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Research on the estimation of wheat AGB at the entire growth stage based on improved convolutional features

Tao Liu, Jianliang Wang, Jiayi Wang, Yuanyuan Zhao, Hui Wang, Weijun Zhang, Zhaosheng Yao, Shengping Liu, Xiaochun Zhong, Chengming Sun

2025, 24 (4): 1403-1423. DOI: 10.1016/j.jia.2024.07.015

Abstract （69） PDF in ScienceDirect

The wheat above-ground biomass (AGB) is an important index that shows the life activity of vegetation, which is of great significance for wheat growth monitoring and yield prediction. Traditional biomass estimation methods specifically include sample surveys and harvesting statistics. Although these methods have high estimation accuracy, they are time-consuming, destructive, and difficult to implement to monitor the biomass at a large scale. The main objective of this study is to optimize the traditional remote sensing methods to estimate the wheat AGB based on improved convolutional features (CFs). Low-cost unmanned aerial vehicles (UAV) were used as the main data acquisition equipment. This study acquired RGB and multi-spectral (MS) image data of the wheat population canopy for two wheat varieties and five key growth stages. Then, field measurements were conducted to obtain the actual wheat biomass data for validation. Based on the remote sensing indices (RSIs), structural features (SFs), and convolutional features (CFs), this study proposed a new feature named AUR-50 (Multi-source combination based on convolutional feature optimization) to estimate the wheat AGB. The results show that AUR-50 could more accurately estimate the wheat AGB than RSIs and SFs, and the average R² exceeded 0.77. AUR-50_MShad the highest estimation accuracy (R² of 0.88) in the overwintering period. In addition, AUR-50 reduced the effect of the vegetation index saturation on the biomass estimation accuracy by adding CFs, where the highest R² was 0.69 at the flowering stage. The results of this study provide an effective method to evaluate the AGB in wheat with high throughput and a research reference for the phenotypic parameters of other crops.

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Optimizing crop yields while minimizing environmental impact through deep placement of nitrogen fertilizer

Lingxiao Zhu, Hongchun Sun, Liantao Liu, Ke Zhang, Yongjiang Zhang, Anchang Li, Zhiying Bai, Guiyan Wang, Xiaoqing Liu, Hezhong Dong, Cundong Li

2025, 24 (1): 36-60. DOI: 10.1016/j.jia.2024.05.012

Abstract （78） PDF in ScienceDirect

Nitrogen (N) serves as an essential nutrient for yield formation across diverse crop types. However, agricultural production encounters numerous challenges, notably high N fertilizer rates coupled with low N use efficiency and serious environmental pollution. Deep placement of nitrogen fertilizer (DPNF) is an agronomic measure that shows promise in addressing these issues. This review aims to offer a comprehensive understanding of DPNF, beginning with a succinct overview of its development and methodologies for implementation. Subsequently, the optimal fertilization depth and influencing factors for different crops are analyzed and discussed. Additionally, it investigates the regulation and mechanism underlying the DPNF on crop development, yield, N use efficiency and greenhouse gas emissions. Finally, the review delineates the limitations and challenges of this technology and provides suggestions for its improvement and application. This review provides valuable insight and reference for the promotion and adoption of DPNF in agricultural practice.

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Strategies for improving crop comprehensive benefits via a decision-making system based on machine learning in the rice‒rape, rice‒wheat and rice‒garlic rotation systems in Southwest China

Xinrui Li, Xiafei Li, Tao Liu, Huilai Yin, Hao Fu, Yongheng Luo, Yanfu Bai, Hongkun Yang, Zhiyuan Yang, Yongjian Sun, Jun Ma, Zongkui Chen

2024, 23 (9): 2970-2988. DOI: 10.1016/j.jia.2023.10.005

Abstract （157） PDF in ScienceDirect

Rice‒rape, rice‒wheat and rice‒garlic rotations are common cropping systems in Southwest China, and they have played a significant role in ensuring ecological and economic benefits (EB) and addressing the challenges of China’s food security in the region. However, the crop yields in these rotation systems are 1.25‒14.73% lower in this region than the national averages. Intelligent decision-making with machine learning can analyze the key factors for obtaining better benefits, but it has rarely been used to enhance the probability of obtaining such benefits from rotations in Southwest China. Thus, we used a data-intensive approach to construct an intelligent decision‒making system with machine learning to provide strategies for improving the benefits of rice–rape, rice–wheat, and rice–garlic rotations in Southwest China. The results show that raising the yield and partial fertilizer productivity (PFP) by increasing seed input under high fertilizer application provided the optimal benefits with a 10% probability in the rice–garlic system. Obtaining high yields and greenhouse gas (GHG) emissions by increasing the N application and reducing the K application provided suboptimal benefits with an 8% probability in the rice–rape system. Reducing N and P to enhance PFP and yield provided optimal benefits with the lowest probability (8%) in the rice‒wheat system. Based on the predictive analysis of a random forest model, the optimal benefits were obtained with fertilization regimes by reducing N by 25% and increasing P and K by 8 and 74%, respectively, in the rice–garlic system, reducing N and K by 54 and by 36%, respectively, and increasing P by 38% in rice–rape system, and reducing N by 4% and increasing P and K by 65 and 23% in rice–wheat system. These strategies could be further optimized by 17‒34% for different benefits, and all of these measures can improve the effectiveness of the crop rotation systems to varying degrees. Overall, these findings provide insights into optimal agricultural inputs for higher benefits through an intelligent decision-making system with machine learning analysis in the rice–rape, rice‒wheat, and rice–garlic systems.

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Increasing root-lower characteristics improves drought tolerance in cotton cultivars at the seedling stage

Congcong Guo, Hongchun Sun, Xiaoyuan Bao, Lingxiao Zhu, Yongjiang Zhang, Ke Zhang, Anchang Li, Zhiying Bai, Liantao Liu, Cundong Li

2024, 23 (7): 2242-2254. DOI: 10.1016/j.jia.2023.07.013

Abstract （123） PDF in ScienceDirect

Drought is an important abiotic stress factor in cotton production. The root system architecture (RSA) of cotton shows high plasticity which can alleviate drought-related stress under drought stress (DS) conditions; however, this alleviation is cultivar dependent. Therefore, this study estimated the genetic variability of RSA in cotton under DS. Using the paper-based growth system, we assessed the RSA variability in 80 cotton cultivars at the seedling stage, with 0 and 10% polyethylene glycol 6000 (PEG6000) as the control (CK) and DS treatment, respectively. An analysis of 23 above-ground and root traits in the 80 cotton cultivars revealed different responses to DS. On the 10th day after DS treatment, the degree of variation in the RSA traits under DS (5–55%) was greater than that of CK (5–49%). The 80 cultivars were divided into drought-tolerant cultivars (group 1), intermediate drought-tolerant cultivars (group 2), and drought-sensitive cultivars (group 3) based on their comprehensive evaluation values of drought resistance. Under DS, the root length-lower, root area-lower, root volume-lower, and root length density-lower were significantly reduced by 63, 71, 76, and 4% in the drought-sensitive cultivars compared to CK. Notably, the drought-tolerant cultivars maintained their root length-lower, root area-lower, root volume-lower, and root length density–lower attributes. Compared to CK, the root diameter (0–2 mm)-lower increased by 21% in group 1 but decreased by 3 and 64% in groups 2 and 3, respectively, under DS. Additionally, the drought-tolerant cultivars displayed a plastic response under DS that was characterized by an increase in the root-lower characteristics. Drought resistance was positively correlated with the root area-lower and root length density-lower. Overall, the RSA of the different cotton cultivars varied greatly under DS. Therefore, important root traits, such as the root-lower traits, provide great insights for exploring whether drought-tolerant cotton cultivars can effectively withstand adverse environments.

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Trends in the global commercialization of genetically modified crops in 2023

Xingru Cheng, Haohui Li, Qiaoling Tang, Haiwen Zhang, Tao Liu, Youhua Wang

2024, 23 (12): 3943-3952. DOI: 10.1016/j.jia.2024.09.012

Abstract （479） PDF in ScienceDirect

The commercialization of genetically modified (GM) crops has increased food production, improved crop quality, reduced pesticide use, promoted changes in agricultural production methods, and become an important new production strategy for dealing with insect pests and weeds while reducing the cultivated land area. This article provides a comprehensive examination of the global distribution of GM crops in 2023. It discusses the internal factors that are driving their adoption, such as the increasing number of GM crops and the growing variety of commodities. This article also provides information support and application guidance for the new developments in global agricultural science and technology.

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Exogenous melatonin improves cotton yield under drought stress by enhancing root development and reducing root damage

Lingxiao Zhu, Hongchun Sun, Ranran Wang, Congcong Guo, Liantao Liu, Yongjiang Zhang, Ke Zhang, Zhiying Bai, Anchang Li, Jiehua Zhu, Cundong Li

2024, 23 (10): 3387-3405. DOI: 10.1016/j.jia.2024.04.011

Abstract （120） PDF in ScienceDirect

The exogenous application of melatonin by the root drenching method is an effective way to improve crop drought resistance. However, the optimal concentration of melatonin by root drenching and the physiological mechanisms underlying melatonin-induced drought tolerance in cotton (Gossypium hirsutum L.) roots remain elusive. This study determined the optimal concentration of melatonin by root drenching and explored the protective effects of melatonin on cotton roots. The results showed that 50 μmol L^–1 melatonin was optimal and significantly mitigated the inhibitory effect of drought on cotton seedling growth. Exogenous melatonin promoted root development in drought-stressed cotton plants by remarkably increasing the root length, projected area, surface area, volume, diameter, and biomass. Melatonin also mitigated the drought-weakened photosynthetic capacity of cotton and regulated the endogenous hormone contents by regulating the relative expression levels of hormone-synthesis genes under drought stress. Melatonin-treated cotton seedlings maintained optimal enzymatic and non-enzymatic antioxidant capacities, and produced relatively lower levels of reactive oxygen species and malondialdehyde, thus reducing the drought stress damage to cotton roots (such as mitochondrial damage). Moreover, melatonin alleviated the yield and fiber length declines caused by drought stress. Taken together, these findings show that root drenching with exogenous melatonin increases the cotton yield by enhancing root development and reducing the root damage induced by drought stress. In summary, these results provide a foundation for the application of melatonin in the field by the root drenching method.

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Natural variation in SbTEF1 contributes to salt tolerance in sorghum seedlings

Chang Liu, Lei Tian, Wenbo Yu, Yu Wang, Ziqing Yao, Yue Liu, Luomiao Yang, Chunjuan Liu, Xiaolong Shi, Tao Liu, Bingru Chen, Zhenguo Wang, Haiqiu Yu, Yufei Zhou

DOI: 10.1016/j.jia.2024.03.030 Online: 03 April 2024

Abstract （49） PDF in ScienceDirect

Salt stress is a major constraint to crop productivity and quality. The limited availability of salt-tolerant genes poses significant challenges to breeding programs aimed at enhancing salt tolerance. Sorghum displays a remarkable ability to withstand saline conditions; therefore, elucidating the genetic underpinnings of this trait is crucial. This study entailed a comprehensive resequencing of 186 sorghum accessions to perform a genome-wide association study (GWAS) focusing on relative root length (RL) and root fresh weight (RFW) under salt stress conditions. We identified eight candidate genes within a co-localized region, among which SbTEF1—a gene encoding a transcription elongation factor protein—was deemed a potential candidate due to its annotation and expression pattern alterations under salt stress. Haplotype analysis, gene cloning, linkage disequilibrium (LD) analysis, and allele effect analysis revealed that PAV284, located in the promoter region of SbTEF1, modulated gene expression under salt stress, which, in turn, influenced sorghum seedlings' salt tolerance. PAV284 holds promise as a genetic marker for the selection of salt-tolerant germplasm via marker-assisted breeding, enhancing the development of salt-tolerant sorghum cultivars.

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Phase-specific enhancement of carotenoids and abscisic acid promotes secondary cell wall synthesis by activating key transcription factors and ethylene biosynthesis in cotton fiber

Chuannan Wang, Baitao Liu, Jianyan Zeng, Yaohua Li, Wanting Yu, Qingwei Suo, Lingfang Ran, Long Chen, Yi Wang, Aimin Liang, Jie Kong, Yuehua Xiao

DOI: 10.1016/j.jia.2025.04.006 Online: 07 April 2025

Abstract （11） PDF in ScienceDirect

Cotton (Gossypium) is an important economic crop providing most of the natural fiber for the global textile industry. The secondary cell wall (SCW) comprises the major dry weight of cotton fiber, and is a key determinant of cotton yield and quality. In this study, a fiber-specific promoter, proFbl2A, was employed to control the expression of a fusion gene of phytoene synthase and 1-deoxy-D-xylulose-5-phosphate synthase (GhPSY2D and GhDXS6D, respectively) in cotton fibers of the SCW synthesis stage, resulting in higher carotenoid and abscisic acid (ABA) levels in the transgenic cotton fibers. The SCW synthesis initiated earlier in the ABA-up-regulated cotton fibers than the wild-type control, along with the expression of SCW stage-specific genes and key SCW regulators. Consistently, several positive bZIP transcription factors of ABA signaling (GhbZIP27b, GhbZIP37b, and GhbZIP66b), were found to bind to and activate the promoters of key SCW regulators (GhTCP4A, GhFSN1, and GhMYB7D). Furthermore, these bZIPs could also interact with and promote the expression of two ethylene synthase genes (GhACS10 and GhACO3). Our data demonstrated that enhancement of carotenoid and ABA could advance SCW initiation by activating key transcription factors, and promote SCW thickening via ethylene biosynthesis in cotton fibers.

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