Building resilience in smallholder-dominated, high-value perishable product (HVPP) systems requires going beyond technology-first approaches, which raise outputs while leaving farm incomes volatile. Drawing on fieldwork in Guangdong’s lychee sector (68 orchards and more than 20 processing and marketing entities from 2024 and 2025), this study demonstrates how pronounced supply swings and a compressed marketing window interact with fragmented producer organizations, asymmetric market access, and procyclical processing. These conditions form a self-reinforcing institutional–structural trap, where weak value chain governance and farmer residual claimancy distort technological upgrading toward short-term productivity, while inadequate buffering capacity prevents intertemporal adjustment. As a result, productivity-enhancing technologies may intensify market gluts, exacerbate price volatility, and worsen income instability, which is an outcome we refer to as the “technology amplification effect”, consistent with a broader productivity–volatility paradox. We develop an institutional–structural–technological framework that conceptualizes resilience as a hierarchical alignment of institutional, structural, and technological capacities and argue for sequenced interventions, namely governance reforms that rebalance risks and rewards and strengthen producer organization, followed by investment in countercyclical buffering infrastructure (processing, cold chains, and market diversification), creating an enabling ecosystem for technology to act as a resilience catalyst. The proposed pathway provides a transferable blueprint for building inclusive resilience in HVPP value chains facing concentrated supply and coordination failures.

Intercropping is widely considered an effective strategy for increasing yields and economic returns per unit of land. However, its effect on household income remains insufficiently understood. From an income structure perspective, this study uses survey data from 1034 households in China’s primary maize-soybean producing areas and conceptualizes maize-soybean strip intercropping (MSSI) adoption in terms of adoption decision and adoption intensity to examine its impact on household income. The results indicate that although MSSI adoption reduces wage income, it increases agricultural operating income and transfer income, leading to a significant increase in total household income. Notably, this effect becomes insignificant when transfer income is excluded, highlighting the role of policy support. An inverted U-shaped relationship exists between adoption intensity and household income, with an optimal adoption ratio of 68.54%. Mechanism analysis shows that MSSI adoption influences income through the yield effect, factor allocation effect, and subsidy effect. Heterogeneity analysis demonstrates that income benefits vary by opportunity cost and income level. Specifically, MSSI adoption yields significant income-increasing effects for smallholders, full-time farmers and non-cash-crop farmers, exhibiting a distinct pro-poor effect. Therefore, to ensure income growth of MSSI adopters, the study suggests optimizing subsidies for MSSI, expanding local income opportunities, and improving intercropping practices.

Agricultural production systems in Sub-Saharan Africa are increasingly exposed to climate-related shocks that threaten food security and livelihood stability. This study examines how African indigenous vegetables function as resilience-enhancing strategies within smallholder production systems in Kenya. Using panel data from Kenya, we analyze how production shocks such as drought, crop failure, and heavy rainfall affect selected dimensions of household food security: coping strategies index, household dietary diversity score, and child anthropometric indicators. Results show that all production shocks significantly increase the coping strategies index, indicating heightened short-term food stress and reduced absorptive capacity. In contrast, dietary diversity remains stable across shock events, suggesting that African indigenous vegetables production helps sustain nutritional quality through its agroecological robustness, short growth cycles, and buffering role in household diets—enhancing adaptive capacity. Anthropometric outcomes reveal that underweight and wasting are sensitive to production shocks. These findings highlight that despite production shocks; households may sustain food group diversity through AIVs and lower-cost staples while overall energy and micronutrient intake may fall below thresholds sufficient to protect child nutritional status. Policy interventions that promote African indigenous vegetables can enhance resilience by supporting diversified, low-input, and climate-adapted production systems in Kenya and similar contexts.

Agrifood resilience is often measured through yield, income, production, adoption and food security indicators. These outcomes remain essential to understanding agrifood resilience but they do not fully capture the social and institutional conditions that help enable households and communities to absorb shocks, adapt to changing climate and market conditions, and sustain gains over time. This paper provides a perspective that draws on an inquiry informed by social return on investment and conducted through a partnership between Nuru, an international non-governmental organization, and Georgetown University’s Global Human Development Capstone program, using Nuru Ethiopia as the primary case. The inquiry found that farmers, cooperative union leaders and staff often interpreted income and market access through broader changes in security, capacity, community, women’s inclusion, and health. Two prior Nuru studies provide supporting examples: Nuru Ethiopia’s use of the Abbreviated Women’s Empowerment in Agriculture Index and Nuru Nigeria’s mixed-methods resilience-capacity evaluation. This perspective provides support for a mixed-methods measurement portfolio to capture resilience in its layered reality combining economic indicators, stakeholder-defined social value, standardized social metrics and resilience-capacity indices. While none of these measures alone can sufficiently capture the complexities of agrifood resilience, together, they allow organizations to monitor and effectively bolster agrifood resilience.

Agricultural plastic film mulches (PFMs) are widely used to enhance crop productivity by conserving soil moisture, suppressing weeds and regulating soil temperature. Although PFM application is a common management practice for high-value crops in Sri Lanka, the effects of different PFM types on soil properties and agroecosystem functioning remain poorly understood. A two-season field study was conducted to evaluate the impact of three PFM types on soil physicochemical properties and chili (Capsicum annuum) performance in an Alfisol, in the dry zone of Sri Lanka, comparing a no-mulch control, reflective low-density polyethylene (LDPE_ref), black LDPE (LDPE_black) and black biodegradable mulch (BDM). Sampling was conducted at 0, 4, 8 and 12 weeks after transplanting. The two seasons presented contrasting hydroclimatic conditions, which influenced the magnitude and consistency of treatment effects. PFM application did not significantly affect soil pH, electrical conductivity, bulk density or total carbon. However, both LDPE-PFMs consistently enhanced soil available phosphorus, LDPE_black and BDM promoted nitrate retention, and LDPE_ref supported the greatest ammonium retention. Among all treatments, LDPE_ref and BDM were most effective in reducing soil temperature and maintaining soil moisture, respectively, under the dry conditions of the region. Chili yield and fresh biomass were significantly higher under all PFM treatments, with highest yields recorded under BDM in growing season 1 and LDPE_ref in growing season 2. Overall, LDPE_ref gave the most consistent agronomic performance in both growing seasons. Long-term implications of PFM accumulation and degradation for soil health and agroecosystem sustainability in Sri Lanka remain to be established.

Accurately predicting the days to flowering for individual rapeseed plants is crucial for supporting operations such as emasculation, bagging, and pollination. However, traditional phenological prediction methods have rarely been applied at the individual plant level. To address this challenge, this study proposes a multimodal deep learning approach that integrates RGB images with future time-series meteorological data. First, the YOLOv8s-seg model segments rapeseed flower regions, reducing interference from complex field backgrounds, and data augmentation enhances data diversity. In the visual branch, five convolutional neural networks—MobileNet V2, MobileNet V3, ResNet18, EfficientNet-B0, and DenseNet 121—are compared. Results show that DenseNet 121 achieves the best balance between prediction accuracy and model complexity, and is selected as the visual backbone network. The meteorological branch uses a long short-term memory model to extract features from future time-series meteorological data, which are then fused with visual features via a multilayer perceptron. To address large-scale differences among meteorological variables, Z-score normalization is used to normalize the meteorological data. Experimental results show that the proposed model performs best on the test set, with a coefficient of determination of 0.8979, a root mean square error of 2.15 days, a mean absolute error of 1.51 days, and a ratio of performance to deviation of 3.13. Therefore, this study provides an effective method for predicting days to flowering in individual rapeseed plants, with potential applications in coordinating flowering periods between parental lines and supporting breeding decision-making.