Possibilities of using digital technologies in agriculture in areas with high agrarian fragmentation

IF 5.4 2区 农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY
Paulina Kramarz, Henryk Runowski
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Abstract

The Małopolskie and Podkarpackie provinces in Poland are characterized by many small farms with many small, scattered fields. This farm structure is labeled “agrarian fragmentation”. Using digital technologies in such small farm areas is usually a challenge. However, there are several digital technologies that, with minimal financial investment, can yield results in the form of improved resource management and agricultural production processes, as well as data-driven decision-making. The overall objective of this analysis is to determine the limitations of using digital technologies in farms operating in areas with high agrarian fragmentation. In addition, the aim was also to identify the differences in the potential for implementing individual digital solutions depending on farm size and activity type conducted in the surveyed area. A survey was conducted by the Paper and Pen Personal Interview (PAPI) method, in which 389 farmers took part. Research showed that the technologies most commonly used in the study area include applications recognizing plant diseases and applications supporting decision-making. The use of advanced digital tools related to precision agriculture and the automation of crop production was very rare. Farm size, the age of the farmer managing the farm, and the number of farm activities were significant factors that increased the probability of implementing digital technologies. The main barriers to their implementation were a lack of sufficient knowledge and trust. The implementation of digital technologies in small farms requires actions aimed at increasing farmer knowledge. Meanwhile, designing new digital solutions must take the specific regional conditions into account, such as geographical factors or the limited investment capacity of farms.

在土地高度碎片化的地区,在农业中使用数字技术的可能性
波兰Małopolskie和Podkarpackie省的特点是许多小农场和许多小而分散的田地。这种农场结构被称为“土地碎片化”。在这样的小农场地区使用数字技术通常是一个挑战。然而,有几种数字技术,只需最少的财政投资,就可以产生成果,改善资源管理和农业生产过程,以及数据驱动的决策。本分析的总体目标是确定在农业高度碎片化地区经营的农场使用数字技术的局限性。此外,目的还在于根据调查地区的农场规模和活动类型,确定实施个别数字解决方案的潜力差异。采用纸笔个人访谈法(PAPI)对389名农民进行了调查。研究表明,该研究领域最常用的技术包括植物病害识别应用和决策支持应用。与精准农业和作物生产自动化相关的先进数字工具的使用非常罕见。农场规模、管理农场的农民的年龄和农场活动的数量是增加实施数字技术可能性的重要因素。实施的主要障碍是缺乏足够的知识和信任。在小农场实施数字技术需要采取旨在增加农民知识的行动。同时,设计新的数字解决方案必须考虑到具体的区域条件,如地理因素或农场有限的投资能力。
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来源期刊
Precision Agriculture
Precision Agriculture 农林科学-农业综合
CiteScore
12.30
自引率
8.10%
发文量
103
审稿时长
>24 weeks
期刊介绍: Precision Agriculture promotes the most innovative results coming from the research in the field of precision agriculture. It provides an effective forum for disseminating original and fundamental research and experience in the rapidly advancing area of precision farming. There are many topics in the field of precision agriculture; therefore, the topics that are addressed include, but are not limited to: Natural Resources Variability: Soil and landscape variability, digital elevation models, soil mapping, geostatistics, geographic information systems, microclimate, weather forecasting, remote sensing, management units, scale, etc. Managing Variability: Sampling techniques, site-specific nutrient and crop protection chemical recommendation, crop quality, tillage, seed density, seed variety, yield mapping, remote sensing, record keeping systems, data interpretation and use, crops (corn, wheat, sugar beets, potatoes, peanut, cotton, vegetables, etc.), management scale, etc. Engineering Technology: Computers, positioning systems, DGPS, machinery, tillage, planting, nutrient and crop protection implements, manure, irrigation, fertigation, yield monitor and mapping, soil physical and chemical characteristic sensors, weed/pest mapping, etc. Profitability: MEY, net returns, BMPs, optimum recommendations, crop quality, technology cost, sustainability, social impacts, marketing, cooperatives, farm scale, crop type, etc. Environment: Nutrient, crop protection chemicals, sediments, leaching, runoff, practices, field, watershed, on/off farm, artificial drainage, ground water, surface water, etc. Technology Transfer: Skill needs, education, training, outreach, methods, surveys, agri-business, producers, distance education, Internet, simulations models, decision support systems, expert systems, on-farm experimentation, partnerships, quality of rural life, etc.
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