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This study aims to identify the most relevant causal factors and the feedback loops of the dynamics between Tuta absoluta incidence in tomato crops and farmers' reactions to the problem. The authors seek to develop a conceptual model based on farmers' know-how to address crop damage by T. absoluta at a local and regional levels in order to determine how to confront this problem in the tomato-growing region of Sáchica, Colombia.

Community-Based System Dynamics (CBSD) is a participatory research methodology in which a group of stakeholders identifies relevant variables and the cause-effect relations among them which are then arranged into a causal loop diagram. The authors implemented this methodology in a workshop, focused on the farmers' insights related to the pest situation at the local and regional level, to achieve a causal loop diagram that explained pest dynamics and their potential management.

The relevant factors for the presence of T. absoluta, seen in the causal loop diagram, vary regionally and locally. At the local level, the pest impacts tomato production, farmers' well-being and their cash flow, while at the regional level, it affects market dynamics and environment and promotes regional coordination among farmers. Farmers propose product innocuity as a key regional objective. They also proposed establishing a planting calendar and census of greenhouses to control the pest throughout the region and the tomato supply.

First, the synthesized model could not be validated with the farmers due to the COVID 19 epidemic. However, the authors held sessions with experts to analyze each result. Second, decision-makers from the local government did not participate in the workshop. Nevertheless, the approach of the workshop was aimed at understanding the mental models of the farmers since they are the ones who decide how pests are managed. Finally, even though farmers showed interest in projects aimed at proposing area-wide, long-term and wide pest control strategies, there is a risk that they will not adopt the proposed changes, due to risk aversion.

CBSD has not been applied to agricultural systems to analyze impacts from pests at the local and regional levels. The results of this study contribute to designing future interventions for pest control in the region, along with the factors which may turn out to be “side effects” or unwanted results. To design pest control interventions at a regional level, a sound understanding of the variables or factors that control the system dynamics at various levels is required. This study represents the first step towards that end.

Agriculture is the backbone of a country, contributing more than half of the sector of economy throughout the world. The need for precision agriculture is essential in evaluating the conditions of the crops with the aim of determining the proper selection of pesticides. The conventional method of pest detection fails to be stable and provides limited accuracy in the prediction. This paper aims to propose an automatic pest detection module for the accurate detection of pests using the hybrid optimization controlled deep learning model.

The paper proposes an advanced pest detection strategy based on deep learning strategy through wireless sensor network (WSN) in the agricultural fields. Initially, the WSN consisting of number of nodes and a sink are clustered as number of clusters. Each cluster comprises a cluster head (CH) and a number of nodes, where the CH involves in the transfer of data to the sink node of the WSN and the CH is selected using the fractional ant bee colony optimization (FABC) algorithm. The routing process is executed using the protruder optimization algorithm that helps in the transfer of image data to the sink node through the optimal CH. The sink node acts as the data aggregator and the collection of image data thus obtained acts as the input database to be processed to find the type of pest in the agricultural field. The image data is pre-processed to remove the artifacts present in the image and the pre-processed image is then subjected to feature extraction process, through which the significant local directional pattern, local binary pattern, local optimal-oriented pattern (LOOP) and local ternary pattern (LTP) features are extracted. The extracted features are then fed to the deep-convolutional neural network (CNN) in such a way to detect the type of pests in the agricultural field. The weights of the deep-CNN are tuned optimally using the proposed MFGHO optimization algorithm that is developed with the combined characteristics of navigating search agents and the swarming search agents.

The analysis using insect identification from habitus image Database based on the performance metrics, such as accuracy, specificity and sensitivity, reveals the effectiveness of the proposed MFGHO-based deep-CNN in detecting the pests in crops. The analysis proves that the proposed classifier using the FABC+protruder optimization-based data aggregation strategy obtains an accuracy of 94.3482%, sensitivity of 93.3247% and the specificity of 94.5263%, which is high as compared to the existing methods.

The proposed MFGHO optimization-based deep-CNN is used for the detection of pest in the crop fields to ensure the better selection of proper cost-effective pesticides for the crop fields in such a way to increase the production. The proposed MFGHO algorithm is developed with the integrated characteristic features of navigating search agents and the swarming search agents in such a way to facilitate the optimal tuning of the hyperparameters in the deep-CNN classifier for the detection of pests in the crop fields.

From the perspective of any nation, rural areas generally present a comparable set of problems, such as a lack of proper healthcare, education, living conditions, wages and market opportunities. Some nations have created and developed the concept of smart villages during the previous few decades, which effectively addresses these issues. The landscape of traditional agriculture has been radically altered by digital agriculture, which has also had a positive economic impact on farmers and those who live in rural regions by ensuring an increase in agricultural production. We explored current issues in rural areas, and the consequences of smart village applications, and then illustrate our concept of smart village from recent examples of how emerging digital agriculture trends contribute to improving agricultural production in this chapter.

After completion of this case study, students will be able to apply the triple bottom line concept to a spice manufacturing and export company (RBT 3); to examine the options before Value Ingredients Private Limited (VIPL), namely, to cultivate spices in the traditional way versus adopting integrated pest management (IPM) to cater to international markets (RBT 4); to analyse the returns for an IPM farmer vis-à-vis a conventional farmer, and to compare the returns therein (RBT 4); and to evaluate the ways and means of engaging farmers to change their way of cultivation (RBT 5)

The COVID-19 pandemic heightened awareness about the benefits of spices and buoyed its demand worldwide, which presented an opportunity to VIPL, a spice manufacturing company based in Chennai, to expand its business. However, the export markets demanded residue-free spices grown with little or no use of pesticides. Traditional farmers supplying spices to VIPL were accustomed to spraying pesticides whenever there was a pest attack. This case study discussed the options that the protagonist Mr Sijil Karim, managing director and CEO of VIPL, had, who wanted to onboard farmers for pesticide-free cultivation. The options before him were either to continue traditional farming or adopt IPM. This case study discussed the merits, demerits and challenges of each of these options.

The triple bottom line concept discussed three Ps – people, planet and prosperity – for this case as follows: The farmers and the consumers constituted the people in the spice supply chain. The farmers supplying organic, export-worthy spices under the guidance of VIPL gained 30% more than regular spice farmers, which were accrued through cost savings and better prices. The consumers benefitted from the pesticide-free, organic spices through accrued health gains. The manufacture of organic, pesticide-free spices helped the planet, as the process did not release hazardous chemicals into the atmosphere. VIPL manufactured pesticide-free spice with a focus on prosperity.

The case study can be introduced in a course on sustainability while discussing the triple bottom line concept. This case study showed how a for-profit company grew without losing sight of the planet or its focus on people. This case is best suited for students who have preliminary knowledge of supply chain management, operations and sustainability. Therefore, it is suited for sophomore-year students of MBA.

Teaching notes are available for educators only.

CSS 11: Strategy.

This study aims to examine the effects of climate change and agricultural technologies on crop production in Vietnam for the period 1990–2018.

Several econometric techniques – such as the augmented Dickey–Fuller, Phillips–Perron, the autoregressive distributed lag (ARDL) bounds test, variance decomposition method (VDM) and impulse response function (IRF) are used for the empirical analysis.

The results of the ARDL bounds test confirm the significant dynamic relationship among the variables under consideration, with a significance level of 1%. The primary findings indicate that the average annual temperature exerts a negative influence on crop yield, both in the short term and in the long term. The utilization of fertilizer has been found to augment crop productivity, whereas the application of pesticides has demonstrated the potential to raise crop production in the short term. Moreover, both the expansion of cultivated land and the utilization of energy resources have played significant roles in enhancing agricultural output across both in the short term and in the long term. Furthermore, the robustness outcomes also validate the statistical importance of the factors examined in the context of Vietnam.

This study provides persuasive evidence for policymakers to emphasize advancements in intensive agriculture as a means to mitigate the impacts of climate change. In the research, the authors use average annual temperature as a surrogate measure for climate change, while using fertilizer and pesticide usage as surrogate indicators for agricultural technologies. Future research can concentrate on the impact of ICT, climate change (specifically pertaining to maximum temperature, minimum temperature and precipitation), and agricultural technological improvements that have an impact on cereal production.

To the best of the authors’ knowledge, this study is the first to examine how climate change and technology effect crop output in Vietnam from 1990 to 2018. Various econometrics tools, such as ARDL modeling, VDM and IRF, are used for estimation.

Agrifood value chains in developing countries are transforming into higher value markets which require sustainable practices, with smallholders playing a critical role. However, smallholders are a heterogeneous group which may have discrepancies in outcomes to meet sustainability standards. This paper aims to empirically investigate smallholders' heterogeneity towards sustainable value chain practice in developing countries.

Eight key enabling factors of sustainable value chain transformation were used to explore smallholders' typology, then profiled, based on their socio-economic status and current practices. A quantitative method was applied in Indonesia's cashew sector with 159 respondents from the primary producer area on Madura Island. A combination of descriptive analysis, cluster analysis, cross-tab analysis and one-way ANOVA analysis was used in this study.

Four types of groups were identified, each with distinct characteristics and arranged in priority order as follows: accelerator, progressor, inattentive and conservative groups. Interventions can be implemented on per clusters basis or based on potential similarities among clusters, depending on priority. It is noted that the pursuit of sustainable value chain practices by smallholders is not necessarily associated with high socio-economic status, as those with low socio-economic status may have a stronger inclination towards them.

The paper enhances awareness of practitioners and policymakers regarding smallholders' heterogeneity in sustainable value chain practice. It enables more effective and focused interventions to support smallholders who require assistance in sustainable production and value-adding activities. Different smallholders' characteristics call for different assistance/intervention. Practitioners can recognise smallholders' characteristics that are more compatible with higher value markets and sustainability requirements to better integrate their practices. Policymakers must carefully develop short-term and long-term interventions based on the activities prioritised by particular traits to “hit the right button” for smallholders' practice development.

This study investigates the typology of smallholders towards sustainable value chain practices by using eight enabling factors and profiling them based on their socio-economic condition and current practices. Additionally, this study shifts the focus of typology exploration away from the traditional lens of farm sustainability to a larger perspective which encompasses sustainable value chain activities.

The quantitative understanding of the safe input management practices of vegetable producers is essential for both food safety and environmental protection. The purpose of this study is to investigate the current status of safe production in vegetable enterprises and examine the key risk factors affecting the safe production of vegetables from the perspective of agricultural inputs.

Based on the theory of risk analysis, a framework of safe vegetable production risk analysis is constructed from the perspective of production input behaviour. Based on 202 valid questionnaires in Beijing, China, this paper identifies direct risks in input management through statistical descriptive analysis; determines weights through an expert elicitation process and calculates weighted safety values accordingly; and finally uses a categorical regression model to explore the indirect risks affecting corporate safety production.

The results show that direct risk factors include seed treatment risk, pesticide and fertilizer use criteria risk, pesticide and fertilizer operation risk, and pesticide application object risk. The production safety value of Beijing's enterprises is found to be high in the north and south regions, and low in the central region. Finally, some indirect risk factors, namely the cognition of agricultural product safety laws, the cognition of pesticide safety intervals, the cognition of prohibited pesticides and the possession of brands, are found to have positive and significant impacts on the safe production behaviour of enterprises.

These findings provide entry points for interventions aimed at reducing dependence on pesticides and fertilizers and promoting input management for safe vegetable production in enterprises, thus avoiding vegetable safety incidents due to improper practices in the production chain.

This chapter investigates the coffee value chain in Latin America. By drawing on the concept of just transitions as a “connective tissue” between the sustainable development goals (SDGs), the discussion zooms in on the promise of agroforestry for environmental upgrading. The chapter concludes by providing examples of trade-offs between environmental, social and economic aspects.

The purpose of this study is to examine the determinants impacting consumer behaviour in organic food consumption in Bangladesh. This study aims to identify the key factors facilitating organic food consumption and establish a framework by analysing their contextual relationships.

The study used interpretive structural modelling (ISM), relying on expert perspectives from experienced academicians and marketing professionals. A Matrice d'Impacts Croisés Multiplication Appliqués à un Classement (MICMAC) analysis was performed to assess the driving forces and interdependencies among these determinants.

The MICMAC analysis grouped determinants influencing organic food purchases into four categories. The dependent factors, like attitude and food safety, showed moderate driving forces and high dependence. Linkage determinants, such as environmental concern and price, exerted considerable influence with moderate dependence. Independent variables, especially knowledge about organic food, had a strong impact with relatively low dependence.

This study’s insights offer valuable guidance for managers in the organic food industry, providing strategies to address consumer behaviour. Prioritising education on environmental benefits, transparent pricing, collaborating on policies, ensuring food safety and understanding determinants impacting purchase intent can aid in designing effective marketing strategies and product offerings aligned with consumer needs, ultimately promoting sustainability.

To the best of the authors’ knowledge, this study is the first to investigate the interconnections and relative significance of determinants influencing organic food purchases, using the ISM approach and MICMAC analysis. It delves into the previously unexplored territory of understanding the relationships and hierarchical significance of these determinants in shaping consumer behaviour towards organic food purchases.

This study aims to develop a hierarchical reliability framework to evaluate the service delivery performance of education public–private partnerships (PPPs) effectively and efficiently during long-term operations.

The research design included development and test phases. In the development phase, three performance layers, i.e. indicator, component and system, in the education service delivery system were identified. Then, service component reliability was computed through first order reliability method (FORM). Finally, the reliability of the service system was obtained using dynamic component weightings. A PPP school example in Australia was set up in the test phase, where performance indicators were collected from relevant contract documents and performance data were simulated under three assumptive scenarios.

The example in the test phase yielded good results for the developed framework in evaluating uncertainties of service delivery performance for education PPPs. Potentially underperforming services from the component to the system level at dynamic timepoints were identified, and effective preventative maintenance strategies were developed.

This research enriches reliability theory and performance evaluation research on education PPPs. First, a series of performance evaluation indicators are constructed for assessing the performance of the service delivery of the education PPP operations. Then, a reliability-based framework for service components and system is developed to predict service performance of the PPP school operations with consideration of a range of uncertainties during project delivery.

The developed framework was illustrated with a real-world case study. It demonstrates that the developed reliability-based framework could potentially provide the practitioners of the public sector with a basis for developing effective preventative maintenance strategies with the aim of prolonging the service life of the PPP schools.

Evaluating education PPPs is challenging as it involves long-term measurement of various service components under uncertainty. The developed reliability-based framework is a valuable tool to ensure that reliability is maintained throughout the service life of education PPPs in the presence of uncertainty.