Towards a Better Understanding of Small Farming System Resilience in Romania

Although Indonesia has recorded good performance in its national economic development, especially in the agriculture sector during the Covid-19 pandemic, the impact of the pandemic on farming and food systems has not been evaluated yet. This study has evaluated the resilience of the two dominant existing farming systems in West Timor, i.e. (i) wetland farming system and (ii) dryland farming system. This research aims to understand the resilience of farming after the Covid-19 pandemic and to develop strategic policies that could be adopted to increase the resilience of the farming system in West Timor. A quantitative analysis using the Structural Equation Modelling (SEM) was employed to evaluate the relationship and impact of the following seven generic aspects: labour movement, sustainability, economy, socio- culture, output markets, input markets, farming system resilience, and 27 reflective indicators. The analysis shows that dryland farming systems are more resilient than wetland farming systems. It might be understood from the size of the regression coefficient, as the impact of exogenous construct variables of the environment, socioculture, input, and output on the resilience of dryland farming systems is more significant than on wetlands. Economic performance rather than labour movement factors will create better resilience of farming systems for wetland or dryland after the Covid-19 pandemic. Finally, the economic recovery process and the ongoing input supply mechanism after the Covid-19 pandemic have increased the resilience of the dryland food system more than the resilience of the wetland farming system.

Rice imports into Trinidad and Tobago in 2005 represented approximately 95% of domestic demand. Local production has been declining since reaching a peak of 21,000 tonnes in 1992. Present production is 3,500 tonnes. Rice is produced under a variety of production systems and on farms of varying sizes, productivity and quality of output. This paper examines the competitiveness of four production systems used in Trinidad and Tobago, viz.: the transplanting system (ST), the broadcast seeded system (SB)(both being small farms and highly labour intensive), the partially mechanized system of medium sized farms (MM) and a fully mechanized large farm utilizing aerial technology in the production system (CRP). The methodology involved the collection of data on revenue and cost of production and analysis using the framework of the policy analysis matrix, which allowed estimation of the indicators of policy effects, competitiveness and comparative advantage. A scenario analysis of the MM system was done using improved farm outputs. Results of the analysis indicated that the labour intensive, small farm systems are not internationally competitive, unlike the mechanized systems of the medium and large farms. None of the four systems showed comparative advantage. All the systems had Nominal Protection Coefficient (NPC) values of 1.75 – 1.79, indicating that farmers derived positive protection from government policies of guaranteed pricing for rice paddy. The ST, SB MM and CRP systems had Effective Protection Coefficient (EPC) values of 2.38, 2.73, 4.56 and 3.68, respectively, also indicating heavy support to output prices and tradable inputs such as fertilizers and pesticides. The Producer Subsidy Equivalent (PSE) values were positive for all systems indicating net transfers to farmers. Both small-farm production systems had Domestic Resource Coefficient (DRC) values of around 4.5, whereas, the MM and CRP values were around 2.5 indicating higher levels of efficiency on larger farms. A scenario analysis of the MM system using improved outputs showed improvements to competitiveness and comparative advantage. To attain comparative advantage in the four production systems and the one scenario will require further improvements in technology, productivity and quality of paddy. The small farms had negative private profitability and prevailing conditions are unattractive for the continued existence of these farms.

For improving the sustainability and resilience of EU farming systems, it is important to assess their likely responses to future challenges under future scenarios. In the SURE-Farm project, a five-steps framework was developed to assess the resilience of farming systems. The steps are the following: 1) characterizing the farming system (resilience of what?), 2) identifying the challenges (resilience to what?), 3) identifying the desired functions (resilience for which purpose?), 4) assessing resilience capacities, and 5) assessing resilience attributes. For assessing the resilience of future farming systems, we took the same approach as for current farming systems, with the addition that future challenges were placed in the context of a set of possible future scenarios, (i.e., Eur-Agri-SSP scenarios). We evaluated future resilience in 11 case studies across the EU, using a soft coupling of different qualitative and quantitative approaches. The qualitative approach was FoPIA-SUREFarm 2, a participatory approach in which stakeholders identified critical thresholds for current systems, evaluated expected system performance when these thresholds would be exceeded, envisaged alternative future states of the systems (and their impact on indicators and resilience attributes), as well as strategies to get there. Quantitative approaches included models simulating the behavior of the systems under some specific challenges and scenarios. The models differed in assumptions and aspects of the farming systems described: Ecosystem Service modelling focused on the biophysical level (considering land cover and nitrogen fluxes), AgriPoliS considered, with an agent-based approach, socio-economic processes and interactions within the farming system, and System Dynamics, taking a holistic approach, explored some of the feedback loops mechanisms influencing the systems resilience from both a qualitative and quantitative approach. Each method highlighted different aspects of the farming systems. For each case study, results coming from different methods were discussed and compared. The FoPIA-SURE-Farm 2 assessment highlighted that most farming systems are close to critical thresholds, primarily for system challenges, but also for system indicators and resilience attributes. System indicators related to food production and economic viability were often considered to be close to critical thresholds. The alternative systems proposed by stakeholders are mostly adaptations of the current system and not transformations. In most case studies, both the current and alternative systems are moderately compatible with 'Eur-Agri-SSP1 – Agriculture on sustainable paths’, but little with other Eur-Agri-SSPs’. From the point of view of ecosystem services and nitrogen fluxes, the more resilient case studies are those able to provide multiple services at the same time (e.g., hazelnut cultivations in Italy and vegetable and fruit cultivation in Poland, able to provide good levels of both food production and carbon storage) and those well connected with other neighbouring farming systems (e.g., the Dutch case study receiving manure by the livestock sectors). The System Dynamic simulation (applied quantitatively for the Dutch and French case study) highlighted the need to develop resources that can increase farmers’ flexibility (e.g., access to cheap credit, local research and development, and local market). It also showed that innovation, networks, and cooperation contribute to building resilience against economic disturbances while highlighting the challenges for building resilience to environmental threats. From the application of AgriPoliS to the German case study it was concluded that changes in direct payment schemes not only affect the farm size structure, but also the functions of the farming system itself and therefore its resilience. The report showed complementarity between different methods and, above all, between quantitative and qualitative approaches. Qualitative approaches are needed for interaction with stakeholders, understand perceptions of stakeholders, consider available knowledge on all aspects of the farming system, including social dimensions, and perform a good basis for developing and parameterizing quantitative models. Quantitative methods allow quantifying the consequences of mental models, operationalizing the impact of stresses and strategies to tackle them and help to unveil unintended consequences, but are limited in their reach. Both are needed to assess resilience of farming systems and suggest strategies for improvement and to help stakeholders to wider their views regarding potential challenges and ways to tackle them.

Nonfarm Activities and Household Production Choices in Smallholder Agriculture in Vietnam

Small ruminant farming is the main rural occupation in mountain areas of Morocco. This activity is subject to numerous pressures, including repeated climatic hazards that modify pastoral resource availability and trigger changes in production systems. This study aimed to identify and analyse changes in availability and utilisation of pastoral resources acknowledged by farmers over the past 40 years for the small ruminant farming systems of the Central High Atlas region of Morocco. We hypothesised, on the basis of previous research, that resilience of the pastoral component of such systems increases with elevation. We interviewed 50 farmers in three municipalities of Azilal province, at moderate (800–1400 m), medium (1400–1800 m) and high (1800–3000 m) elevations. In the 1970–1980s, pastoral resources were abundant and mobile flocks grazed collective rangelands. Subsequently, repeated droughts and the increased stock numbers promoted by agricultural policies led to the deterioration of resource abundance and quality, especially palatable plants. At moderate elevations, wooded areas and phytomass declined; erosion occurred at high elevations. Traditional systems diversified by including agricultural activities. At moderate elevations, livestock farming intensified, with the adoption of a productive breed, concentrate distribution and the grazing of fallow land. Currently, flock mobility is limited and rangelands provide less than 60% of sheep requirements. At high elevations, the pastoral component persisted at the cost of a diversification of pastoral areas and greater flock mobility. Rangelands are still collectively managed and provide almost 90% of sheep requirements. Since rangelands remain a major feed source, changes in pastoral resources can be considered as a major driver of change in livestock systems. The sustainability of small ruminant farming systems at these elevations thus depends on the conservation of rangeland.

Camarines Sur, located in the Bicol Region of the Philippines, is considered one of the most climate-vulnerable provinces. Despite this, it is a model for a robust and proactive disaster risk reduction and management (DRRM) program. The province features a diverse agroecological landscape, ranging from hilly and rolling terrains to plains surrounding Mt. Isarog National Park, which covers an area of 10.112 hectares with an elevation reaching 1.966 meters above sea level. Farming systems include monocropping, intensive multiple cropping, and agroforestry, with sugarcane and corn on downslopes, groundnuts and vegetables on lower slopes, and irrigated or rainfed rice fields. The integration of cacao (Theobroma cacao L.) is increasingly being explored as a means to enhance both the sustainability and resilience of these farming systems. This study aims to assess the sustainability and resilience of cacao cultivation at the farm level in Barangay Binanuanan, Pili, Camarines Sur. A qualitative research design was employed, utilizing semi-structured interviews with local government officials, focus group discussions with farmers, and a Strengths, Opportunities, and Challenges (SOC) analysis. Findings indicate that cacao farming contributes to economic sustainability, as most farmers benefit from diversified income sources, including off-farm employment and family financial support. Ecological sustainability is supported by cacao’s compatibility with existing farming practices. However, social resilience remains limited, as evidenced by the aging farming population and minimal youth engagement in agriculture. To strengthen overall system resilience, targeted interventions promoting youth participation and community engagement in cacao-based farming are recommended.

Poverty is a rural dilemma and continues to be a persistent multidimensional problem. It is associated with poor farmers, small farm systems, the landless, resource endowments, the socioeconomic environment and externalities. Over 75% of the poor live and work in rural areas, trapped in a poverty–adaptation–fragile lives–little hope–low life expectancy complex, with an enduring hope for self-reliance and a more comfortable life tomorrow. Since agricultural growth is central to improved livelihoods, strategies that focus on promoting such growth are critical: improved efficiency in natural resource management (NRM), of which animals are a part, can directly contribute to productivity enhancement and reduced poverty. It is estimated that approximately 678 million of the rural poor keep animals, within which the largest ownership of animals is seen in mixed crop–animal systems. The role and contribution of animals is discussed with reference to household benefits — current, medium-term and long-term savings, increased productivity of mixed farm systems, sustainability of agricultural systems and the environment, and enhancement of social stability. While large ruminants (buffalo and cattle) are very valuable for agriculture and farm security, milk and beef, ploughing and dung production, small animals (goats, sheep, chickens, pigs and ducks) are particularly important for nutritional and household security. The link between gender and animals is emphasized, especially the implications of the very strong relationship between women and children and small animals. This paper describes in detail characteristics of poor farmers and small farm systems. It discusses opportunities for NRM, and presents examples of data from different categories of animals and the extent of their contribution to total farm income, which in Asia ranges from 7–69%. It is concluded that improved animal production systems can increasingly make a significant contribution to improved human welfare, rural growth and reduced poverty. To achieve this, however, much more investment in agricultural research and development is necessary that can target less-favoured rainfed areas, coupled with participatory and interdisciplinary approaches, effective public–private sector partnerships, and commitment to purpose.

PurposeThe purpose of this study is to explore the relationship between migration, remittances and agricultural productivity by applying the new economics of labor migration model in the context of north‐west China. The specific objectives are to examine the impacts of rural out‐migration on agricultural productivity in various farming systems, and whether remittances have been reinvested in agriculture.Design/methodology/approachCross‐sectional household survey data from three townships were analyzed with the three‐stage least squares (3SLS) regression model.FindingsIn multi‐cropping small farming systems, at least in the short run, the loss resulting from losing family labour on lower‐return grain crop production is likely to be offset by the gain from investing in capital‐intensive and profitable cash crop production.Originality/valueThis study provides empirical evidence for the MELM theory. It expands Tayloret al.'s studies by comparing investment behavior and production choices among multiple farm activities, and enriches previous studies by showing that the relation between remittances and agricultural investment depends on the farm activities' profitability.

Farming systems in Europe are experiencing multiple stresses and shocks that may push systems beyond critical thresholds after which system change is expected to occur. These critical thresholds may lie in the economic, environmental, social and institutional domain. In this paper we take a participatory approach with involvement of farming system stakeholders to assess the presence of critical thresholds in 11 European farming systems, and the potential consequence of surpassing those with regard to system sustainability and resilience. First, critical thresholds of the main challenges, key system variables and their interactions in the studied farming systems were assessed. Second, participants assessed the potential developments of the key system variables in case critical thresholds for main system challenges would be exceeded. All studied systems were perceived to be close, at or beyond at least one identified critical threshold. Stakeholders were particularly worried about economic viability and food production levels. Moreover, critical thresholds were perceived to interact across system levels (field, farm, farming system) and domains (social, economic, environmental), with low economic viability leading to lower attractiveness of the farming system, and in some farming systems making it hard to maintain natural resources and biodiversity. Overall, a decline in performance of all key system variables was expected by workshop participants in case critical thresholds would be exceeded. For instance, a decline in the attractiveness of the area and a lower maintenance of natural resources and biodiversity. Our research shows that concern for exceeding critical thresholds is justified and that thresholds need to be studied while considering system variables at field, farm and farming system level across the social, economic and environmental domains. For instance, economic variables at farm level (e.g. income) seem important to detect whether a system is approaching critical thresholds of social variables at farming system level (e.g. attractiveness of the area), while in multiple case studies there are also indications that approaching thresholds of social variables (e.g. labor availability) are indicative for approaching economic thresholds (e.g. farm income). Based on our results we also reflect on the importance of system resources for stimulating sustainability and resilience of farming systems. We therefore stress the need to include variables that reflect system resources such as knowledge levels, attractiveness of rural areas and general well-being of rural residents when monitoring and evaluating the sustainability and resilience of EU farming systems.

The evaluation and validation of simulation models are important stages of model development. The aim of this work was to validate the Buttercup model (BCM) with experimental data from on-farm trials. Two on-farm experiments (Experiment 1 E1 and Experiment 2 E2) were carried out to evaluate the effect of supplementing dairy cattle with different sources of protein in Small Scale Dairy Farming Systems. Three concentrates (C1= maize grain 610, soybean meal 310, urea 30, and cane molasses 50g kg−1 DM; C2= maize grain 630, soybean meal 320 and cane molasses 50g kg−1 DM; and C3= maize grain 900, urea 50 and cane molasses 50g kg−1 DM) were developed using the BCM. The effect of the concentrates on milk yield, body condition score and live weight was evaluated using a double Latin Square experimental design. In E1 there were no significant differences (P> 0.05) for the variables evaluated. In E2, significant differences (P<0.05) were observed in milk fat content, for C2 (33.8g kg−1) was higher than C1 and C3 (26.2 and 26.0g kg−1). The results suggest that it was possible to fulfil the metabolisable protein requirements of the cows in these systems with low cost sources of non-protein nitrogen like the urea used in C3; and that the synthesis of microbial protein (from non-protein nitrogen and fermentable metabolisable energy of the concentrates) was enough to fulfil the cows’ requirements of MP for the milk yield levels observed in both experiments. The results also indicate that when specific input data set from the experiments was used the model predictions are acceptable, when comparing predicted vs. observed data as a very significant correlation was observed (P<0.01).

The small scale dairy farming systems (SSDFS) contributes to maintain the rural environment and livelihoods in the highlands of central Mexico. However, their viability is compromised due to the high costs of feeds and deficient feeding strategies used by farmers. Simulation models in animal production are important tools for decision making, technology transfer and research. The aim of the present work was: develop a methodological framework for using a simulation model as a decision support tool to develop alternative feeding strategies for cows in SSDFS in the highlands of central Mexico. Local and alternative strategies were simulated (900 feeding strategies). The Buttercup model (BCM) predictions were compared with those reported in a previous work. Deficiencies in the local feeding strategies were identified in terms of metabolizable protein (MP). Alternative strategies were designed to cover these deficiencies by the use of concentrate supplementation. Three concentrates (C1= maize grain 610, soybean meal 310, urea 30, and cane molasses 50g kg−1 DM; C2= maize grain 630, soybean meal 320 and cane molasses 50g kg−1 DM; and C3= maize grain 900, urea 50 and cane molasses 50g kg−1 DM) where tested in order to meet the MP and metabolizable energy (ME) requirements and to evaluate the productive response of dairy cattle through on-farm experiments. For the BCM validation results data of milk yield from the on-farm experiments were compared with the BCM data predictions. The present methodology is aimed to simplify the use of simulation models in situations, places, and systems similar to the one reported here.

Veterinary RecordVolume 188, Issue 10 p. 378-378 Business & Innovation Tech support for small ruminant farming systems First published: 21 May 2021 https://doi.org/10.1002/vetr.529Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume188, Issue1022/29 May 2021Pages 378-378 RelatedInformation

Finding pathways to more sustainability and resilience of farming systems requires the avoidance of exceeding critical thresholds and the timely identification of viable alternative system configurations. To serve this purpose, the objective of this paper is to present a participatory, integrated and indicator-based methodology that leads researchers and farming system actors in six steps to a multi-dimensional understanding of sustainability and resilience of farming systems in the future. The methodology includes an assessment of current performance (Step 1), identification of critical thresholds whose exceedance can lead to large and permanent system change (Step 2), impact assessment when critical thresholds are exceeded (Step 3), identification of desired alternative systems and their expected improved performance of sustainability and resilience (Step 4), identification of strategies to realize those alternative systems (Step 5), and an assessment on the compatibility of alternative systems with the developments of exogenous factors as projected in different future scenarios (Step 6). The method is applied in 11 EU farming systems, and the application to extensive sheep production in Huesca, Spain, is presented here, as its problematic situation provides insights for other farming systems. Participants in the participatory workshop indicated that their farming system is very close to a decline or even a collapse. Approaching and exceeding critical thresholds in the social, economic and environmental domain are currently causing a vicious circle that includes low economic returns, low attractiveness of the farming system and abandonment of pasture lands. More sustainable and resilient alternative systems to counteract the current negative system dynamics were proposed by participants: a semi-intensive system primarily aimed at improving production and a high-tech extensive system primarily aimed at providing public goods. Both alternatives place a strong emphasis on the role of technology, but differ in their approach towards grazing, which is reflected in the different strategies that are foreseen to realize those alternatives. Although the high-tech extensive system seems most compatible with a future in which sustainable food production is very important, the semi-intensive system seems a less risky bet as it has on average the best compatibility with multiple future scenarios. Overall, the methodology can be regarded as relatively quick, interactive and interdisciplinary, providing ample information on critical thresholds, current system dynamics and future possibilities. As such, the method enables stakeholders to think and talk about the future of their system, paving the way for improved sustainability and resilience.

Typologies may be used as tools for dealing with farming system heterogeneity. Small holder farming system into a few farm types particularly achieved by classifying farms into groups that have common characteristics, i.e. farm types, which can support the implementation of a more tailored approach to agricultural development. This article explored patterns of farming system diversity through the classification of 60 smallholder farm households in Nagpur districts of two blocks (Tahsils) Katol (High productive) and Narkhed (Low productive) of India’s Central Region. Based on 2017 survey data, the typology was constructed using the multivariate statistical techniques of principal component analysis and cluster analysis. Results proposed three farm types, stratified on the basis of household, labor, land use, livestock and income variables, explaining the structural and functional differences between farming systems. Types 1 was characterized by relatively high levels of resource endowment and oriented towards other-farm enterprises and crop sales with bullock pair, Types 2 were characterized by relatively low levels of resource endowment and oriented towards other-farm enterprises, dairy (livestock) and crop sales and Types 3 was characterized by crop + Horticulture + Dairy and Bullock pair, respectively. The most salient differences among farm types concerned rearing of bullock pair (income from bullock pair), degree of legume integration, household size and hired labor, degree of diversification into off/non-farm activities and severity of resource constraints (Type 2 was most constrained with a small farm area and herd comprised mainly of livestock particularly cow and under Type 3 crop + Horticulture + Dairy and Bullock pair). It was found that livelihood strategies reflected the distinctive characteristics of farm households; with economic benefit from the farming. This study clearly demonstrates that using the established typology as a practical framework allows identification of type-specific farm household opportunities and constraints for the targeting of agricultural interventions and innovations, which will be further analyzed in the research-for-development project. Conclude that a more flexible approach to typology construction, for example through the incorporation of farmer perspectives, might provide further context and insight into the causes, consequences and negotiation of farm diversity. The three type of farming system studied with some scientific incorporation of intervention and found 34 percent increase over benchmark in field crop + dairy farming system.

Immediate impact of COVID-19 pandemic on farming systems in Central America and Mexico