Agricultural Systems最新文献

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.

CONTEXT

The development of new advanced technologies has led to rapid changes livestock production systems. Precision livestock farming (PLF) uses digital devices that gather, process and examine specific physiological, behavioral and production indicators to improve control of individual animals. Regarding dairy farming, despite the wide scientific debate, the digital devices (i.e., pedometers) are not widely used among farmers, and as such, market data are unavailable to study farmer demand for the technology. In literature, the exact causes that favor or discourage the adoption of digital devices have not yet been identified.

OBJECTIVE

The aim of the work was to estimate dairy farmers' willingness to pay (WTP) for an innovative prototype of pedometer that does not need IT infrastructure to work but can operate autonomously, related to factors not yet investigated, and in particular the “payment method”, “type of data provided” by the device, the “convenience of use” and the “price”.

METHODS

We conducted a choice experiment (stated preference method) to determine dairy farmers demand for pedometers and their willingness-to-pay.

RESULTS AND CONCLUSIONS

Farmers are likely to purchase the pedometer under certain conditions and with specific functions:

• ●

  through a monthly subscription associated with the technical support service;

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  for a trial period and possibly returning it at the end of the period;

• ●

  reading data provided by the pedometer through their smartphones;

• ●

  having information related to estrus prediction rather than other related to animal welfare.

SIGNIFICANCE

The results provide a useful contribution to understand the influencing factors of the underutilization of digital devices by dairy farmers. The issue of digital device adoption has become crucial for increasing the competitiveness of dairy farms especially in the current historical period when issues of sustainability and digitization have become of great importance. New solutions or strategies need to be thought of that enable the use of such devices that are technically easy to use and economically more affordable for farmers.

Context

Understanding how multiple factors interact in complex systems is an important issue. In particular, agricultural production systems are based on biological and ecological processes that are influenced by environmental and human factors, all of which interact. When evaluating such systems statistically, these multiple dependences and interactions make it more difficult to model system performances as a function of management practices and weather.

Objective

Our objective was to assess interactions among management practices, weather and system performances. We aimed in particular to identify subsets of farms whose correlations for given pairs of variables as a function of another variable deviated greatly from the traditional correlation between the variables (i.e., atypical farms).

Methods

We investigated a measure of dependence that assesses whether (and if so, how) the correlation between two variables varies as a function of a third one: conditional Kendall's tau. We applied this measure to a set of variables that described management practices (e.g., concentrated feed fed), weather (e.g., precipitation) and performances (e.g., milk production, enteric methane emissions) for dairy-cattle systems in France in 2013 and 2014 (2523 and 804 farms, respectively).

Results and conclusions

In 2013, the amount of digestible organic matter in the ration ingested per cow influenced the correlation between milk production per cow and enteric methane emissions per livestock unit. In particular, the correlation was negative for a set of atypical farms whose ingested digestible organic matter was $\approx$ 2050-$2900\mathrm{kg}.{\mathrm{cow}}^{-1}$. In addition, total annual precipitation in 2013 influenced the correlation between the amount of concentrated feed fed per cow and milk production per cow for farms surveyed in either year. In 2013 and 2014, the correlation began decreasing strongly beyond a certain threshold of precipitation (ca. 1400 and 1100 mm, respectively), which highlighted the need to adapt each farm's practices to its agricultural and weather context.

Significance

Application of conditional Kendall's tau identified interactions that caused the effectiveness of management practices to vary and how they did so.

CONTEXT

In order for farmers in developing countries to combat the effects of climate change, sustainable agricultural practices (SAP) have been promoted, but their adoption rate remains modest. Prior research examining psychological and socio-economic issues has identified farmers' limited knowledge and capital contribute to this problem. However, supply chain systems that require farmers to follow formal and informal rules (governance arrangements) have received little attention in previous studies.

OBJECTIVE

From a system thinking standpoint, this article seeks to comprehend farmers decision regarding SAP adoption by examining the influence of governance arrangements on the socioeconomic and psychological aspects of farmers. This paper concentrates specifically on governance arrangements between farmers and midstream actors, to whom farmers sell their products directly.

METHOD

This study utilises a qualitative research methodology, specifically employing multiple case studies. A system thinking approach utilising a Causal Loop Diagram was applied to comprehend farmers decision regarding SAP adoption. The case studies cover supply chain systems that have different governance arrangements. The study focused on examining the rice supply chains in Indramayu District and the fresh vegetable supply chains in West Bandung District, located in West Java, Indonesia.

RESULTS AND CONCLUSION

This paper found that different governance arrangements exhibit distinct influences, which in turn have varying impacts on farmers' decisions to adopt SAP. Governance arrangements that foster greater market integration and exert influence on the psychological (e.g., knowledge and information) and socio-economic (e.g., provision of production inputs and price assurance) aspects are regarded as the most effective approach to promoting the adoption of SAP by farmers. Furthermore, this paper revealed that midstream actors play a crucial role in promoting the adoption of SAP among farmers.

SIGNIFICANCE

This paper contributes to the body of knowledge on how governance arrangements within agricultural supply chains influence farmers decision regarding SAP adoption. Specifically, it investigates the influence of midstream actors in facilitating farmers' adoption of SAP by addressing their psychological and socioeconomic requirements. The findings imply that government supports must be extended to midstream actors that have made efforts to persuade their farmers to implement SAP as a means of addressing climate change, as well as implementing midstream actors-to-farmer extension initiatives alongside the government's existing farmer-to-farmer extension programmes.

Context

Agroecological practices are known to reduce environmental pressure of farming systems and increase food system resilience in tropical regions. In contrast, in the temperate climate and industrialized agricultural context of the Netherlands, agroecology use remains limited and its impacts are unknown. As agroecological systems can form a sustainable alternative to conventional farming, it is relevant to study to what extent farmers use agroecological practices in the Netherlands, potentially serving as a model for transition.

Objective

This study assesses spatial patterns of uptake of agroecological practices and their relationship with income resilience.

Methods

Using data from 735 Dutch farms, we created a composite Agroecology Index to quantify the extent of use of agroecological practices. The FAO's 10 Elements of Agroecology framework was used to select indicators for the index, by selecting the six of the Elements in this framework for which our dataset contained suitable data. Linear regressions explored underlying factors, such as farm size and farm type. Using an outlier analysis, bright and dark spot farms, exceeding or lagging the Dutch average, were mapped. We evaluated the effects of agroecological practices on income resilience over a 10-year period.

Results and conclusions

On a 0–100 scale, agroecology scores ranged from 16.5% to 61.1% (36.6 ± 7.0), with stark contrasts between different Elements of agroecology. Arable farms excelled in Diversity, while livestock farms performed better in Efficiency. Spatial variation was substantial, with both bright and dark spots dispersed across the country. We found highest average scores and the strongest prevalence of bright spots in the province of Flevoland, an area with an exceptional number of organic farmers. Higher agroecology scores were linked to increased income stability, independent of farm size or type.

Significance

The methods developed in this study provide an approach to address the growing demand for evidence of the upscaling of agroecology in practice. Also, the relationships between agroecology and social and environmental outcomes from farm to regional scales can be addressed, which can guide developments towards sustainable agriculture.

Anthropogenic activities are leading to increased concentrations of greenhouse gases, especially CO₂, in the atmosphere. This is threatening the resilience of cropping systems, although many crops show strong adaptation abilities. How interactions between climate change and increases in the atmospheric CO₂ concentration ([CO₂]) will ultimately affect regional crop production, including growth processes and water utilization, is not well understood. Climate variability has different effects on agriculture depending on the type of water resources (i.e., rain-fed vs. irrigated crops). To date, however, there have been no reports on disparities in the responses of crop productivity and water consumption to climate change between irrigated and rain-fed agricultural production at identical latitudes. We aimed to compare the responses of maize crops, in terms of productivity and water consumption, between two mid-high latitude regions under various climate change scenarios, with and without considering the effects of elevated [CO₂]. The Southwestern Plain of the Great Lakes (SPG) located in the U.S. Corn Belt and the Northeast Plain (NEPC) located in the China Corn Belt were selected as irrigated and rain-fed case study areas, respectively. Using the Agricultural Production Systems sIMulator with three global climate models under two representative concentration path emission scenarios in combination with six CO₂ trajectories, the risks and opportunities of global warming for maize crops, in terms of growth and water consumption, were characterized at a regional scale from the viewpoint of the water footprint concept. The influence of climate warming on maize crops will be stronger in the SPG than in the NEPC in terms of the future average length of the whole growing season duration (GSD-w), yield, and water consumption. The sowing date and maize variety were kept constant in these simulations. The model predicted that the protective effect of elevated [CO₂] on maize GSD-w will not be as significant as that on yield. Our results indicate that elevated [CO₂] could reduce the water intensity per unit yield of maize by 159.2 m³/t, on average, in the two study regions. The results of this study provide insights into the risks and opportunities of climate change for irrigated and rain-fed maize cropping systems in mid-high-latitude regions.

CONTEXT

Crop yield forecasting is crucial for ensuring food security and adapting to the impacts of climate change, as it provides early insights into potential harvest outcomes and helps farmers and policymakers make informed decisions in the face of changing environmental conditions. The accuracy of the crop model–based yield forecasting frameworks is affected by the uncertainty in future weather data, which is often substituted with synthetic weather realizations generated by stochastic weather generators.

OBJECTIVE

This study aims to assess the performance of three recent stochastic weather generators—Global Weather Generator (GWGEN), WeatherGEN, and R Multi-Sites Autoregressive Weather GENerator (RMAWGEN) — in producing synthetic weather realizations that accurately represent regional climate variations and their impact on winter wheat yield forecasting.

METHODS

We utilized historical weather data from Daymet, an interpolation of daily meteorological observations that produces gridded datasets with a spatial resolution of 1 km. This data was used both as an input for the weather generators and for evaluating the performance of the generated weather realizations. Furthermore, the weather realizations generated by these weather generators across multiple winter wheat field sites in Kansas were employed in the calibrated Environmental Policy Integrated Climate (EPIC) crop model to assess the potential impact of variations in weather generators on the accuracy of crop yield forecasts.

RESULTS AND CONCLUSIONS

RMAWGEN and WeatherGEN excelled in accurately simulating rainy days and precipitation amounts, with WeatherGEN particularly effective in wet months and RMAWGEN performing best in dry months, showcased their proficiency in diverse weather conditions. RMAWGEN consistently showed lowest error across all variables, including precipitation, solar radiation, and both maximum and minimum temperatures. Except for GWGEN, both RMAWGEN and WeatherGEN demonstrate good agreement with Daymet in replicating spatial variability patterns. RMAWGEN notably outperformed other weather generators, particularly during the forecasting period. Consequently, it showed superior capabilities in forecasting crop yields closely matching the simulated results with Daymet data.

SIGNIFICANCE

The findings of this study are crucial for selecting accurate weather data estimates for crop yield forecasting. Utilizing alternative sources such as ensembles of multiple weather generators or outputs from sub-seasonal multi-model forecast systems may further enhance the accuracy of crop yield forecasts.