stage, allowing the authorities to plan a seamless availability of food.

Multi-trait selection for mean performance and stability of maize hybrids in mega-environments delineated using envirotyping techniques

Under global climate changes, understanding climate variables that are most associated with environmental kinships can contribute to improving the success of hybrid selection, mainly in environments with high climate variations. The main goal of this study is to integrate envirotyping techniques and multi-trait selection for mean performance and the stability of maize genotypes growing in the Huanghuaihai plain in China. A panel of 26 maize hybrids growing in 10 locations in two crop seasons was evaluated for 9 traits. Considering 20 years of climate information and 19 environmental covariables, we identified four mega-environments (ME) in the Huanghuaihai plain which grouped locations that share similar long-term weather patterns. All the studied traits were significantly affected by the genotype × mega-environment × year interaction, suggesting that evaluating maize stability using single-year, multi-environment trials may provide misleading recommendations. Counterintuitively, the highest yields were not observed in the locations with higher accumulated rainfall, leading to the hypothesis that lower vapor pressure deficit, minimum temperatures, and high relative humidity are climate variables that -under no water restriction- reduce plant transpiration and consequently the yield. Utilizing the multi-trait mean performance and stability index (MTMPS) prominent hybrids with satisfactory mean performance and stability across cultivation years were identified. G23 and G25 were selected within three out of the four mega-environments, being considered the most stable and widely adapted hybrids from the panel. The G5 showed satisfactory yield and stability across contrasting years in the drier, warmer, and with higher vapor pressure deficit mega-environment, which included locations in the Hubei province. Overall, this study opens the door to a more systematic and dynamic characterization of the environment to better understand the genotype-by-environment interaction in multi-environment trials.

Identification of Functional Genetic Variations Underlying Flooding Tolerance in Brazilian Soybean Genotypes

Flooding is a frequent environmental stress that reduces soybean (Glycine max) growth and grain yield in many producing areas in the world, such as, e.g., in the United States, Southeast Asia and Southern Brazil. In these regions, soybean is frequently cultivated in lowland areas by rotating with rice (Oryza sativa), which provides numerous technical, economic and environmental benefits. Given these realities, this work aimed to characterize physiological responses, identify genes differentially expressed under flooding stress in Brazilian soybean genotypes with contrasting flooding tolerance, and select SNPs with potential use for marker-assisted selection. Soybean cultivars TECIRGA 6070 (flooding tolerant) and FUNDACEP 62 (flooding sensitive) were grown up to the V6 growth stage and then flooding stress was imposed. Total RNA was extracted from leaves 24 h after the stress was imposed and sequenced. In total, 421 induced and 291 repressed genes were identified in both genotypes. TECIRGA 6070 presented 284 and 460 genes up- and down-regulated, respectively, under flooding conditions. Of those, 100 and 148 genes were exclusively up- and down-regulated, respectively, in the tolerant genotype. Based on the RNA sequencing data, SNPs in differentially expressed genes in response to flooding stress were identified. Finally, 38 SNPs, located in genes with functional annotation for response to abiotic stresses, were found in TECIRGA 6070 and absent in FUNDACEP 62. To validate them, 22 SNPs were selected for designing KASP assays that were used to genotype a panel of 11 contrasting genotypes with known phenotypes. In addition, the phenotypic and grain yield impacts were analyzed in four field experiments using a panel of 166 Brazilian soybean genotypes. Five SNPs possibly related to flooding tolerance in Brazilian soybean genotypes were identified. The information generated from this research will be useful to develop soybean genotypes adapted to poorly drained soils or areas subject to flooding.

Assessment and Prediction of Grain Production Considering Climate Change and Air Pollution in China

This study examines the spatial and temporal impacts of climate change on grain production in China. This is achieved by establishing a spatial error model consisting of four indicators: the climate, air pollution, economic behavior, and agricultural technology, covering 31 provinces in China from 2004 to 2020. These indicators are used to validate the spatial impacts of climate change on grain production. Air pollution data are used as instrumental variables to address the causality between climate and grain production. The regression results show that: First, climatic variables all have a non-linear “increasing then decreasing” effect on food production. Second, SO2, PM10, and PM2.5 have a negative impact on grain production. Based on the model, changes in the climatic production potential of grain crops can be calculated, and the future spatial layout of climate production can also be predicted by using random forests. Studies have shown that the median value of China’s grain production potential is decreasing, and the low value is increasing.

Livelihood Resilience of Rural Residents under Natural Disasters in China

The impact of natural disasters on rural areas has recently increased, the question of how to effectively improve the livelihood resilience of rural residents has therefore become an essential issue. In this context, a livelihood resilience evaluation index system for rural residents was constructed in four dimensions: livelihood quality, livelihood promotion, livelihood provision, and disaster stress. A structural dynamics model was used to analyze the changing characteristics of the livelihood resilience of rural residents in China between the years 1980 and 2020, and a livelihood resilience trend from 2021 to 2030 was predicted based on the ARIMA model. The main factors influencing livelihood resilience were explored using ridge regression analysis. The results show that: (1) livelihood resilience of rural residents in China fluctuated significantly between 1980 and 2020, tending generally to increase; (2) livelihood resilience is positively correlated with livelihood quality, livelihood promotion, and livelihood provision, while it is negatively correlated with disaster stress; (3) livelihood quality, livelihood promotion, and livelihood supply will still increase between the years 2021 and 2030, while the increase of livelihood resilience will tend to slow down; and (4) six variables have a significant positive impact on livelihood resilience, and provide a basis for the subsequent enhancement of livelihood resilience.

Impact of Climate Change on Dryland Agricultural Systems: A Review of Current Status, Potentials, and Further Work Need

Dryland agricultural system is under threat due to climate extremes and unsustainable management. Understanding of climate change impact is important to design adaptation options for dry land agricultural systems. Thus, the present review was conducted with the objectives to identify gaps and suggest technology-based intervention that can support dry land farming under changing climate. Careful management of the available agricultural resources in the region is a current need, as it will play crucial role in the coming decades to ensure food security, reduce poverty, hunger, and malnutrition. Technology based regional collaborative interventions among Universities, Institutions, Growers, Companies etc. for water conservation, supplemental irrigation, foliar sprays, integrated nutrient management, resilient crops-based cropping systems, artificial intelligence, and precision agriculture (modeling and remote sensing) are needed to support agriculture of the region. Different process-based models have been used in different regions around the world to quantify the impacts of climate change at field, regional, and national scales to design management options for dryland cropping systems. Modeling include water and nutrient management, ideotype designing, modification in tillage practices, application of cover crops, insect, and disease management. However, diversification in the mixed and integrated crop and livestock farming system is needed to have profitable, sustainable business. The main focus in this work is to recommend different agro-adaptation measures to be part of policies for sustainable agricultural production systems in future.

Soybean Production and Spatial Agglomeration in China from 1949 to 2019

By mastering the spatial-temporal evolution of patterns of soybean production, a reference for optimizing a soybean production layout could be provided, ensuring food security. The variation coefficient method, and the comparative advantage and spatial autocorrelation models were used to analyze the spatial divergence regularities of soybean production, sown area and yield, spatial-temporal changes in the comparative advantages of soybean planting efficiency and soybean planting scale, and the spatial agglomeration characteristics in China from 1949 to 2019. The results indicate that (1) from 1949 to 2019, soybean production and yield changes in China remained constant with a fluctuating upwards trend, and soybean sown areas hardly changed, yet experienced a sharp fluctuation. (2) The Northeast China Plain (NECP) was the main soybean-producing area, and its main position was strengthened. In contrast, the main soybean production position of the Huang-Huai-Hai Plain (HHHP) has declined. The Northern arid and semiarid region (NASR), the Sichuan Basin and surrounding areas (SBSR), the Middle-Lower Yangtze Plain (MLYP), and the Yunnan-Guizhou Plateau (YGP) became new soybean production growth poles. (3) The spatial distribution of soybean planting efficiency-related comparative advantages in China extended from northern China to the whole country, and the soybean planting scale-related comparative advantages proceeded through three stages: steady expansion, relative stability, contraction, and stabilization. (4) The spatial agglomeration of soybean planting efficiency-related comparative advantages has weakened, and the spatial agglomeration of the soybean planting scale-related comparative advantages exhibited a strengthening-weakening-strengthening-weakening process. Through our research analysis, we propose a policy resource to fully utilize the soybean planting efficiency-related comparative advantages in southern China (SC), promote grain-soybean rotation patterns in the HHHP and NECP, improve the soybean cultivation subsidy system, and build a soybean industry chain in the NECP.

Improving Spatial Disaggregation of Crop Yield by Incorporating Machine Learning with Multisource Data: A Case Study of Chinese Maize Yield

Spatially explicit crop yield datasets with continuous long-term series are essential for understanding the spatiotemporal variation of crop yield and the impact of climate change on it. There are several spatial disaggregation methods to generate gridded yield maps, but these either use an oversimplified approach with only a couple of ancillary data or an overly complex approach with limited flexibility and scalability. This study developed a spatial disaggregation method using improved spatial weights generated from machine learning. When applied to Chinese maize yield, extreme gradient boosting (XGB) derived the best prediction results, with a cross-validation coefficient of determination (R2) of 0.81 at the municipal level. The disaggregated yield at 1 km grids could explain 54% of the variance of the county-level statistical yield, which is superior to the existing gridded maize yield dataset in China. At the site level, the disaggregated yields also showed much better agreement with observations than the existing gridded maize yield dataset. This lightweight method is promising for generating spatially explicit crop yield datasets with finer resolution and higher accuracy, and for providing necessary information for maize production risk assessment in China under climate change.

Physiology of Plant Responses to Water Stress and Related Genes: A Review

Drought and waterlogging seriously affect the growth of plants and are considered severe constraints on agricultural and forestry productivity; their frequency and degree have increased over time due to global climate change. The morphology, photosynthetic activity, antioxidant enzyme system and hormone levels of plants could change in response to water stress. The mechanisms of these changes are introduced in this review, along with research on key transcription factors and genes. Both drought and waterlogging stress similarly impact leaf morphology (such as wilting and crimping) and inhibit photosynthesis. The former affects the absorption and transportation mechanisms of plants, and the lack of water and nutrients inhibits the formation of chlorophyll, which leads to reduced photosynthetic capacity. Constitutive overexpression of 9-cis-epoxydioxygenase (NCED) and acetaldehyde dehydrogenase (ALDH), key enzymes in abscisic acid (ABA) biosynthesis, increases drought resistance. The latter forces leaf stomata to close in response to chemical signals, which are produced by the roots and transferred aboveground, affecting the absorption capacity of CO2, and reducing photosynthetic substrates. The root system produces adventitious roots and forms aerenchymal to adapt the stresses. Ethylene (ETH) is the main response hormone of plants to waterlogging stress, and is a member of the ERFVII subfamily, which includes response factors involved in hypoxia-induced gene expression, and responds to energy expenditure through anaerobic respiration. There are two potential adaptation mechanisms of plants (“static” or “escape”) through ETH-mediated gibberellin (GA) dynamic equilibrium to waterlogging stress in the present studies. Plant signal transduction pathways, after receiving stress stimulus signals as well as the regulatory mechanism of the subsequent synthesis of pyruvate decarboxylase (PDC) and alcohol dehydrogenase (ADH) enzymes to produce ethanol under a hypoxic environment caused by waterlogging, should be considered. This review provides a theoretical basis for plants to improve water stress tolerance and water-resistant breeding.

A Dual Attention Convolutional Neural Network for Crop Classification Using Time-Series Sentinel-2 Imagery

Accurate and timely mapping of crop types and having reliable information about the cultivation pattern/area play a key role in various applications, including food security and sustainable agriculture management. Remote sensing (RS) has extensively been employed for crop type classification. However, accurate mapping of crop types and extents is still a challenge, especially using traditional machine learning methods. Therefore, in this study, a novel framework based on a deep convolutional neural network (CNN) and a dual attention module (DAM) and using Sentinel-2 time-series datasets was proposed to classify crops. A new DAM was implemented to extract informative deep features by taking advantage of both spectral and spatial characteristics of Sentinel-2 datasets. The spectral and spatial attention modules (AMs) were respectively applied to investigate the behavior of crops during the growing season and their neighborhood properties (e.g., textural characteristics and spatial relation to surrounding crops). The proposed network contained two streams: (1) convolution blocks for deep feature extraction and (2) several DAMs, which were employed after each convolution block. The first stream included three multi-scale residual convolution blocks, where the spectral attention blocks were mainly applied to extract deep spectral features. The second stream was built using four multi-scale convolution blocks with a spatial AM. In this study, over 200,000 samples from six different crop types (i.e., alfalfa, broad bean, wheat, barley, canola, and garden) and three non-crop classes (i.e., built-up, barren, and water) were collected to train and validate the proposed framework. The results demonstrated that the proposed method achieved high overall accuracy and a Kappa coefficient of 98.54% and 0.981, respectively. It also outperformed other state-of-the-art classification methods, including RF, XGBOOST, R-CNN, 2D-CNN, 3D-CNN, and CBAM, indicating its high potential to discriminate different crop types.

A Satellite-Based Method for National Winter Wheat Yield Estimating in China

Satellite-based models have tremendous potential for monitoring crop production because satellite data can provide temporally and spatially continuous crop growth information at large scale. This study used a satellite-based vegetation production model (i.e., eddy covariance light use efficiency, EC-LUE) to estimate national winter wheat gross primary production, and then combined this model with the harvest index (ratio of aboveground biomass to yield) to convert the estimated winter wheat production to yield. Specifically, considering the spatial differences of the harvest index, we used a cross-validation method to invert the harvest index of winter wheat among counties, municipalities and provinces. Using the field-surveyed and statistical yield data, we evaluated the model performance, and found the model could explain more than 50% of the spatial variations of the yield both in field-surveyed regions and most administrative units. Overall, the mean absolute percentage errors of the yield are less than 20% in most counties, municipalities and provinces, and the mean absolute percentage errors for the production of winter wheat at the national scale is 4.06%. This study demonstrates that a satellite-based model is an alternative method for crop yield estimation on a larger scale.

Long-term partial replacement of mineral fertilizer with in situ crop residues ensures continued rice yields and soil fertility: A case study of a 27-year field experiment in subtropical China

High yields and environment-related issues because of over-fertilization in rice (Oryza sativa L.) production is a major concern in China. Partial replacement of mineral fertilizer (MF) with organic matter is considered a win-win approach for resource-saving and environmentally friendly rice production. Here, we examined the effects of reduced MF and in situ crop residue on the rice yield and soil fertility in the long term. A 27-year field experiment (a randomized block design with three replicates) in subtropical China was conducted to test the feasibility of the substitution in a double rice paddy ecosystem. The treatments were CT (no fertilizer application considered as control), NPK (mineral fertilizer N, P, and K), and RFC (reduced MF and in situ crop residue to supplement the reduced NPK dose). The crop residue included half of the rice straw and green manure contents, which were retained in situ in the RFC treatment. The RFC maintained the same rice yield and soil fertility levels as NPK. In general, soil organic carbon (SOC) and total nitrogen (TN) content in RFC increased by 10.3% -20.8%, and 7.5% -28.0%, respectively, than that in NPK from the 5th to the 25th years. There was no significant difference in the content and net accumulation of SOC, TN, and TP and soil available nutrients between the RFC and NPK treatments after 25 years. The average annual yields were 9690 and 9872 kg ha^-1 for the NPK and RFC treatments, respectively. There was no difference in the yield of the first, second, and annual rice crops between RFC and NPK in most years (six of the fifty-four seasons showed a significant difference). RFC increased the partial factor productivity (PFP), agronomic efficiency (AE) of MF, and yield stability (CV) (p < 0.05). Positive nutrient balance and a reduced loss of nutrients are evident reasons for achieving better indicators (PFP, AE, and CV) for nutrient compensation and organic nutrient utilization in the RFC treatment. The partial replacement of MF with in situ crop residue retention, is a simple and efficient way to maintain the soil fertility and rice yield as NPK in southern China.

Spatiotemporal variations in damages to cropland from agrometeorological disasters in mainland China during 1978-2018

Drought, flood, hail, low temperature, and frost (LTF) are the main agrometeorological disasters (AMDs) in China; however, comprehensive and quantitative studies on cropland damage induced by AMDs across the whole country in terms of long-term trends are still lacking and urgently needed. Based on historical statistical data from yearbooks and bulletins, the overall characteristics of cropland damage by AMDs during 1978-2018 were analyzed using a pre-whitening procedure and a Mann-Kendall trend test at yearly and provincial scales in China. The results showed that drought was the most severe, with an average covered area of 22.2 million ha and an affected area of 11.2 million ha every year during 1978-2018, followed by flood, hail, and LTF. A decreasing trend was observed in covered area and affected area by drought, flood, and hail, while only LTF showed an increasing trend. On provincial scale, more than 70% of the covered area by AMDs was induced by drought and flood in most provincial districts. In all provincial districts of northern China, more than 50% of the covered area was induced by drought. In most provincial districts of southern China, more than 40% of the covered area was induced by flood. Hail disasters were prominent in Xinjiang, with significant increasing trends among all parameters. Compared with the other three AMDs, LTF covered and affected the smallest cropland area, but significant increasing trends were observed in the northwest and middle parts of China. The results of this study systematically display the characteristics of damage to cropland by four main AMDs, which are critical and necessary for disaster risk reduction and adaptive strategy development.

The fingerprints of climate warming on cereal crops phenology and adaptation options

Growth and development of cereal crops are linked to weather, day length and growing degree-days (GDDs) which make them responsive to the specific environments in specific seasons. Global temperature is rising due to human activities such as burning of fossil fuels and clearance of woodlands for building construction. The rise in temperature disrupts crop growth and development. Disturbance mainly causes a shift in phenological development of crops and affects their economic yield. Scientists and farmers adapt to these phenological shifts, in part, by changing sowing time and cultivar shifts which may increase or decrease crop growth duration. Nonetheless, climate warming is a global phenomenon and cannot be avoided. In this scenario, food security can be ensured by improving cereal production through agronomic management, breeding of climate-adapted genotypes and increasing genetic biodiversity. In this review, climate warming, its impact and consequences are discussed with reference to their influences on phenological shifts. Furthermore, how different cereal crops adapt to climate warming by regulating their phenological development is elaborated. Based on the above mentioned discussion, different management strategies to cope with climate warming are suggested.