UPORABA ANALITIČKOG HIERARHIJSKOG PROCESA U POLJOPRIVREDI

Hierarchical decision models are a general decision support methodology aimed at the classification or evaluation of options that occur in decision-making processes. Decision models are typically developed through the decomposition of complex decision problems into smaller and less complex subproblems. This paper presents an approach to the development and implementation of multicriteria decision model based on Analytical Hierarchy Process – AHP (Expert Choice, EC). Likewise, the AHP is used as a potential multicriteria decision making method for application in agriculture. In order to show the implementation of explained MCDA methods in real situation in agriculture, the application of AHP on a sample model farm is presented in the second part of the article.Hijerarhijski modeli odlučivanja su opće prihvaćena metodologija za klasifikaciju alternative, koje se javljaju u procesu odlučivanja. Modeli su razvijeni postupkom dekompozicije problema u manje kompleksne podprobleme. U ovom radu predstavljamo razvoj i implementaciju višekriterijskog modela, koji bazira na analitičkom hijerarhijskom procesu – AHP (Expert Choice. EC). U tom smislu AHP se javlja kao potencijalni metodološki pristup za potporu donošenju odluka u poljoprivrednom managementu. U radu je demonstrirana aplikacija AHP na realnom problemu odlučivanja u poljoprivredi na primjeru modelnog ekološkog gospodarstva

The Economic Feasibility of Conventional and Organic Farm Production in Slovenia

The aim of the research was the comparison of economic feasibility of most common conventional and organic farm production in Slovenia. The methodology of an integrated deterministic technologic-economic simulation system KARSIM 1.0 (DSM) application for cost analysis and decision-making support on farms is described in this article. The direct simulation model result is an individual conventional or organic farm product enterprise budget. The DSM consists of 148 deterministic production simulation models that enable different types of costs and financial feasibility calculations for conventional and organic production and food processing. The developed simulation model enables economical evaluation of some most important economic parameters (breakeven price, breakeven yield, financial result, total revenue and coefficient of economics). In conventional farming system the most suitable farm product is potato (Ke = 1.52), followed by milk and maize production (Ke = 1.10), wheat production (Ke = 1.06) and suckling cows production (Ke = 1.02). The husked spelt production is in conventional farming system economically infeasible (Ke= 0.82). In organic farming system the most feasible farm product is husked spelt (Ke = 1.56), followed by potato (Ke = 1.15), milk (Ke = 1.04) and suckling cows production (Ke = 1.03). Maize (Ke = 0.90) and wheat production (Ke = 0.83) are economically infeasible

How Artificial Intelligence Improves Agricultural Productivity and Sustainability: A Global Thematic Analysis

Amidst the rising issues of food security and climate change, the agricultural sector has started deploying artificial intelligence (AI) in business operations. While many potential AI benefits are anticipated, a comprehensive understanding of the objectives motivating AI adoption and its impacts is lacking. This research attempts to fill this gap by exploring the key themes related to the use of AI in agriculture through the lens of dynamic capabilities. Using centering resonance analysis, we conduct text mining of news articles from 2014-2019 in the regions of Asia, Africa, Europe, and North America to identify how AI is addressing significant farming challenges. Globally, the results suggest that AI is primarily being applied to increase productivity and efficiency and secondarily to address labor shortages and environmental sustainability concerns. At regional level, the results reflect active AI adoption in North America and Europe with increasing efforts in Asia and Africa

The improvement of strategic crops production via a goal programming model with novel multi-interval weights

Nowadays, the need to increase agricultural production has becomes a challenging task for most of the countries. Generally, there are many resource factors which affect the deterioration of production level, such as low water level, desertification, soil salinity, low on capital, lack of equipment, impact of export and import of crops, lack of fertilizers, pesticide, and the ineffective role of agricultural extension services which are significant in this sector. The main objective of this research is to develop fuzzy goal programming (FGP) model to improve agricultural crop production, leading to increased agricultural benefits (more tons of produce per acre) based on the minimization of the main resources (water, fertilizer and pesticide) to determine the weight in the objectives function subject to different constraints (land area, irrigation, labour, fertilizer, pesticide, equipment and seed). FGP and GP were utilized to solve multi-objective decision making problems (MODM). From the results, this research has successfully presented a new alternative method which introduced multi-interval weights in solving a multi-objective FGP and GP model problem in a fuzzy manner, in the current uncertain decision making environment for the agricultural sector. The significance of this research lies in the fact that some of the farming zones have resource limitations while others adversely impact their environment due to misuse of resources. Finally, the model was used to determine the efficiency of each farming zone over the others in terms of resource utilization

IT as enabler of sustainable farming:an empirical analysis of farmers' adoption decision of precision agriculture technology

Precision agriculture (PA) describes a suite of IT based tools which allow farmers to electronically monitor soil and crop conditions and analyze treatment options. This study tests a model explaining the difficulties of PA technology adoption. The model draws on theories of technology acceptance and diffusion of innovation and is validated using survey data from farms in Canada. Findings highlight the importance of compatibility among PA technology components and the crucial role of farmers' expertise. The model provides the theoretical and empirical basis for developing policies and initiatives to support PA technology adoption

Modelling supply chain network for procurement of food grains in India

The procurement of food grains from farmers and their transportation to regional level has become decisive due to increasing food demand and post-harvest losses in developing countries. To overcome these challenges, this paper attempts to develop a robust data-driven supply chain model for the efficient procurement of food grains in India. Following the data collected from three leading wheat producing Indian regions, a mixed-integer linear programming model is formulated for minimising total supply chain network costs and determining number and location of procurement centres. The NK Hybrid Genetic Algorithm (NKHGA) is employed to cluster the villages, along with a novel density-based approach to optimise the supply chain network. Sensitivity analysis indicates that policymakers should focus on creating an adequate number of procurement centres in each surplus state, well before the start of the harvesting season. The study is expected to benefit food grain supply chain stakeholders such as farmers, procurement agencies, logistics providers and government bodies in making an informed decision