{"title":"Possibilities of using digital technologies in agriculture in areas with high agrarian fragmentation","authors":"Paulina Kramarz, Henryk Runowski","doi":"10.1007/s11119-025-10244-2","DOIUrl":null,"url":null,"abstract":"<p>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.</p>","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"24 1","pages":""},"PeriodicalIF":5.4000,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Precision Agriculture","FirstCategoryId":"97","ListUrlMain":"https://doi.org/10.1007/s11119-025-10244-2","RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AGRICULTURE, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
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.
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.