The lack of agricultural big data resources and the failure to share them make queries difficult

In the current global landscape of technological and economic development, data has become a kind of productivity and competitiveness. At present, big data has rapidly developed into a new-generation information technology and service format, and has become a basic strategic resource of the nation. Agriculture and rural areas are one of the important fields for the production and application of big data, and agricultural and rural big data has become a new type of resource element for modern agriculture.

In recent years, agricultural big data has been extremely hot. But compared with other industries, the collection, release and application of agricultural and rural big data still face various difficulties that urgently need to be resolved.

China's agricultural big data has not yet taken shape

Agricultural big data, which covers a wide range of fields, is especially huge and complex, and can be called the biggest big data.

According to the characteristics of agriculture and the division of the entire agricultural industry chain, agricultural big data can be divided into agricultural environment and resources big data, agricultural production big data, agricultural market big data and agricultural management big data, etc. From the perspective of industry, agricultural big data can be divided into different industries such as crop farming, agricultural inputs and breeding, among which it can be further subdivided into different varieties and products.

Li Daoliang, a professor at the College of Information and Electrical Engineering of China Agricultural University, pointed out at the China Big Data Industry Summit held in May this year that agricultural big data mainly comes from four aspects: the Internet of Things, biological information data, resource and environment data, and agricultural statistical data. From the perspective of application, agricultural big data is mainly in five aspects: first, basic research; second, intelligent agricultural production; third, agricultural product market price forecasting and logistics; fourth, agricultural product quality and safety; and fifth, agricultural resource integration, sharing and service platforms.

Li Daoliang told a reporter from China Science Daily that at present China's big data can be generally summarized into two major categories: one is micro-level, mainly from enterprises; the other is macro-level, from government departments.

As the status of big data as a strategic resource becomes increasingly prominent, many enterprises in the agricultural field have successively laid out and deepened their efforts in big data, and some have even transformed because of it. The China president of Monsanto revealed in the first half of this year that Monsanto's strategic direction in recent years has been the application of data science in agriculture. In 2014, Dabeinong Group proposed the "Smart Dabeinong" strategy and launched the "Three Networks and One Communication." It is understood that it has distributed tens of thousands of salespeople nationwide to record pig farm production conditions and collect customer information in order to continuously update data.

But Li Daoliang also said that whether viewed from the government level or the enterprise level, China's agricultural big data at present "has not yet taken shape."

"This is the biggest problem at present." Li Daoliang told the reporter that this is a situation formed over a long period and is very difficult to change in a short time. "This is related to our past neglect of accumulation, and also to our scientific research mechanisms and the working systems of government departments."

In 2013, Zhang Hecheng, then director of the Market and Economic Information Department of the Ministry of Agriculture, wrote an article pointing out that there is still a relatively large gap between China's data collection, release and application and the needs of decision-making. Data collection and release are still in the primary stage and urgently need reform at the institutional level.

The root cause lies in the lack of a complete data system

"Domestic agricultural enterprises are now consciously getting involved in big data, but the enterprises that can also cover the entire industry chain are very few." Li Xia, manager of the animal husbandry industry group of Shandong Zhuochuang Consulting Group, said in an interview with a reporter from China Science Daily.

She introduced that, taking the animal husbandry industry group as an example, doing big data for the entire industry chain means starting from the supply and demand of feed raw materials, then to the breeding and circulation links, and then to the downstream slaughtering and processing links, all closely linked, to achieve citation and corroboration between data. "What many enterprises do in big data is mostly in the fields they are familiar with and good at." Li Xia told the reporter.

In Li Daoliang's view, the "hottest" players in big data at present are enterprises. Building big data platforms can not only provide a basis for decision-making in enterprise production and operation, but also help them grasp the right to speak in data. "Only large enterprises in the industry can truly form and master big data."

At the forum of the China Big Data Industry Summit, Li Daoliang summarized the problems facing China's big data: agricultural big data is lacking, big data models lack long-term accumulation, big data lacks integration with industries, and big data lacks necessary standards.

Li Daoliang told a reporter from China Science Daily that due to reasons such as compartmentalized management, data is not shared among departments, resulting in a lack of agricultural big data. "Now, from the government level, in fact, efforts are being made to break this situation and achieve resource sharing. Only with resource sharing can big data be formed, and only then can big data be analyzed."

Speaking of data accumulation, Li Xia also said, "The workload of data collection is enormous. It requires continuous screening, filtering and updating. Only data formed through long-term accumulation is valuable."

An industry insider who was unwilling to be named told a reporter from China Science Daily that at present, China's market industries, especially the agricultural field, are far less in demand for and use of big data analysis than those abroad. "In the final analysis, what is still needed is a solid, highly accurate and complete data system."

The talent gap urgently needs to be filled

About half a month ago, the Ministry of Agriculture issued the "Second Batch Pilot Plan for Whole-Industry-Chain Agricultural Information Analysis and Early Warning," aiming through pilot projects to establish whole-industry-chain agricultural information analysis and early warning teams and form a working pattern featuring rapid analytical response, comprehensive information content and accurate forecasting and judgment.

The reporter learned that at present the country's personnel in agricultural information collection and analysis "face a very large gap" and are "not professional."

Ming Junren of the School of Management at Wuhan Institute of Technology pointed out that the current agricultural information talent team mainly has the following problems: a serious lack of specialized agricultural information talent, an imbalanced structure of the agricultural information talent team, non-standardized workflows in agricultural information activities, and an unsound compensation management system for agricultural information talent.

Li Xia, however, used the word "fault" to describe the current talent situation in related fields. "There are leading experts and scholars and national-level information early warning analysts in the industry," she explained, "but below that, there is nothing."

In the view of Li Xia, who has been on the front line of agricultural information collection and analysis for many years, doing information collection and analysis should be "down-to-earth," truly understanding the relevant industries and sectors through field investigations. "I believe that in terms of methods, information collection and analysis personnel are certainly familiar with them, but what is more important in doing this work is understanding of the industry and the resources accumulated within the field." Li Xia said.

In addition, Li Xia believes that a good organizational structure and collection process also need to be formed. "Simply put, it is how to collect, when to update, and how to inspect and supervise. All of this requires a whole set of supporting measures."

Ming Junren suggested incorporating the training of agricultural informatization talent into China's higher education disciplinary training system and building a diversified training system for agricultural information talent.

Song Changqing, executive deputy director of the Agricultural Big Data Research Center of Shandong Agricultural University, once wrote an article pointing out that, according to the development of agricultural big data and the application needs of modern agriculture, it is necessary to formulate training plans for agricultural big data technology and application talent and establish multidisciplinary collaborative innovation teams.