Decision Support Systems for Agrotechnology Transfer

Sarkar, Reshmi

doi:10.1007/978-94-007-4113-3_10

Cited by 4 publications

(3 citation statements)

References 111 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The future impetus will be on the Decision Support Systems (DSSs) that enable assessing the effectiveness of organic fertilizers locally or regionally [51]. Such an organic fertilizer promotional strategy utilizing a DSS will go a long way in the journey of Indian agriculture towards a sustainable future [52,53].…”

Section: Discussionmentioning

confidence: 99%

What Drives the Use of Organic Fertilizers? Evidence from Rice Farmers in Indo-Gangetic Plains, India

et al. 2021

View full text Add to dashboard Cite

There is a growing concern about the sustainability of agriculture due to the indiscriminate use of chemical fertilizers in several parts of the world. In India, the Indo-Gangetic Plain (IGP) is a region where the externalities of excessive use of chemical fertilizers for cereal production manifest in groundwater pollution, air pollution due to emissions, and soil degradation. In this context, we study the adoption of organic fertilizers in the region and the determining factors. We use data collected from 400 rice farmers to empirically test the link between farmers’ perceptions, revenue expectations, socioeconomic factors, and the policy environment to adopt organic fertilizers. We use Tobit and Cragg’s double hurdle model to study the farmers’ expenditure and adoption of organic fertilizers, respectively. The results show that only 32% of the farmers adopted organic fertilizers in the region. Further, membership in farmer organizations, training, and education are the key variables that determine the adoption of organic fertilizers, in addition to a positive perception of the benefits of their usage. The findings highlight the need for efficient extension efforts in organic fertilizers and suggest policy interventions that promote collective learning through farmer groups.

show abstract

Section: Discussionmentioning

confidence: 99%

What Drives the Use of Organic Fertilizers? Evidence from Rice Farmers in Indo-Gangetic Plains, India

et al. 2021

View full text Add to dashboard Cite

show abstract

“…uses predictive models to anticipate the effects of biotic and abiotic factors on agriculture, including crop growth and ecological models. Some examples of these models are the DSSAT model (Decision Support System for Agrotechnology Transfer), which simulates the development of different crops under different climatic, management and fertilization conditions, and which has been applied in Colombia to assess the impact of climate change and climate variability on crops such as maize, rice, beans and potatoes (Sarkar, 2012); another example is the AquaCrop model, which estimates the water consumption and biomass production of crops as a function of water availability in the soil and atmosphere (Dercas et al, 2022). Finally, the CROPGRO (Crop Growth) model, which simulates the growth and development of leguminous crops such as beans, peanuts and soybeans, considering the effects of biotic factors such as insects, diseases and weeds, has been used in Colombia to optimize integrated pest and disease management practices (Van Loon et al, 2018).…”

Section: Group Search Stringmentioning

confidence: 99%

Assessing the impact of emerging technologies on sustainable fruit production: A systematic review of the literature

Pardo-Pardo,

Cuervo-Bejarano

2023

Agron. Colomb.

View full text Add to dashboard Cite

Agriculture 4.0 refers to innovations in technological tools used in agriculture to achieve different objectives, such as adapting the supply chain to avoid waste, increasing productivity and collecting mass data through ICT (Information and Communication Technologies) to meet the growing food demand of the population. The objective of this study is to conduct a systematic literature review to evaluate the impact of emerging technologies on sustainable fruit production. Initially, a bibliographic search was conducted on the technologies currently implemented in agriculture; the Bibliometrix library of the R Studio software was used, and then an analysis of relevant scientific publications published in the last ten years was carried out through the VOSviewer® software, which allowed the construction and visualization of bibliometric networks. The results show Europe and China as the leading regions in technological development, while developing countries face economic and research limitations; in Colombia, the use of Agriculture 4.0 is focused on the implementation of satellite images for monitoring agro-climatic conditions. In summary, Agriculture 4.0 aims to achieve economic, social, and environmental sustainability in the agri-food sector through data-generating technologies to improve production, reduce costs, and ensure food safety and quality. However, there is a technology gap between developed and developing countries that affects the adoption of these innovations. More support is therefore needed from governments, academia, and the private sector to drive innovation, training, and adoption of these technologies, which can contribute to the economic, social, and environmental development of the country.

show abstract

“…These tools include database management programs for soil, weather, crop management, and experimental data, as well as utilities and application interfaces. The crop simulation models simulate the growth, development, and yield of crops based on the dynamic relationships among soil, plants, and the atmosphere [34][35][36]. In this study, the CSM-CROPGRO-Cotton model, incorporated within the DSSAT shell [18,37], was used to investigate the impact of climate change on cotton yield.…”

Section: Dssat Model Descriptionmentioning

confidence: 99%

A Decision-Making System for Cotton Irrigation Based on Reinforcement Learning Strategy

Chen,

Yu,

Han

et al. 2023

Agronomy

View full text Add to dashboard Cite

This article addresses the challenges of water scarcity and climate change faced by cotton cultivation in the Xinjiang region of China. In response, a precise irrigation model based on reinforcement learning and the crop model DSSAT is proposed. The experimental site chosen for this study is Changji City in northwest China’s Xinjiang province. Integrating the cotton model, CSM-CROPGRO, from the DSSAT model with reinforcement learning algorithms, a decision system was developed to provide accurate irrigation strategies that maximize cotton yield. The experimental results demonstrated that our approach significantly improved cotton yield and, compared to genetic algorithms, reduced water consumption while increasing production. This provides a better solution for developing cotton cultivation in the Xinjiang region. Additionally, we analyzed the differences in irrigation strategies among different decision scenarios, and the results showed that the reinforcement learning method achieved higher yields in water application trends during different periods. This research offers new ideas and methods for improving cotton-crop-management decisions. The study’s focus on maximizing cotton yield while reducing water usage aligns with the sustainable management of water resources and the need for agricultural adaptation to changing climate conditions. It highlights the potential of reinforcement learning methods in improving irrigation decision-making and their applicability in addressing water scarcity challenges. This research contributes to the advancement of cotton crop management and provides valuable insights for agricultural decision-makers in the Xinjiang region and beyond.

show abstract

Decision Support Systems for Agrotechnology Transfer

Cited by 4 publications

References 111 publications

What Drives the Use of Organic Fertilizers? Evidence from Rice Farmers in Indo-Gangetic Plains, India

What Drives the Use of Organic Fertilizers? Evidence from Rice Farmers in Indo-Gangetic Plains, India

Assessing the impact of emerging technologies on sustainable fruit production: A systematic review of the literature

A Decision-Making System for Cotton Irrigation Based on Reinforcement Learning Strategy

Contact Info

Product

Resources

About