Agricultural Systems最新文献

CONTEXT

Sustainable dairy production is included in the policy agenda of many countries in the tropics to address, among others, their commitment to the Paris Agreement. To the best of our knowledge, however, a study to assess the impact of the proposed interventions for sustainable dairy production is still lacking for most of those countries. Using policy goals as entry points to develop scenarios can provide insight into the impact of policy interventions on dairy farming practices.

OBJECTIVE

This study aimed to evaluate the implication of interventions towards sustainable dairy development identified by the governments of Indonesia and Costa Rica.

METHODS

Information about current farming practices (i.e. the baseline) were collected on 32 smallholder dairy farms in Indonesia and 24 dairy farms in Costa Rica. Scenarios were designed based on policy goals for dairy development and climate change mitigation in each country. The scenarios for Indonesia encompassed relocation of the dairy sector to Sumatra to allow coupling of livestock to land combined with a restriction on manure production to ensure all manure to be applied to grow forage, and a restriction on the amount of purchased feeds, at two levels: maximally 100% and 50% of the baseline. The scenarios for Costa Rica included a silvopastoral system and a reduction in the amount of purchased feeds, at two levels: 50% and 80% lower than the baseline. We estimated greenhouse gas (GHG) emissions at chain level and carbon (C) stocks at farm level.

RESULTS AND CONCLUSIONS

The scenarios for Indonesia increased herd size and milk output by 240–360%, and GHG emissions per farm by 269–455%, while decreased GHG emissions per kg milk by 1–10%, compared to the baseline. C stocks per farm were higher in the scenarios than in the baseline, but compared to natural vegetation much more C is lost under the scenarios because more land is being used. The scenarios for Costa Rica reduced herd size and milk output by 5–25% and GHG emissions per farm by 17–35%, while GHG emissions per kg milk decreased by 10%, compared to the baseline. C stocks per farm were comparable.

SIGNIFICANCE

To achieve the multiple policy goals for sustainable dairy development, the governments need to consider the trade-off between increasing milk production and reducing GHG emissions. In Indonesia, relocation of the dairy sector needs a strict policy to avoid the expansion of dairy farms into tropical forest land. Furthermore, the Costa Rican government needs to incentivise dairy farmers to implement a silvopastoral system to reduce GHG emissions and land use. This, however, will be at the expense of milk output.

CONTEXT

Estimates of conservation agriculture (CA) adoption vary worldwide because of a lack of a standardized methodology to quantify the simultaneous utilization of its core principles of minimum soil disturbance, permanent soil organic cover and crop diversification. Comparisons of CA adoption among farms across regions requires estimation of the farm area and cropping season where CA principles are applied.

OBJECTIVE

To develop the Conservation Agriculture Appraisal Index (CAAI) as a standardized conceptual framework with defined thresholds that indicates the intensity and frequency of use of each CA core principle. CAAI was subsequently applied to quantify CA adoption on farms across four wheat (triticum aestivum) growing regions, both with and without livestock, including dryland and irrigated systems in Australia and Mexico, respectively.

METHODS

CAAI is a continuous scoring system that estimates the intensity and frequency of application of the core principles and their concurrent utilization to assess the extent of CA adoption. CAAI score is the sum of the scores of each core principle, accounting for the percentage of the farm area and cropping season where CA is applied. CAAI emerged from semi-structured interviews, questionnaires, and farm visits that captured underlying patterns of CA use in regional-specific contexts.

RESULTS AND CONCLUSIONS

CAAI assessed annual CA adoption on 100 farms in four wheat growing regions with different environments and farming systems. The adoption of CA was higher in Australia than Mexico, where partial adoption was more prevalent, especially for summer crops. ‘No adoption’ of CA occurred when one of the core principles consistently scored zero within a year.

SIGNIFICANCE

The CAAI can be used as a benchmarking research tool at the farm level to standardize units for comparisons and identify levels of CA adoption by farm area and cropping seasons between and across regions.

CONTEXT

Life Cycle Assessment (LCA) remains a method of choice for assessing the environmental performance of agricultural systems. However, it is rarely applied to multifunctional extensive production systems, in which livestock use, apart from animal production, maintains a continuous disturbance that sustains the diversity of habitats and species.

OBJECTIVE

This study aims to assess the environmental impact and biodiversity of extensive ruminant production on semi-natural grasslands (SNG), that is, High Nature Value (HNV) farming across Europe. We collected data from a total of 41 HNV farms in five countries (Finland, Estonia, Spain, Greece, and France) that produce beef, sheep, and goats, and that incorporate (to a varied degree) semi-natural and permanent pastures into production.

METHODS

We used LCA to assess the potential environmental impact of HNV farms according to global warming potential (GWP₁₀₀), fossil resource scarcity (FRS), water scarcity (WS) and land use (LU), by using the Solagro Carbon Calculator and OpenLCA software. We assessed biodiversity based on the expert scoring system of SALCA-BD. We compared impacts on per area and per product basis across the farms, and related them to the productivity.

RESULTS AND CONCLUSIONS

Results revealed a considerable variation in all environmental impacts among HNV farms, explained mostly by the type of ruminants, main product (meat or milk) and the production level. GWP₁₀₀ per unit in beef product in France was almost twice as high as that in boreal and 3 times more than in Spain, while sheep systems in Greece varied 7-fold for meat. Sheep systems consistently had the highest GWP₁₀₀, while goat systems used the most land, fossil fuel and water. Small ruminant production in Spain had both the highest land occupation and biodiversity values. Biodiversity was at its highest on farms utilising only SNG for production, which, however, related negatively to the farms' production output. Enteric fermentation accounted for 32% of overall emissions.

SIGNIFICANCE

This study makes a novel contribution towards a better understanding of the environmental performance and production capacity of HNV farming systems that are often used as examples of multifunctional and sustainable ruminant-based production.

Context

On pioneer fronts, the new spatial-temporal evolution of agriculture needs to be understood to help farmers find their way to conciliate food production and forest conservation. Analyzing farm trajectories is consequently critical for designing such futures and to assess their commitments with agroecology principles.

Objective

Based on the analysis of the literature on farm trajectories and pathways we proposed a renewed analytical framework to analyze farm trajectories in pioneer fronts and support the identification of desirable strategies for the future.

Methods

A systematic review adapted from the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology was used. From an initial record of 246 papers, 81 were selected as eligible for the review. The articles were classified in six categories according to three criteria: i) the retrospective or prospective analysis of farm trajectories, ii) the consideration or not of the territorial scale (drawing lessons at territorial scale), iii) the use or not of modeling tools. We also explored whether off-farm factors (such as existing infrastructure at territorial scale or access to credit) and intra-farm factors (such as the organization of family work and the role of women within this organization) were considered since these factors affect farms trajectories in pioneer fronts.

Results and conclusions

Results indicate that the concept of trajectory is mainly associated with retrospective analyses while the concept of pathway is mostly associated with prospective studies generally using simulation tools for the design of future scenarios. The link between trajectories and agroecological principles also has been little explored in the literature. Both retrospective and prospective studies fail to pay sufficient attention to the roles of women and family organization. Lastly, most of the methodologies studied do not fully consider the effects of off-farm territorial factors and public policies on these trajectories.

We propose an analytical framework that would address these limitations.

Significance

This framework is currently used in Brazilian and Colombian Amazon and will help defining sustainable farm trajectories limiting deforestation. Such a framework is needed to support farm development on pioneer fronts and broadly in territories that must deal with highly critical environmental agendas.

CONTEXT

Current agricultural systems in the Netherlands and other parts of Europe depend on large quantities of nutrients from imported feed and artificial fertilizers. This may result in unwanted nutrient accumulation and losses and negative environmental impacts. One way of making these systems more sustainable is to balance livestock numbers with the local feed supply and nutrient requirements for crop production. However, there is currently limited quantitative- and spatially-explicit insight into the effects of reduced imports on livestock numbers and nutrient balances.

OBJECTIVE

The aim of this study is to quantify the number of livestock that can be sustained when minimizing nitrogen (N) imports of feed and artificial fertilizers into Dutch agriculture.

METHODS

We developed a spatially-explicit mathematical programming model to optimize livestock numbers in different scenarios of allowed N imports. The model takes into account current flows of nutrients, related to nutrient supply from livestock manure, feed and fodder, and nutrient requirements in agricultural production.

RESULTS AND CONCLUSIONS

Minimizing N feed imports and artificial N fertilizer use under current land use resulted in an overall reduction of livestock density of 57.4% compared to the current livestock density based on 2020 activity data. In addition, it led to an increase in artificial N fertilizer inputs of 20.5% to maintain the N requirements of agricultural land. In contrast, focusing on minimizing artificial fertilizer use led to a reduction in livestock density of 25.8% compared to the current livestock levels. Depending on the scenario, we found strong regional variation in nutrient balances and livestock numbers. For instance, while currently the application rates of artificial fertilizers and livestock manure are relatively constant in space, this was no longer the case when N import in the form of feed was minimized. In that case, the crop-dominated areas showed a deficit in livestock manure and a substantial increase of artificial fertilizers.

SIGNIFICANCE

The presented model allows for assessing spatially explicit impacts of reduced N imports of feed and artificial fertilizers on livestock density. Using the Netherlands as case study, we show that reducing feed imports is a more effective leverage point to reduce N surplus and external N inputs into agriculture as compared to when reducing artificial fertilizer use. Our findings can inform the development of area-specific strategies that are aimed at reconnecting livestock and agricultural land.

Context

Intensification of arable farming in Northwest Europe has led to high yields. However, inadequate use of external inputs such as nutrients, irrigation water and crop protection products has contributed to several environmental problems, such as nitrate leaching and losses of crop protection products. There is a need to reduce environmental losses and contribute to a more circular and sustainable agriculture in Northwest Europe.

Objective

Here, we take ware potato production in the Netherlands as an example cropping system to assess if there is scope to reduce input levels and environmental impact of nutrient, water and crop protection product use without compromising yield.

Methods

We determined variability in use and use efficiency of nitrogen (N), phosphorus (P), potassium (K) and pesticides as well as water productivity (WP) and yields of 96 on-farm ware potato fields in the Netherlands, on both clay and sandy soils. In addition, we assessed if relatively high performance could be achieved on multiple environmental indicators simultaneously.

Results and conclusions

Average N surplus was 265 kg N ha⁻¹ on clay soils and 139 kg N ha⁻¹ on sandy soils and varied among fields by a factor three. Phosphorus and K input exceeded P and K output on clay soils by 33 and 105 kg ha⁻¹, respectively, while on sandy soils P and K balances were close to zero. Mean WP was 43 kg dry matter (DM) mm⁻¹ ha⁻¹ and ranged from 30 to 60 kg DM mm⁻¹ ha⁻¹ for both soil types. In terms of crop protection product use, lowest and highest use differed by a factor four. Unexpectedly, yields did not increase with higher input rates, suggesting that lower input rates are sufficient to obtain current yields. Consequently, input rates were the most important drivers to explain variability in resource use efficiency and environmental impacts. At the same time, a comparison across multiple indicators simultaneously showed that it was possible to achieve relatively high yields with relatively low N surplus, high WP, and low crop protection product use. Hence, environmental impact could decrease substantially if all fields performed similar to the best performing fields.

Significance

This study showed that it seems feasible to maintain high yield levels while reducing resource use in a substantial share of the potato production fields in the Netherlands. If put in practice, this will reduce losses to the environment and therefore contribute to a more sustainable but still productive agriculture.

Context

Human micronutrient deficiencies in sub-Saharan Africa are connected through complex pathways to soils and how soils are managed. Interventions aiming directly at nutrient consumption, such as supplements and food fortification, have direct impacts but are often limited in their reach and require continuous support. In contrast, less direct changes, such as agricultural diversification and agronomic biofortification, are complicated by a wide array of factors that can limit progress toward nutritional outcomes. However, changes in agriculture and dietary patterns, if successfully linked to deficiencies, provide a more systemic transformation with the potential to achieve wide-reaching and self-perpetuating attainment of nutritional goals.

Objective

The purpose of this paper is to advance theoretical frameworks and research methods for holistic analysis of agriculture-based interventions for micronutrient deficiencies.

Methods

We synthesize lessons from the literature and from the Africa RISING project in Malawi and Tanzania about the connections between soil nutrients and human micronutrient deficiencies from the perspective of the five domains of sustainable intensification (productivity, economic, environmental, human condition and social).

Results and conclusions

We present a menu of indicators for future research on the soil-plant-food-nutrition pathway related to micronutrient deficiency and smallholder farming that need to be considered to effectively assess how agricultural interventions may or may not result in the desired nutritional outcomes. Ultimately, addressing micronutrient deficiencies through agricultural interventions requires a holistic approach that considers all five domains. Research on soil-nutrition linkages should consider the feedback loops across the five domains of sustainable intensification.

Significance

Interdisciplinary and participatory research to effectively link soils to human health supports sustainable development.

CONTEXT

Farm size is a key indicator associated with environmental, economic, and social contexts and outcomes of agriculture. Farm size data is typically obtained from agricultural censuses or household surveys, but both are usually only available in infrequent time intervals and at aggregate spatial scales. In contrast, spatially explicit and detailed data on individual fields can be accessed from cadastral information systems or agricultural subsidy applications in some regions or can be derived from Earth observation data. Empirically exploring the field-size-to-farm size relationship (FFR) is a lever to enhance our understanding of spatial patterns of farm sizes by assessing field sizes. However, our currently limited empirical knowledge does not allow for the characterization of the FFR over large spatial extents.

OBJECTIVE

We analyze the FFR using data from the Integrated Administration and Control System (IACS) for Germany. The IACS manages agricultural subsidy applications in the European Union; therefore, the data include spatial information on the extent of all fields and farms for which farmers have applied for subsidies.

METHODS

We developed a Bayesian multilevel model and a machine learning model to estimate farm size based on field size, controlling for contextual factors such as crop types, state boundaries, topography, and neighborhood effects.

RESULTS AND CONCLUSIONS

We found that farm size generally increased with field size for almost all federal states and crop type groups, but the FFR varied considerably in magnitude. Farm size predictions were accurate for medium-sized and large farms (50–7,000 ha, representing 66% of the data) with mean absolute percentage errors of 40–114%, but estimates for smaller farms had higher errors. To evaluate the relationship at the landscape level, we spatially aggregated the predictions into hexagons with a diameter of 15 km. This resulted in more accurate predictions (mean absolute percentage errors of 37%) than at the field level.

SIGNIFICANCE

Our study presents the first empirical insights into the FFR, opening future research directions towards producing spatially explicit farm size predictions at scale. Such information is key for monitoring scale transitions in agricultural systems, facilitating the design of timely and targeted interventions, and avoiding undesired outcomes of such processes.