Smallholder farmers as a backbone for the implementation of the Sustainable Development Goals

2018 ◽  
Vol 27 (3) ◽  
pp. 523-529 ◽  
Author(s):  
Wiltrud Terlau ◽  
Darya Hirsch ◽  
Michael Blanke
Keyword(s):  
Sustainable Development ◽  
Sustainable Development Goals ◽  
Smallholder Farmers ◽  
The Sustainable Development ◽  
Development Goals

scholarly journals Mapping Smallholder Maize Farms Using Multi-Temporal Sentinel-1 Data in Support of the Sustainable Development Goals

2021 ◽  
Vol 13 (9) ◽  
pp. 1666
Author(s):  
Zinhle Mashaba-Munghemezulu ◽  
George Johannes Chirima ◽  
Cilence Munghemezulu
Keyword(s):  
Sustainable Development ◽  
Food Insecurity ◽  
Sustainable Development Goals ◽  
Smallholder Farmers ◽  
Support Vector ◽  
Temporal Data ◽  
The Sustainable Development ◽  
Multi Temporal ◽  
Production Area ◽  
Development Goals

Reducing food insecurity in developing countries is one of the crucial targets of the Sustainable Development Goals (SDGs). Smallholder farmers play a crucial role in combating food insecurity. However, local planning agencies and governments do not have adequate spatial information on smallholder farmers, and this affects the monitoring of the SDGs. This study utilized Sentinel-1 multi-temporal data to develop a framework for mapping smallholder maize farms and to estimate maize production area as a parameter for supporting the SDGs. We used Principal Component Analysis (PCA) to pixel fuse the multi-temporal data to only three components for each polarization (vertical transmit and vertical receive (VV), vertical transmit and horizontal receive (VH), and VV/VH), which explained more than 70% of the information. The Support Vector Machine (SVM) and Extreme Gradient Boosting (Xgboost) algorithms were used at model-level feature fusion to classify the data. The results show that the adopted strategy of two-stage image fusion was sufficient to map the distribution and estimate production areas for smallholder farms. An overall accuracy of more than 90% for both SVM and Xgboost algorithms was achieved. There was a 3% difference in production area estimation observed between the two algorithms. This framework can be used to generate spatial agricultural information in areas where agricultural survey data are limited and for areas that are affected by cloud coverage. We recommend the use of Sentinel-1 multi-temporal data in conjunction with machine learning algorithms to map smallholder maize farms to support the SDGs.

Farmer-Prioritized Climate-Smart Agriculture Technologies

2020 ◽  
pp. 423-445
Author(s):  
Caroline Mwongera ◽  
Chris M. Mwungu ◽  
Mercy Lungaho ◽  
Steve Twomlow
Keyword(s):  
Climate Change ◽  
Sustainable Development ◽  
Sustainable Development Goals ◽  
Smallholder Farmers ◽  
The Sustainable Development ◽  
Trade Offs ◽  
Development Goals ◽  
Smart Agriculture ◽  
Study Sites ◽  
Context Specific

Climate-smart agriculture (CSA) focuses on productivity, climate-change adaptation, and mitigation, and the potential for developing resilient food production systems that lead to food and income security. Lately, several frameworks and tools have been developed to prioritize context-specific CSA technologies and assess the potential impacts of selected options. This study applied a mixed-method approach, the climate-smart agriculture rapid appraisal (CSA-RA) tool, to evaluate farmers’ preferred CSA technologies and to show how they link to the sustainable development goals (SDGs). The chapter examines prioritized CSA options across diverse study sites. The authors find that the prioritized options align with the food security and livelihood needs of smallholder farmers, and relate to multiple sustainable development goals. Specifically, CSA technologies contribute to SDG1 (end poverty), SDG2 (end hunger and promote sustainable agriculture), SDG13 (combating climate change), and SDG15 (life on land). Limited awareness on the benefits of agriculture technologies and the diversity of outcomes desired by stakeholders’ present challenges and trade-offs for achieving the SDGs. The CSA-RA provides a methodological approach linking locally relevant indicators to the SDG targets.

Value Network Analysis for (Re)Organizing Business Models Toward the Sustainable Development Goals

2020 ◽  
pp. 489-517
Author(s):  
Domenico Dentoni ◽  
Laurens Klerkx ◽  
Felix Krussmann
Keyword(s):  
Sustainable Development ◽  
Network Analysis ◽  
Diagnostic Tool ◽  
Sustainable Development Goals ◽  
Business Models ◽  
Innovation Policy ◽  
Smallholder Farmers ◽  
The Sustainable Development ◽  
Value Network ◽  
Development Goals

This chapter introduces the use of value network analysis (VNA) as a diagnostic tool for (re-)organizing business models seeking to contribute to the achievement of multiple sustainable development goals (SDGs). While VNA has already been widely applied to the study of technological innovation ecosystems, this chapter introduces its role for decision-makers in business models seeking to support sustainability transitions toward the SDGs. To demonstrate the approach, the authors apply VNA to the case of the Agricultural Commodity Exchange (ACE) in Malawi. The ACE represents a business model seeking to increase value-chain efficiency while including smallholder farmers to foster food security and reduce rural poverty and marginalization. The authors discuss how VNA can act also as a diagnostic tool for actors seeking to contribute to reduce poverty and hunger (SDGs 1 and 2); enhance economic growth and public infrastructure (SDGs 8 and 9); and foster cross-sector partnerships for sustainability (SDG17). The ACE case demonstrates that VNA provides several entry points for building strategic cross-sector partnerships that act as systemic instruments in science, technology, and innovation policy, coordinating actions to ensure that the right policy mix comes in place to tackle different SDG targets in an integrated way.

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