Agricultural Systems最新文献

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

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:

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  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;

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  reading data provided by the pedometer through their smartphones;

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  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

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.

CONTEXT

Climate change profoundly affects agriculture through increased occurrences of extreme weather events, directly affecting crop growth and food security. The North China Plain (NCP), a significant region for winter wheat production, faces challenges from the changing climate, which could threaten agricultural output and sustainability.

OBJECTIVE

This study aimed to evaluate the effects of a warming climate, fluctuating precipitation, and rising CO₂ levels on winter wheat production in the NCP. Additionally, it developed adaptation strategies, such as modifying the timing of planting and adjusting irrigation and nitrogen fertilizer levels, to mitigate the negative impacts of a changing climate on grain production.

METHODS

Using the DSSAT CROPSIM CERES-Wheat and NWheat models, this study incorporated baseline climate data from 2001 to 2020 and future climate projections from 12 GCMs under the CMIP6 framework. The evaluation was segmented into four future terms (terms 1 to 4) spanning from 2021 to 2100, under two societal development scenarios known as Shared Socioeconomic Pathways (SSPs): SSP2–4.5 and SSP5–8.5.

RESULTS AND CONCLUSIONS

The projections indicated an increase in temperature and precipitation over the century, with the most substantial changes under the SSP5–8.5 scenario. Term 1 (2021–2040) forecasts predicted mild temperature increases (0.89 °C increase in average maximum temperature, 0.74 °C in average minimum temperature) and an 8% increase in precipitation. Term 4 (2081–2100) projections indicated a more severe climate impact, with maximum temperatures rising by 3.19 °C, minimum temperatures by 3.07 °C, and seasonal precipitation increasing by 23%. These climatic changes are expected to reduce the winter wheat growing season by 4–17%, decrease grain numbers by 3–21%, and reduce yield by 4–20% compared to the baseline. However, the increase in CO₂ from terms 1 to 4 could enhance grain yield by 4–30% under SSP5–8.5, indicating a complex interaction between climatic factors and crop productivity. This study showed that adaptation strategies, including adjusting planting times (early October), irrigation levels (300–400 mm), and nitrogen fertilizer application (250–300 kg ha⁻¹), can effectively minimize the negative impacts of warming on grain yield.

SIGNIFICANCE

This study underscores the critical need for immediate and effective adaptation strategies to address the impact of climate change on agriculture. By adjusting agricultural practices, the negative effects on winter wheat production in the NCP can be mitigated, thereby contributing to regional food security in the face of ongoing climate challenges.

CONTEXT

In several lower middle income countries, organic agriculture (OA) has emerged as a top-down government initiative backed by strong political interest, which entails the risk of seriously neglecting the challenges faced by many organic farmers. In some cases, the promotion of OA, particularly large-scale OA conversion programs like in Sikkim and Bhutan, has received widespread attention. A system-level analysis of conversion to OA can provide a fair assessment and is desirable but rare. Often, there are serious issues with data paucity hindering deeper analyses of the feasibility of large-scale OA and its system-level challenges.

OBJECTIVE

This article aims to analyze the feasibility of large-scale conversion to OA by describing management and farming practices, yield gaps, nutrient balances, as well as the governance and the institutional setting of OA in Bhutan. Bhutan is a suitable case study given the generally good availability of data on the agricultural sector.

DATA AND METHODS

We conduct qualitative content analysis of annual, status, and consultation reports, and gray literature. We analyze the yield gap between organic and conventional farms using agricultural census data. A tentative aggregated nutrient balance at the district and organic village levels is also carried out relying on data from agricultural surveys and censuses and associated reports.

RESULTS AND CONCLUSION

OA in Bhutan requires compliance with the standard requirements defined in the Bhutan Organic Standard (BOS), which is part of the family of standards established by the International Federation of Organic Agriculture Movements (IFOAM). Farmers are increasingly opting for certified organic farming, with 6% and 3% of arable land being registered and certified, respectively, under the local organic assurance system (LOAS). The National Center for Organic Agriculture (NCOA) has instituted model organic villages (MOVs) and provides capacity building training and in-kind farm support to organic farmers. The results of the yield gap analysis show that yields in organic systems are between 18% to 45% lower across the three main agro-ecological zones compared to conventional cropping systems. This could exacerbate the country's food self-sufficiency. The results of the nutrient balance reveal a general nitrogen deficiency which could be mitigated with improved OA practices. OA faces many challenges, such as a shortage of funds for implementing organic programs, missing extension for OA, and a lack of research to improve the existing methods.

SIGNIFICANCE

This paper provides clarity on the challenges faced by farmers under state-driven large-scale OA conversion. Understanding the challenges of converting to OA in Bhutan can lead to transferable findings for many similar contexts characterized by smallholder farming systems.

CONTEXT

Crop breeding in the Global South faces a ‘phenotyping bottleneck’ due to reliance on manual visual phenotyping, which is both error-prone and challenging to scale across multiple environments, inhibiting selection of germplasm adapted to farmer production environments. This limitation impedes rapid varietal turnover, crucial for maintaining high yields and food security under climate change. Low adoption of improved varieties results from a top-down system in which farmers have been more passive recipients than active participants in varietal development.

OBJECTIVE

A new suite of research at the Alliance of Bioversity and CIAT seeks to democratize crop breeding by leveraging mobile phenotyping technologies for high-quality, decentralized data collection. This approach aims to resolve the inherent limitations and inconsistencies in traditional visual phenotyping methods, allowing for more accurate and efficient crop assessment. In parallel, the research seeks to harness multimodal data on farmer preferences to better tailor variety development xzto meet specific production and consumption goals.

METHODS

Novel mobile phenotyping tools were developed and field-tested on breeder stations in Colombia and Tanzania, and data from these trials were analyzed for quality and accuracy, and compared with traditional manual estimates and absolute ground truth data. Concurrently, Human-Centered Design (HCD) methods were applied to ensure the technology suits its context of use, and serves the nuanced requirements of breeders.

RESULTS AND CONCLUSIONS

Computer vison (CV)-enabled mobile phenotyping achieved a significant reduction in scoring variation, attaining imagery-modeled trait accuracies with Pearson Correlation values between 0.88 and 0.95 with ground truth data, and reduced labor requirements with the ability to fully phenotype a breeder's plot (4 m × 3 m) in under a minute. With this technology, high-quality quantitative phenotyping data can be collected by anyone with a smartphone, expanding the potential to measure crop performance in decentralized on-farm environments and improving accuracy and speed of crop improvement on breeder stations.

SIGNIFICANCE

Inclusive innovations in mobile phenotyping technologies and AI-supported data collection enable rapid, accurate trait assessment and actively involve farmers in variety selection, aligning breeding programs with local needs and preferences. These advancements offer a timely solution for accelerating varietal turnover to mitigate climate change impacts, while ensuring developed varieties are both high-performing and culturally relevant.