Subjects: Agriculture, Forestry,Livestock & Aquatic Products Science >> Agronomy submitted time 2023-07-18
Yuan, Nuo
Liu, Yumin
Zhang, Jibo
Dong, Nan
Li, Xiaojing
Jiang, Xilong
Zhao, Qiang
Zhu, Yinghua
Gao, Xue
Zha, Hainie
Xia, Haiyong
Abstract： ChatGPT, Wenxin Yiyan, Tongyi Qianwen, Pangu NLP, Hunyuan, Ririxin, Sequence Monkey, etc. are natural language processing models based on artificial neural networks. They can engage in dialogue with humans, answer questions, and help people obtain information, knowledge, inspiration, and complete creative tasks such as writing emails, creating video scripts, copywriting, translation, writing codes, and producing papers more efficiently and conveniently. Recently, they have been gradually applied to various industries. This paper aims to explore the application prospects of CHatGPT-like artificial intelligence products in agricultural production and research, as well as analyzing the possible impacts and problems. The potential applications of ChatGPT and other language models in agricultural production mainly include agrometeorological forecasting, recommendation of crop varieties and management measures, management of livestock and aquaculture, identification of crop diseases, pests, weeds, agrochemical (fertilizers, pesticides, herbicides, fungicides) damages and nutrient deficiency symptoms, as well as seedling diagnosis, agricultural products and food quality detection, construction of agricultural knowledge map and intelligent agricultural question answering system, etc. They can provide decision support for agricultural technicians and farmers, help them complete agricultural operations more precisely, and improve the level of intelligence, scientific and technological content and production efficiency. Their applications in agricultural research mainly include literature retrieval and review, data mining and analysis, research report and paper generation, agricultural standards and main promotion technology formulation, book compilation, as well as transformation and popularization of scientific and technological achievements. The application of ChatGPT-like artificial intelligence can greatly improve the efficiency of agricultural production, make agricultural research more accurate, efficient and convenient, and bring valuable opportunities for agricultural development. However, their applications have also brought some unprecedented challenges, for example, some agricultural technology extension personnel, agricultural science popularization workers may be replaced, and farmers with poor learning ability will be eliminated. Agricultural producers and researchers must learn how to apply ChatGPT-like artificial intelligence language models, and constantly improve the level of application skills to improve work and production efficiency, which has higher requirements for the knowledge level, learning and innovation ability of agricultural producers and researchers. In addition, ChatGPT-like language models also have some self-problems and shortcomings, such as user information leakage, data and intellectual property security risks, factual errors, poor real-time, lack of stability and repeatability, etc. The development of this emerging technology is triggering a new round of artificial intelligence ethical disputes. In short, the application of ChatGPT-like artificial intelligence language models in agricultural production and scientific research will have a positive impact on agricultural development, and implant "smart chips" and install "smart brains" for modern agriculture. Meanwhile, there are some uncertain and unpredictable consequences, which need to be discussed in practice.
Peer Review Status:
Awaiting Review
Subjects: Agriculture, Forestry,Livestock & Aquatic Products Science >> Agronomy submitted time 2023-02-16 Cooperative journals: 《干旱区科学》
Abstract：Since Pb is a non-biodegradable inorganic pollutant and a non-essential metal, its long-term presence in soil poses a great threat to the environment. Iris lactea Pall. var. chinensis (Fisch.) Koidz., a perennial dense bush herb with high resistance of Pb and wide adaptability, was used in pot experiments to study the effects of exogenous nitrate N (NO3-N) on the absorption and transportation of Pb and plant growth under different Pb concentrations. Then, the mechanism of NO3-N affecting Pb and nutrient uptake and transport was explored. The concentration of Pb in the experiment ranged from 0 to 1600 mg/kg, and the added concentration of NO3-N was 0.00.3 g/kg. The results showed that I. lactea was highly tolerant to Pb, and the shoot fraction was more sensitive to varied Pb concentrations in the soil than the root fraction. This protective function became more pronounced under the condition of raised Pb concentration in the soil. When the concentration of Pb in the soil reached 800 mg/kg, the highest Pb content of I. lactea was found under the condition of 0.1 g/kg of NO3-N addition. When Pb concentration in the soil increased to 1600 mg/kg, the increase in NO3-N addition promoted Pb uptake by the root. To ensure the well growth of I. lactea and the effect of remediation of Pb-contaminated soil, the recommended concentration of NO3-N in the soil is 0.1 g/kg. This result provides a theoretical basis for exogenous N regulation of phytoremediation of Pb-contaminated soil.
Subjects: Agriculture, Forestry,Livestock & Aquatic Products Science >> Agronomy submitted time 2022-05-08
Abstract：
Planting cover crop has been suggested as a way of increasing soil organic carbon in agricultural land. Ryegrass (Lolium multiflorum L.), as a cover crop, could improve soil fertility and lower soil CO2 emission. However, effects of soil water content and nitrogen on soil carbon mineralization after ryegrass incorporation are not fully understood. The present study was to investigate the effect of soil water content and nitrogen rate on soil carbon mineralization after ryegrass incorporated into upland red soil (Ferralsols). A laboratory experiment was established, including soil water contents [15% (W1), 30% (W2), 45% (W3)] and nitrogen rates [0 (N1), 60 mg/kg(N2), 120 mg/kg(N3)]. The results showed that the highest soil carbon mineralization accumulation was observed in W3N3. Nitrogen application inhibited carbon mineralization rate and accumulation in the late stage of ryegrass incorporation at W1, but increased carbon mineralization rate and accumulation at W2. With increasing soil water content, nitrogen application could improve soil carbon mineralization at the early stage of ryegrass incorporation. In conclusion, soil nitrogen and water content could regulate soil carbon mineralization. Considering to reduce the soil CO2 emissions, rational nitrogen application should be taken seriously during cover crop (ryegrass) incorporated into the upland red soil.
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Peer Review Status:Awaiting Review
Subjects: Agriculture, Forestry,Livestock & Aquatic Products Science >> Agronomy submitted time 2018-01-23 Cooperative journals: 《干旱区科学》
Abstract：Limited water resources often result in reduced crop yield and low water productivity (WP). In northwestern China, crop production is generally dependent on precipitation. Therefore, a variety of agricultural rainwater harvesting (ARH) techniques have been used for conserving soil moisture, ameliorating soil environment, increasing crop yield, and improving water use efficiency. A two-year (2013–2015) field experiment was conducted under a typical sub-humid drought-prone climate in Yangling (108°24′E, 34°20′N; 521 m a.s.l.), Shaanxi Province, China, to explore the effects of mulching (same for summer maize and winter wheat) on soil moisture, soil temperature, crop water consumption, and crop yield with a winter wheat/summer maize rotation. Crops were planted in a ridge-furrow pattern and the treatments consisted of a transparent film mulch over the ridges (M1), a crop straw mulch in the furrows (M2), a transparent film mulch over the ridges and a crop straw mulch in the furrows (M3), a black film mulch over the ridges and a crop straw mulch in the furrows (M4), and a control with no mulch (CK). Results showed that M4 was the best treatment for improving soil water storage and content, and decreasing crop water consumption during the summer maize and winter wheat rotation. In both maize and wheat seasons, M1 had a higher soil temperature than M2 and CK, and M3 had a higher soil temperature than M4. In the maize seasons, M4 had the highest yield, WP, and precipitation productivity (PP), with the average values for these parameters increasing by 30.9%, 39.0%, and 31.0%, respectively, compared to those in CK. In the wheat seasons, however, M3 had the highest yield, WP, and PP, with the average values for these parameters being 23.7%, 26.7%, and 23.8% higher, respectively, than those in CK. Annual yield (maize and wheat yields combined) and WP did not differ significantly between M3 and M4. These results suggested that M3 and M4 may thus be the optimal ARH practices for the production of winter wheat and summer maize, respectively, in arid and semi-arid areas.
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