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Impact of agricultural cooperative membership on household food security in Mchinji District, Malawi
No full textDespite being an agrarian economy, food insecurity and malnutrition remain major challenges in Malawi. Progress towards food security has been undermined by problems such as environmental degradation, lack of mechanisation, improper land management, insufficient and untimely extension services, and limited landholding, leading to low productivity among smallholders. Agricultural cooperatives can improve productivity by making agricultural inputs, extension advice, and modern farming technologies more accessible. Additionally, cooperatives can enhance smallholders’ income opportunities through collective marketing of agricultural produce to preferred markets. While smallholder cooperatives often fall short of meeting performance expectations, empirical studies in some developing countries provide evidence supporting the view that cooperative membership can improve food security among smallholders. However, no such study has been conducted in Malawi. This study aims to understand the impact of cooperative membership on household food security in rural areas of Malawi’s Mchinji district. Representative samples of cooperative member and non-member households were selected using probability-based sampling techniques. Primary data needed to estimate the impact of cooperative membership on household food security were collected using a structured questionnaire administered in-person to 475 household respondents. The impact of cooperative membership on household food security was estimated using propensity score matching and two-stage least squares (2SLS) regression with instrumental variables. These methods were chosen for their ability to account for selection bias and the effects of other determinants of food security. The findings bridge a gap in cooperative-food security literature and suggest a number of targeted policy interventions to address food insecurity in rural Malawi. The results will also serve as a reference for future research
Changes in total soluble solids concentration, fruit acidity, and yeast assimilable nitrogen in response to altered leaf area to fruit weight ratio in Pinot noir
No full textThe increasing consumer demand for lower alcohol wine and the need to mitigate against a warming climate presents new challenges for winegrowers and the desire to produce grapes with lower total soluble solids (sugar) or earlier harvesting, while preserving other wine compounds and attributes, particularly for varieties intended for red wine production. We investigated how reducing vine leaf area through shoot trimming and leaf removal, applied at different severities and timings to modify the leaf area to fruit weight (LA) ratio, affects fruit composition for producing lower alcohol quality Pinot noir wine. Shoot trimming treatments (half canopy, H in 2015/16 and 2016/17, and quarter canopy, Q in 2016/17 by alternately removing leaves after trimming shoots to half) were applied shortly before veraison (V-, E-L 34), during veraison (V, E-L 35) and post-veraison (V+, E-L 36) in three different vineyard locations (Marlborough, Canterbury and Central Otago, New Zealand). Untrimmed vines served as the control. Lateral shoots were removed during treatment application, and regrowth was removed to maintain a consistent leaf area. Results showed that reducing the LA:FW ratio delayed the accumulation and concentration of total soluble solids (reduction of 1.0 to 2.7 °Brix) at harvest in all trimmed vines over both seasons. Berry weight, malic acid concentration, titratable acidity, and pH at harvest were unaffected by trimming. At target TSS levels of 18 °Brix (10 % ethanol, v/v) in the Marlborough vineyard and 20 °Brix (11 % v/v) in the Central Otago vineyard, V+ vines showed malic acid and titratable acidity levels comparable to the control. At the Canterbury vineyard, these parameters remained similar across all treatments at 16 °Brix (8.9 % v/v). Yeast-assimilable nitrogen concentration increased (188 to 411 mg/L) in early trimmed vines. At Central Otago, roots showed lower carbohydrate reserves across all trimming treatments, likely due to the high yield at the site, while at Canterbury, trimming did not result in significant differences in carbohydrate reserves, likely due to the low yield at the site. At the Marlborough vineyard, vines trimmed early and more severely had less starch in their roots, while HV+ vines maintained similar levels of starch to controls. In conclusion, halving the canopy post-veraison (HV+, LA 0.70 m²/kg as observed in Marlborough) could be considered a viable viticultural option to lower sugar accumulation, thereby reducing potential alcohol content, without affecting titratable acidity and pH. This practice offers significant potential for adapting to a warming climate and producing lower alcohol Pinot noir wines in a sustainable manner
Does fertility intention affect household consumption? Evidence from China
No full textChina is grappling with steep demographic challenges: low fertility rates, a shrinking labour force, and a rising old-age dependency ratio. Combined with its perennially high savings rates, these can stifle consumption spending and overall economic growth. This paper is devoted to analysing the effects of fertility intention, the precursor to fertility rates, on the average propensity to consume in China. Using the two-stage endogenous treatment regression model to account for the endogeneity of fertility intention, we analyse the 2019 and 2021 Chinese Social Survey (CSS) data collected by the Chinese Academy of Social Sciences. The results show that those intending to have more children spend less as a proportion of their income. Furthermore, individuals’ intentions to have more children are negatively associated with age and household size and positively associated with education. Getting remarried and fertility intention are also positively related. Disaggregated analysis shows that the effects of fertility intention on the average propensity to consume vary across household income quartiles, between males and females, urban and rural residents, and households of different sizes. Policies that simultaneously stimulate spending and incentivize having more children should be considered
Prevalence and abundance of plant-parasitic nematodes in New Zealand maize fields: Effects of territory, soil orders, crop stage, and sampling time
Get PDFPlant-parasitic nematodes (PPNs) are significant agricultural pests that can reduce maize yields. This study examines the prevalence, abundance, and diversity of PPNs in New Zealand maize fields, focusing on the effects of territory, soil orders, crop stages, and sampling times. Seven PPN genera were identified: Pratylenchus spp. (root-lesion), Helicotylenchus spp. (spiral), Meloidogyne spp. (root-knot), Heterodera spp. (cyst), Paratylenchus spp. (pin), Criconemella spp. (ring), and Tylenchus spp. PPNs were present in 98% of the samples, with Pratylenchus spp. being the most prevalent (91%), followed by Helicotylenchus spp. (38%). Compared to Waikato and Manawatu-Whanganui, Canterbury had the highest nematode populations, particularly of Pratylenchus spp. and Helicotylenchus spp. Brown and pallic soils supported higher PPN abundances. Sampling during the maize harvesting stage and late autumn resulted in the highest nematode populations and diversity indices. Pratylenchus spp. populations often exceeded the economic threshold of 500 Pratylenchus kg‾¹ of soil, suggesting a significant threat to maize yield in New Zealand. The findings highlight the need for further research to assess the impact of Pratylenchus spp. on maize yield and to develop effective management practices for maize cultivation in the country
Understanding smallholder preferences for joint ventures in Ghana's rice sector: Improving market access through inclusive business models
Get PDFEfforts to connect farmers with markets in an inclusive manner have gained prominence within organizations like the Food and Agriculture Organization of the United Nations (FAO) and Inclusive Business Models (IBMs) have emerged as a promising avenue for achieving this objective. Currently, the adoption of IBM structures remains limited, particularly in developing countries, resulting in a paucity of information on motivating factors behind farmer engagement. This study employs a discrete choice experiment to discern these preferences, focusing on a joint venture model within the rice sector in Ghana. Data from face-to-face surveys with smallholder rice farmers analysed with a latent class model revealed heterogeneity in preferences towards IBM attributes, with a majority (55 per cent) choosing alternatives that were consistent with a joint venture business model. These related to level of investment and decision making, quality standards, price, payment schedules and control. The study also identified demographic and experiential characteristics of farmers willing to engage with IBMs. Such farmers tended to be more educated, younger, possess greater experience in rice farming, manage smaller farms, have experience with contracts, invest in processing equipment, and infrequently adopt new production practices. These findings underscore the potential to enhance the quality of domestically produced rice through IBMs and advocate for government intervention to overcome barriers, especially in the context of investment. Additionally, the results suggest that targeting younger farmers with prior contract-selling experience could encourage participation in IBMs
A model of faulty and faultless disagreement for post-hoc assessments of knowledge utilization in evidence-based policymaking
Get PDFWhen evidence-based policymaking is so often mired in disagreement and controversy, how can we know if the process is meeting its stated goals? We develop a novel mathematical model to study disagreements about adequate knowledge utilization, like those regarding wild horse culling, shark drumlines and facemask policies during pandemics. We find that, when stakeholders disagree, it is frequently impossible to tell whether any party is at fault. We demonstrate the need for a distinctive kind of transparency in evidence-based policymaking, which we call transparency of reasoning. Such transparency is critical to the success of the evidence-based policy movement, as without it, we will be unable to tell whether in any instance a policy was in fact based on evidence
A review of economic, relational, social and environmental measures of agricultural cooperatives performance: Trends, sectoral, and geographical association
Get PDFThere is increasing interest in the performance of agricultural cooperatives. However, there is little consensus on what measures are most appropriate to use or how best to capture the social and environmental impact of these organisations. Despite this, to date, there are no studies that review the literature on agricultural cooperative performance to establish how the most used performance measures have evolved over time and any relationship between the sectors and locations of agricultural cooperatives. Thus, this paper seeks to address this gap by (i) identifying the foremost measures that have been used to evaluate the performance of agricultural cooperatives; and (ii) exploring the trends, sectoral and geographical association with the use of these performance measures. A multistage analytical framework, comprising a journal article network analysis and a qualitative meta-analysis is used to extract relevant information from 124 journal articles and perform content analysis. Subsequently, a non-parametric test is used to examine the association between the year of publication, sector and geographical location of agricultural cooperatives and the performance measures. The results highlight a diverse list of indicators utilised to assess the performance of agricultural cooperatives. However, there is a narrow focus and dominant use of short-term economic metrics, and limited use of environmental and sustainability measures. Also, the results show a significant increase in the use of liquidity indicators in more recent publications. There exists a significant association between the sector of the agricultural cooperative and the most used performance measures but no association with the geographical location. The findings highlight the need to develop performance measures that evaluate the positive spill-over effects of agricultural cooperative activities on non-members, communities, and the natural environment. Also, the findings provide a rubric for benchmarking the performance and identifying best practices that can be shared across different cooperatives
Sectoral uncertainty spillovers in emerging markets: A quantile time–frequency connectedness approach
No full textThis study investigates the sectoral expected uncertainty connectedness in emerging markets across different frequencies and quantiles using the novel quantile time–frequency connectedness approach of Chatziantoniou et al. (2022a). The employed dataset spans from January 1st, 2003 to October 4th, 2022, encompassing 10 key sectors. The findings reveal a robust and notable interconnection among these sectors, with a substantial total connectedness index of 91.01%. We also note that the largest proportion of the sectoral total connectedness is associated with long-term spillovers. Consumer Cyclicals emerges as the primary source of net risk transmission. Conversely, the Communications & Networking and Healthcare appear to be the greatest net receivers of shocks at the median level. Furthermore, we find that the degree of interconnectedness substantially varies over time, frequency, and quantile, and by economic events. In addition, we find suggestive evidence of asymmetric sectoral uncertainty connectedness effects as the uncertainty spillovers are higher during turbulent market conditions than normal market conditions. A positive relationship between uncertainty measures and sectoral connectedness is also observed during periods of smooth and normal market conditions. Besides, we also conduct different portfolio analyses illustrating the importance of risk diversification to reduce investment uncertainty. This has important implications for international investors and policymakers in forming optimal investment portfolios reducing adverse risk spillovers
Vision 2050: A road map for shaping impact-driven future business schools
No full textAs the landscape of business and management education evolves, international bodies such as the AACSB have already released reports highlighting the drivers of the future. There is a need to prepare future business leaders with knowledge and skills to cope with uncertainty and increasing complexities. Thus, a need to transition towards a new paradigm. This research offers insight into the future of business and management education by using tools from the discipline of future studies. By unveiling a new compass to guide the transformation of business education, this research highlights that the future story is more than degrees; it's about shaping business and management education that prioritises community, relevance, impact, and ethical technology use while empowering learners and ensuring equity
Predicting groundwater heads in alluvial aquifers: Benchmarking different model classes and machine-learning techniques with BMA/S
No full textGroundwater heads are commonly used to monitor storage of aquifers and as decision variables for groundwater management. Alluvial gravel aquifers are often characterized by high transmissivities and a corresponding strong seasonal and inter-annual variability of storage. The sustainable management of such aquifers is challenging, particularly for already tightly allocated aquifers and in increasingly extreme and potentially drier climates, and might require the restriction of groundwater abstraction for periods of time. Stakeholders require lead-in time to prepare for potential restrictions of their consented takes.
Groundwater models have been used in the past to support groundwater decision making and to provide the corresponding predictions of groundwater levels for operational forecasting and management. In this study, we benchmark and compare different model classes to perform this task: (i) a spatially explicit 3D groundwater flow model (MODFLOW), (ii) a conceptual, bucket-type Eigenmodel, (iii) a transfer-function model (TFN), and (iv) three machine learning (ML) techniques, namely, Multi-Layer Perceptron models (MLP), Long Short-Term Memory models (LSTM), and Random Forrest (RF) models. The model classes differ widely in their complexity, input requirements, calibration effort, and run-times. The different model classes are tested on four groundwater head time series taken from the Wairau Aquifer in New Zealand (Wöhling et al., 2020). Posterior parameter ensembles of MODFLOW (Wöhling et al., 2018) and the EIGENMODEL (Wöhling & Burbery, 2020) were combined with TFN and ML variants with different input features to form a (prior) multi-model ensemble. Models classes are ranked with posterior model weights derived from Bayesian model selection (BMS) and averaging (BMA) techniques.
Our results demonstrate that no “model that fits all” exists in our model set. The more physics-based MODFLOW model is not necessarily providing the most accurate predictions, but can provide physical meaning and interpretation for the entire model region and outputs at locations where no data is available. ML techniques have generally much lower input requirements and short run-times. They show to be competitive candidates for groundwater head predictions where observations are available, even for system states that lie outside the calibration data range.
Because the performance of model types is site-specific, we advocate the use of multi-model ensemble forecasting wherever feasible. The benefit is illustrated by our case study, with BMA uncertainty bounds providing a better coverage of the data and the BMA mean performing well for all tested sites. Redundant ensemble members (with BMA weights of zero) are easily filtered out to obtain efficient ensembles for operational forecasting