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Research Article

Agricultural total factor productivity, digital economy and agricultural high-quality development

Roles Conceptualization, Writing – original draft

* E-mail: [email protected]

Affiliation Party School of Liaoning Provincial Party Committee, Shenyang, China

Roles Writing – review & editing

• Dandan Gao, 
• Xiaogang Lyu

• Published: October 4, 2023
• https://doi.org/10.1371/journal.pone.0292001
• Reader Comments

The long-term and stable development of agriculture is the key to China’s economic development and social stability. Agricultural total factor productivity and the digital economy have become new kinetic energy and new engines driving agricultural high-quality development. It is of great significance to verify whether there are significant spatial and threshold effects in the process of high-quality development of agriculture and to explore the intrinsic relationship between high-quality development of agriculture and agricultural total factor productivity and digital economy. This paper takes 31 provinces in China from 2011 to 2020 as the research object. The coefficient of variation method is used to estimate the comprehensive evaluation index of agricultural high-quality development and digital economy. And Dea-Malmquist index method is used to estimate agricultural total factor productivity. On this basis, the spatial Durbin model and threshold regression model are constructed to explore the spatial and threshold effects of agricultural total factor productivity, digital economy and other factors and high-quality agricultural development. The conclusion is as follows: the high-quality development of agriculture has significant spatial autocorrelation. Agricultural total factor productivity and digital economy have significant direct effect and indirect spillover effect on the high-quality development of agriculture. Agricultural total factor productivity has stage differences in each range of digital economy level, but its influence on agricultural high-quality development shows a positive state. Based on this, the paper puts forward some countermeasures to promote the high-quality development of agriculture.

Citation: Gao D, Lyu X (2023) Agricultural total factor productivity, digital economy and agricultural high-quality development. PLoS ONE 18(10): e0292001. https://doi.org/10.1371/journal.pone.0292001

Editor: Umer Shahzad, University of Galway, Ireland / Anhui University of Finance and Economics, CHINA

Received: June 18, 2023; Accepted: September 8, 2023; Published: October 4, 2023

Copyright: © 2023 Gao, Lyu. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The data underlying the results presented in the study are available from https://data.stats.gov.cn/easyquery.htm?cn=E0103 ; https://data.stats.gov.cn/easyquery.htm?cn=C01 .

Funding: The authors received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

1 Introduction

China is a large agricultural country, and the report of the 20th Party Congress clearly points out that we should adhere to the priority development of agriculture and rural areas and accelerate the building of a strong agricultural country. From the China Rural Statistical Yearbook, it can be found that the average growth rate of the total output value of agriculture, forestry, animal husbandry and fishery from 2000 to 2011 was 11.04%, while the average growth rate of the total output value of agriculture, forestry, animal husbandry and fishery from 2011 to 2021 has become significantly slower, with its average growth rate being 6.43%, which shows that China’s agricultural development has entered a critical period of gear shifting and quality improvement, and the main direction of the current agricultural development is to accelerate the transformation of the agricultural development model and further improve the quality of agricultural development. The report of the 19th CPC National Congress states that “raising total factor productivity is an important and dependable path to achieving high-quality economic development”. Total factor productivity is an important tool for measuring the quality of economic growth and clarifying the driving force of economic growth. It can reflect the economic growth brought by other factors after excluding factor inputs [ 1 , 2 ]. Long SB, Zhang MX (2021) and other scholars have proposed to promote the efficiency of the agricultural supply system through “green agriculture”, “quality agriculture” is an important path to promoting high-quality development of agriculture [ 3 ]. The national “14th Five-Year Plan” puts forward accelerating the development of the digital economy is a realistic need to build a new engine of high-quality development. With the rapid development of big data, the Internet of Things and other new-generation information technology, the digital economy has continuously empowered agriculture, and become a new driving force and a new engine for high-quality development of agriculture [ 4 – 6 ]. Zhou QX, Li XE (2022) and other scholars proposed that the digital economy can break the spatial and temporal constraints of information exchange, effectively promote the sharing of agricultural production information, resource integration and interconnection of factors, thereby enhancing the matching efficiency of the factors of agricultural production, promoting the transformation of the old and new kinetic energy, and boosting the high-quality development of agriculture [ 7 , 8 ]. Therefore, it is of great theoretical significance and practical value to explore the relationship between agricultural total factor productivity, digital economy and agricultural high-quality development.

At present, scholars at home and abroad have provided clearer and more accurate descriptions of the conceptual connotations, influencing factors and research methods of agricultural total factor productivity, digital economy and high-quality development of agriculture. For high-quality agricultural development, scholars have used subjective and objective assignment methods, multi-objective function method, Dagum Gini coefficient, threshold regression model and spatial econometric model to measure and decompose high-quality agricultural development from different perspectives [ 9 – 11 ], and clarified that the factors such as trade opening, China-ASEAN free trade zone tariff concessions for intermediate goods, the construction of the two types of society, agricultural socialization services, digital inclusive finance, green financial support, agricultural technology progress, agricultural science, technology innovation, digital economy, land transfer policy, agricultural tourism integration, rural infrastructure, agricultural productive services, labor migration, and population aging have contributed to or inhibiting effects [ 12 – 24 ]. For total factor productivity in agriculture, scholars have used SFA (stochastic frontier model), DEA (data envelopment analysis model) and Cobb-Douglas function to measure and decompose it, and clarify the relationship between factors such as capital deepening, digital countryside, agricultural insurance, agricultural trade liberalization and total factor productivity in agriculture [ 25 – 28 ]. For the digital economy, scholars have constructed a comprehensive evaluation index system from multiple perspectives, examined its spatial and temporal characteristics as well as regional differences, and explored its empowering effects. However, the relationship between total factor productivity in agriculture, the digital economy and high-quality agricultural development has been less explored, and mainly between the two of them. The research on digital economy and agricultural high-quality development mainly includes: first, exploring the relationship between digital economy and agricultural high-quality development. Li BQ (2022), Lu ZY (2022) and other scholars based on the role of the digital economy empowered agricultural high-quality development mechanism for empirical testing, found that the digital economy on agricultural high-quality development has a nonlinear positive impact, and has regional heterogeneity [ 29 , 30 ]. Second, the practice path of digital economy empowered agricultural high-quality development. Luo QF, Zhao QF (2022) have proposed that to promote the high-quality development of agriculture, we should strengthen the positive external effects of digital technology standards and technological innovation, improve the level of digital services and the ability to use digital technology, and play the role of digital technology in promoting the integration of agricultural development and the expansion of functions [ 6 ]. Zhou QX, Li XE (2022) proposed to vigorously promote the construction of digital infrastructure as a breakthrough, the development of digital technology as a focus point, tapping the potential for endogenous development of agriculture, expanding the digital application scenarios, promoting the deep integration of the digital economy and rural agriculture, creating a distinctive brand of agricultural products, and promoting the high-quality development of agriculture [ 8 ]. For the study of the digital economy and agricultural total factor productivity: Sun GL (2023), Lin QN (2022) and other scholars found that the digital economy has a significant positive impact on agricultural total factor productivity, and regional differences are obvious [ 31 , 32 ]. For the study of agricultural total factor productivity and agricultural high-quality development: scholars such as Long SB, Zhang MX (2021) used agricultural total factor productivity to measure the level of agricultural high-quality development [ 3 ]. Xu PJ (2019) and Wu G (2022) proposed that total factor productivity improvement has a significant positive impact effect on high-quality development [ 33 , 34 ]. The above studies provide useful references and reference for understanding the relationship between agricultural total factor productivity, digital economy and agricultural high-quality development. However, there are certain limitations in the existing literature:(1) The current research on the interaction relationship between agricultural total factor productivity, digital economy and agricultural high-quality development is still weak, and moreover, there is a lack of detailed discussion on its spatial effect and threshold effect. (2) With regard to the indicators for measuring the digital economy and the high-quality development of agriculture, different scholars do not select the indicators in a uniform manner, and some of the indicators are not selected reasonably enough. Can agricultural total factor productivity and digital economy drive agricultural high-quality development? What is the mechanism between the three? In order to answer the above questions, this paper, on the basis of existing research, verifies whether there is spatial correlation and agglomeration of agricultural high-quality development, and establishes spatial regression model and threshold regression model to explore the direct effect, spatial spillover effect and threshold effect of agricultural total factor productivity and digital economy on agricultural high-quality development, with a view to providing certain reference for the road of agricultural high-quality development in China.

2. Theoretical analysis and research hypothesis

2.1 the impact of agricultural total factor productivity on high-quality agricultural development.

Total factor productivity in agriculture mainly affects the high-quality development of agriculture through technological progress and efficiency improvement. Technological progress in agriculture is mainly manifested in the improvement of agricultural management level and the improvement of agricultural innovation capacity. This can improve the working skills of farmers, improve or create more advanced agricultural production tools, which in turn can improve the quality and structure of agricultural factors and promote the process of division of labor and specialization in agriculture. Improvements in agricultural efficiency are mainly in the form of increases in pure technical efficiency and scale efficiency. The increase in agricultural efficiency allows agriculture to unleash greater potential by increasing the coordination between various resource factors under the current level of technology, thus achieving greater overall efficiency per unit of time. Agricultural production factors, enterprises and sectors continue to converge to higher value-added areas due to the price transmission mechanism, creating scale effects and industrial agglomeration.

• H1: Total factor productivity in agriculture has a significant positive effect on high-quality agricultural development.

2.2 The impact of the digital economy on the high-quality development of agriculture

The digital economy mainly influences the high-quality development of agriculture through the accumulation and sharing of data elements and the innovation and application of digital technology. The accumulation and sharing of data elements can enable farmers, producers and processors, the government and other relevant interest groups to grasp the basic situation of agriculture and rural areas, relevant government policies, as well as a large number of information resources such as supply and demand, weather conditions, pests and disasters, etc., create good conditions for communication between township governments and residents, reduce the transaction costs of obtaining information, break down barriers to information exchange, increase the circulation of data and information and knowledge elements speed, reduce the mismatch between supply and demand and ineffective supply caused by information asymmetry and information lag, realize the interoperability and linkage of agricultural production, circulation and consumption, and improve production efficiency. The innovation and application of digital technology can provide real-time monitoring of the data required in agricultural production, provide efficient, accurate and intelligent technical support, and enhance digital and smart agriculture. At the same time, it also changes the traditional way of trading in agriculture, strengthening the connection between different subjects through online platforms and live streaming software, reconfiguring the production supply chain, releasing and reducing redundant links, broadening the sales channels and scope, and expanding the scale of cultivation and production.

• H2: The digital economy has a significant positive effect on the high-quality development of agriculture.

2.3 Spatial spillover and threshold effects of high-quality agricultural development

Total factor productivity in agriculture and the digital economy are shared, permeable, external and diffuse, and can break through spatial restrictions and constraints to achieve the flow of information, factors and technology. Neighboring regions can enhance the breadth and depth of their own agricultural activities by absorbing advanced agricultural technologies, experiences and outstanding achievements from advanced regions. The fragmented state of agriculture-related industries is broken, and planting, breeding, tourism, services, catering, transportation, and logistics are closely linked to promoting the integrated development of various regions and industries.

The digital economy has an important impact on total factor productivity in agriculture, and existing research shows that technological innovation can drive technological progress and efficiency improvements in agriculture, thereby increasing total factor productivity. At the same time, the development of the digital economy can break the constraints of time and space on the dissemination of agricultural knowledge, information and technology, accelerate the integration and optimization of factors, improve the efficiency of resource allocation, and thus optimize agricultural total factor productivity. The impact of the digital economy on agricultural total factor productivity is marginal, and when the level of the digital economy is not high, the impact of agricultural total factor productivity on high-quality agricultural development may be overshadowed by other factors, so that it cannot fully play its role in promoting high-quality agricultural development. That is, the enhancing effect of agricultural total factor productivity on high-quality agricultural development is influenced by the intensity of the digital economy and may show a non-linear relationship.

• H3: Total factor productivity in agriculture and the digital economy have spatial spillover and have a significant positive effect on the high-quality agricultural development of the surrounding areas.
• H4: The influence of agricultural total factor productivity on the high-quality development of agriculture has the threshold effect of digital economy.

3 Research methodology and indicator system construction

3.1 research methodology, 3.1.1 coefficient of variation method..

In this paper, the coefficient of variation method is chosen to measure the comprehensive evaluation index of high-quality agricultural development and digital economy. The calculation steps of this method are [ 35 ]:

3.1.2 Dea-Malmquist index method.

3.1.3 Moran’s index.

In this paper, the Moran index is used to explore whether high-quality agricultural development is spatially relevant. The index is calculated as [ 37 ]:

3.1.4 Spatial panel regression model.

When λ = 0,ρ≠0,δ = 0, it is a spatial autoregressive model; when λ≠0,ρ = 0,δ = 0, it is a spatial error model; when λ = 0,ρ≠0,δ≠0, it is a spatial Durbin model.

3.1.5 Threshold regression model.

In this paper, the Bootstrap method (Hansen, 1999) was used to draw samples 300 times for significance testing of the threshold effect and Wang Qunyong’s threshold regression model instruction (xthreg) to establish a threshold regression model to determine whether there is a threshold effect, as well as the number of thresholds and threshold values. When it is determined that there is a single threshold effect, the existence of double thresholds as well as multiple thresholds can be further explored.

3.2 Indicator system construction

At present, the academic community has not yet formed a comprehensive evaluation index system for high-quality development of agriculture and digital economy. In this paper, on the basis of following the principles of systematicity, scientificity and comprehensiveness, a combination of theoretical analysis, frequency analysis and expert consultation is adopted for the selection of indicators for high-quality development of agriculture and the digital economy. Drawing specifically on the research results of Yang N (2022), Liu ZY (2021), Ma XJ (2022), Gao DD (2023) and Yin CJ (2022) [ 40 – 44 ], this paper seeks to comprehensively and accurately reflect the reality of high-quality agricultural development and digital economy in China, as shown in Tables 1 and 2 . The measurement of total factor productivity in agriculture is relatively mature in academia, with input variables including labor (the number of employees in the primary industry), land (the area of crops sown), fertilizer (the discounted amount of fertilizer applied to agriculture) and capital (the total power of agricultural machinery), and output variables being the value added of agriculture, forestry, animal husbandry and fishery.

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https://doi.org/10.1371/journal.pone.0292001.t001

https://doi.org/10.1371/journal.pone.0292001.t002

3.3 Data sources and description of variables

The data in this paper were obtained from the China Statistical Yearbook , China Environmental Statistical Yearbook and China Rural Statistical Yearbook from 2010 to 2020. For individual missing data, the smoothing index method, linear interpolation method and moving average method were used to supplement them.

In the spatial panel regression model, the dependent variable, the level of high-quality development in agriculture, is represented by (Y). The independent variables are total factor productivity (TFP) and digital economy (DIG) in agriculture. The control variables are urbanization (URB), infrastructure development (INF) and government intervention (FIS). In the threshold regression model, the dependent variable is quality agricultural development (Y), the independent variable is total factor productivity in agriculture (TFP), the threshold variable is the digital economy, and urbanization (URB), infrastructure development (INF) and government intervention (FIS) are the control variables. Where total factor productivity in agriculture is measured using the Malmquist index and is cumulative.

4 Empirical analysis

4.1 analysis of the spatial effect of high-quality development of chinese agriculture.

According to the results of the global Moran index ( Table 3 ), it can be seen that the Moran index is greater than 0 and passes the significance test. It indicates that there is a significant spatial dependence of the level of agricultural high-quality development in space, and therefore a spatial factor should be introduced when building the regression model. According to the results of the LR and Wald tests ( Table 4 ), the LR-SAR, LR-SEM, Wald-SAR and Wald-SEM tests all passed the 1% significance test, indicating that the spatial Durbin model cannot be reduced to a spatial error model or a spatial lag model, and the choice of the spatial Durbin model to explore the spatial spillover effects of total factor productivity in agriculture and the digital economy on the level of high-quality development in agriculture is The R 2 was 0.9472, indicating that the spatial Durbin model fits well. According to the results of the Hausman test, the fixed effects of the spatial Durbin model are all better than the random effects, so the analysis should be based on fixed effects.

https://doi.org/10.1371/journal.pone.0292001.t003

https://doi.org/10.1371/journal.pone.0292001.t004

According to the results of the spatial Durbin model ( Table 4 ), the spatial autoregressive coefficient of the level of high-quality agricultural development was 0.3550, which passed the 1% significance test, which was consistent with the test results of the spatial association effect. The regression coefficients of agricultural total factor productivity and digital economy were 0.0313 and 0.2739 respectively, indicating a positive correlation between agricultural total factor productivity, digital economy and high-quality agricultural development, and all of them passed the 1% significance test. From the spatial lagged variables, we can see that the coefficient of the spatial interaction term of agricultural total factor productivity passed the 1% significance test, and the regression coefficient of 0.0203, is positively correlated with the high-quality development of agriculture. It verifies that the high-quality development of agriculture is open and there are external spillover effects.

As the spatial Durbin model contains a spatial lag term for the variables, the regression results generated by the run cannot accurately estimate the impact of the explanatory variables on the region and the explanatory variables in neighboring regions [ 37 ]. Therefore, the model further decomposes the marginal effects into direct effects (local effects) and indirect effects (spillover effects), as shown in Table 5 .

https://doi.org/10.1371/journal.pone.0292001.t005

The direct and indirect effects of agricultural total factor productivity on the level of quality agricultural development were 0.0343 and 0.0460 respectively, and both passed the 1% significance test. This implies that the improvement of agricultural total factor productivity has a significant contribution to the improvement of high-quality agricultural development. At the same time, the improvement of total factor productivity in agriculture in neighboring provinces will also drive the high-quality development of agriculture in the province. The direct and indirect effects of the digital economy on the level of high-quality agricultural development were 0.2837 and 0.1629 respectively, and both passed the 5% significance test. This implies that the enhancement of the digital economy has a significant contribution to the high-quality development of agriculture. At the same time, the enhancement of the digital economy in neighboring provinces will also drive the high-quality development of agriculture in the province. This is mainly because total factor productivity in agriculture, as an important indicator to measure the quality of agricultural development, its enhancement leads to improved agricultural management, continuous improvement of the system, improvement of the crude development method in the process of agricultural development. Agricultural technology is improved and innovated, increasing the added value of agricultural products, and the input and use of agricultural factors are reasonably allocated, which plays a role in improving quality and increasing efficiency. This in turn improves the level of quality development of local agriculture. The application of digital technology in agriculture has led to changes in agricultural production methods, promoting the optimization and upgrading of traditional input factor structures, improving factor allocation efficiency and continuously promoting the integration of old and new business models. At the same time, the increase in total factor productivity and digital economy in the province’s agriculture can spread to the periphery, allowing the “periphery” to absorb and learn from its management experience, operational models and advanced knowledge and technology, thereby driving high-quality agricultural development in neighboring provinces.

4.2 Analysis of threshold effects for high-quality development of Chinese agriculture

From the results of the threshold effect test ( Table 6 ), the single threshold estimate of 0.41 passed the significance test at the 1% level, suggesting the existence of at least 1 threshold; the double threshold test did not pass the significance tests at 1%, 5% and 10%, suggesting the non-existence of a 2nd threshold. Therefore, there is a threshold of 1 for the level of quality development in agriculture. After determining the existence of a threshold effect and the number of thresholds, the single threshold was estimated and the results showed a threshold of 0.41 for the digital economy at the 95% confidence level. from the threshold regression model for high-quality development in agriculture in Table 7 , it can be seen that urbanization and government intervention have a positive effect on high-quality development in agriculture. The non-linear positive impact of agricultural total factor productivity on the level of agricultural quality development, agricultural total factor productivity has a stage difference between the various zones of the digital economy level, but the impact on agricultural quality development shows a positive upward state. When the digital economy level (Y) is less than 0.41, the level of agricultural quality development increases by 0.0385 for each unit increase in agricultural total factor productivity; when Y≥0.41, the level of agricultural quality development increases by 0.0817 for each unit increase in agricultural total factor productivity. The effect of total factor productivity on the quality development of agriculture has a significant and gradually increasing effect.

https://doi.org/10.1371/journal.pone.0292001.t006

https://doi.org/10.1371/journal.pone.0292001.t007

In summary, all hypotheses proposed in this paper are valid, as shown in Table 8 .

https://doi.org/10.1371/journal.pone.0292001.t008

5 Conclusions and implications

Based on China’s provincial panel data from 2011–2020, this paper innovatively analyses the mechanism of agricultural total factor productivity and digital economy on agricultural high-quality development. Using Moran index, spatial Durbin model and threshold regression model to empirically examine the spatial spillover effect and non-linear relationship between agricultural total factor productivity and digital economy on agricultural high-quality development, the following conclusions are drawn:

(1) The process of agricultural high-quality development is not isolated, and there is a significant spatial dependence on agricultural high-quality development in space. This is consistent with the conclusions drawn by Qin XJ (2020) [ 45 ]. The improvement of agricultural total factor productivity and digital economy has a significant role in promoting the improvement of agricultural high-quality development. At the same time, the improvement of agricultural total factor productivity and digital economy in neighboring provinces will also drive the high-quality development of agriculture in this province. The conclusions of the scholars such as Lu ZY (2022), Chen YH (2022), Suan GL (2023) from the side to confirm the reliability of the research conclusions of this paper [ 18 , 30 , 31 ]. The findings of this study provide practical guidance for vigorously developing the digital economy, improving total factor productivity in agriculture, stimulating innovation in agriculture and rural areas, and strengthening exchanges and cooperation with neighboring provinces to promote the high-quality development of agriculture. (2) There is a significant threshold effect in the process of agricultural high-quality development. With the improvement of the level of the digital economy, the impact of agricultural total factor productivity on agricultural high-quality development has a significant upgrading effect, and the effect is gradually increasing. This indicates that the impact of factors at different threshold stages of high-quality development of agriculture varies, so it should be optimized and adjusted according to the needs of reality, so as to promote the level of high-quality development of agriculture. This conclusion further clarifies the key point and focus points for promoting the high-quality development of farmers through the development of the digital economy. Based on this, the following recommendations are made:

(1) Regions should pay sustained attention to the construction of rural digital infrastructure, expand the coverage of rural networks, break down information barriers and promote information exchange and collaborative operations among all parties. Through policy guidance, promote innovation and transformation of scientific research results in digital technology in agriculture, develop and promote digital information technology and agricultural production equipment adapted to high-quality agricultural development, and improve the level of agricultural science and technology innovation and intelligent agricultural mechanization. Strengthen professional training and policy support for digital skills and literacy of agricultural practitioners, attract technology enterprises and technical talents to join the team, create a high-level digital talent pool, and improve the suitability of agricultural labor endowment and digital technology.

(2) Each region will promote the construction of an agricultural science and technology innovation system and platform in accordance with the actual situation of agricultural development, and enhance the overall effectiveness of the agricultural innovation system. Strengthen the transformation and application of agricultural science and technology achievements, take the path of special agricultural innovation and promote agricultural technology progress. The government and relevant agricultural departments should do a good job of top-level design, formulate scientific and orderly agricultural business planning, comprehensively promote plans and actions to enhance the high quality and quality of agriculture, strengthen the collaborative capacity of agriculture, reasonably allocate agricultural innovation factor inputs and ensure the smooth operation of the agricultural innovation system, thereby enhancing the efficiency of agricultural technology.

(3) Regions should abandon the concept of “local protectionism and self-consistent development” and take the initiative to exchange and cooperate with “pioneering” regions to absorb advanced agricultural technology, management experience and models. We should give full play to the radiating effect of high-quality agricultural development and the spatial spillover effects of positive factors such as total factor productivity and the digital economy, promote the flow of resources and factors between provinces and regions, and encourage regions with a high level of high-quality agricultural development to give more support to regions with a low level of high-quality agricultural development, so as to form a new pattern of win-win cooperation and virtuous development.

There are still some issues to be explored in depth in this study: firstly, this paper uses the digital economy as a threshold variable to examine the non-linear relationship between agricultural total factor productivity and agricultural high-quality development caused by changes in its state variables. In the future, the institutional variable (policy support) can also be considered as a threshold variable to identify the impact of institutional heterogeneity on the improvement of agricultural high-quality development. Secondly, this paper takes the panel data of 31 provinces in China as the research basis to explore the spatial and threshold effects of agricultural total factor productivity, digital economy and agricultural high-quality development at the provincial level, and to grasp the relationship between the three as a whole. It is hoped that with the establishment and improvement of agricultural satellite accounts, small regions or microdata can be measured in the future, which will be the focus of subsequent research.

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• Published: 17 June 2021

The impact of climate change on the productivity of conservation agriculture

• Yang Su   ORCID: orcid.org/0000-0002-4717-9971 1 ,
• Benoit Gabrielle   ORCID: orcid.org/0000-0002-9131-2549 1 &
• David Makowski   ORCID: orcid.org/0000-0001-6385-3703 2  

Nature Climate Change volume  11 ,  pages 628–633 ( 2021 ) Cite this article

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• Agriculture
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Conservation agriculture (CA) is being promoted as a set of management practices that can sustain crop production while providing positive environmental benefits. However, its impact on crop productivity is hotly debated, and how this productivity will be affected by climate change remains uncertain. Here we compare the productivity of CA systems and their variants on the basis of no tillage versus conventional tillage systems for eight major crop species under current and future climate conditions using a probabilistic machine-learning approach at the global scale. We reveal large differences in the probability of yield gains with CA across crop types, agricultural management practices, climate zones and geographical regions. For most crops, CA performed better in continental, dry and temperate regions than in tropical ones. Under future climate conditions, the performance of CA is expected to mostly increase for maize over its tropical areas, improving the competitiveness of CA for this staple crop.

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Data availability

The dataset 10 is made fully available and described in a data paper 11 . The dataset can be accessed using the following link: https://doi.org/10.6084/m9.figshare.12155553 .

Code availability

All the R and MATLAB codes are available in figshare and can be accessed through the link https://doi.org/10.6084/m9.figshare.14427977 or on request from the corresponding author.

Bisht, J. K, Meena, V. S, Mishra, P. K. & Pattanayak, A. (eds) Conservation Agriculture: An Approach to Combat Climate Change in Indian Himalaya (Springer, 2016).

Farooq, M. & Siddique, K. H. M. (eds) Conservation Agriculture (Springer, 2015).

Giller, K. E. et al. Beyond conservation agriculture. Front. Plant Sci. 6 , 870 (2015).

Jat, S. L. et al. Differential response from nitrogen sources with and without residue management under conservation agriculture on crop yields, water-use and economics in maize-based rotations. Field Crops Res. 236 , 96–110 (2019).

Article   Google Scholar  

Steward, P. R. et al. The adaptive capacity of maize-based conservation agriculture systems to climate stress in tropical and subtropical environments: a meta-regression of yields. Agric. Ecosyst. Environ. 251 , 194–202 (2018).

Pittelkow, C. M. et al. When does no-till yield more? A global meta-analysis. Field Crops Res. 183 , 156–168 (2015).

Conservation Agriculture (FAO, 2020); http://www.fao.org/conservation-agriculture/en/

Pittelkow, C. M. et al. Productivity limits and potentials of the principles of conservation agriculture. Nature 517 , 365–368 (2015).

Article   CAS   Google Scholar  

Nichols, V., Verhulst, N., Cox, R. & Govaerts, B. Weed dynamics and conservation agriculture principles: a review. Field Crops Res. 183 , 56–68 (2015).

Su, Y., Gabrielle, B. & Makowski, D. A global dataset for crop production under conventional tillage and no tillage practice. figshare https://doi.org/10.6084/m9.figshare.12155553 (2020).

Su, Y., Gabrielle, B. & Makowski, D. A global dataset for crop production under conventional tillage and no tillage systems. Sci. Data 8 , 33 (2020).

University of Delaware Air Temperature & Precipitation (NOAA/OAR/ESRL PSL, 2020); https://www.esrl.noaa.gov/psd/data/gridded/data.UDel_AirT_Precip.html

CPC Global Daily Temperature (NOAA/OAR/ESRL PSL, 2020); https://www.esrl.noaa.gov/psd/data/gridded/data.cpc.globaltemp.html

Martens, B. et al. GLEAM v3: satellite-based land evaporation and root-zone soil moisture. Geosci. Model Dev. 10 , 1903–1925 (2017).

Miralles, D. G. et al. Global land-surface evaporation estimated from satellite-based observations. Hydrol. Earth Syst. Sci. 15 , 453–469 (2011).

Crop Calendar Dataset: netCDF 5 degree (Univ. of Wisconsin-Madison, 2020); https://nelson.wisc.edu/sage/data-and-models/crop-calendar-dataset/netcdf0-5degree.php

Ho, T. K. Random decision forests. In Proc. 3rd International Conference on Document Analysis and Recognition 278–282 (IEEE, 1995).

Kottek, M., Grieser, J., Beck, C., Rudolf, B. & Rubel, F. World Map of the Köppen–Geiger climate classification updated. Meteorol. Z. 15 , 259–263 (2006).

Laborde, J. P., Wortmann, C. S., Blanco-Canqui, H., Baigorria, G. A. & Lindquist, J. L. Identifying the drivers and predicting the outcome of conservation agriculture globally. Agric. Syst. 177 , 102692 (2020).

Su, Z. et al. Effects of conservation tillage practices on winter wheat water-use efficiency and crop yield on the Loess Plateau, China. Agric. Water Manag. 87 , 307–314 (2007).

van Wie, J. B., Adam, J. C. & Ullman, J. L. Conservation tillage in dryland agriculture impacts watershed hydrology. J. Hydrol. 483 , 26–38 (2013).

Shukla, M. K., Lal, R. & Ebinger, M. Tillage effects on physical and hydrological properties of a typica argiaquoll in Central Ohio. Soil Sci. 168 , 802–811 (2003).

Shen, Y., McLaughlin, N., Zhang, X., Xu, M. & Liang, A. Effect of tillage and crop residue on soil temperature following planting for a Black soil in Northeast China. Sci. Rep. 8 , 4500 (2018).

Zuazo, V. H. D. & Pleguezuelo, C. R. R. Soil-erosion and runoff prevention by plant covers. A review. Agron. Sustain. Dev. 28 , 65–86 (2008).

Shaxson, F. & Barber, R. Optimizing Soil Moisture for Plant Production: The Significance of Soil Porosity (FAO, 2003).

Derpsch, R., Sidiras, N. & Roth, C. H. Results from studies made from 1977–1984 to control erosion by no-tillage techniques in Paraná, Brazil. Soil Tillage Res. 8 , 253–263 (1986).

TerAvest, D., Carpenter-Boggs, L., Thierfelder, C. & Reganold, J. P. Crop production and soil water management in conservation agriculture, no-till, and conventional tillage systems in Malawi. Agric. Ecosyst. Environ. 212 , 285–296 (2015).

Page, K. L., Dang, Y. P. & Dalal, R. C. The ability of conservation agriculture to conserve soil organic carbon and the subsequent impact on soil physical, chemical, and biological properties and yield. Front. Sust. Food Syst. 4 , 31 (2020).

Halvorson, A. D. et al. Spring wheat response to tillage and nitrogen fertilization in rotation with sunflower and winter wheat. Agron. J. 92 , 136–144 (2000).

Radford, B. J. & Thornton, C. M. Effects of 27 years of reduced tillage practices on soil properties and crop performance in the semi-arid subtropics of Australia. Int. J. Energy Environ. Econ. 19 , 565 (2011).

Google Scholar  

García-Palacios, P., Alarcón, M. R., Tenorio, J. L. & Moreno, S. S. Ecological intensification of agriculture in drylands. J. Arid. Environ. 167 , 101–105 (2019).

Scopel, E. et al. Conservation agriculture cropping systems in temperate and tropical conditions, performances and impacts. A review. Agron. Sustain. Dev. 33 , 113–130 (2013).

Thierfelder, C. & Wall, P. C. Investigating conservation agriculture (CA) systems in Zambia and Zimbabwe to mitigate future effects of climate change. J. Crop Improve. 24 , 113–121 (2010).

Lal, R. The role of residues management in sustainable agricultural systems. J. Sustain. Agric. 5 , 51–78 (1995).

Thierfelder, C. et al. Complementary practices supporting conservation agriculture in southern Africa. A review. Agron. Sustain. Dev. 38 , 16 (2018).

Corbeels, M., Naudin, K., Whitbread, A. M., Kühne, R. & Letourmy, P. Limits of conservation agriculture to overcome low crop yields in sub-Saharan Africa. Nat. Food 1 , 447–454 (2020).

Smith, P., Powlson, D. S., Glendining, M. J. & Smith, J. U. Preliminary estimates of the potential for carbon mitigation in European soils through no‐till farming. Glob. Change Biol. 4 , 679–685 (1998).

Williams, A. et al. A regionally-adapted implementation of conservation agriculture delivers rapid improvements to soil properties associated with crop yield stability. Sci. Rep. 8 , 8467 (2018).

Kassam, A., Friedrich, T., Shaxson, F. & Pretty, J. The spread of conservation agriculture: justification, sustainability and uptake. Int. J. Agric. Sustain. 7 , 292–320 (2009).

Hobbs, P. R., Sayre, K. & Gupta, R. The role of conservation agriculture in sustainable agriculture. Phil. Trans. R. Soc. B 363 , 543–555 (2008).

Govaerts, B. et al. Conservation agriculture as a sustainable option for the central Mexican highlands. Soil Tillage Res. 103 , 222–230 (2009).

Su, Y., Gabrielle, B., Beillouin, D. & Makowski, D. High probability of yield gain through conservation agriculture in dry regions for major staple crops. Sci. Rep. 11 , 3344 (2021).

Soil Texture Map (Univ. of Tokyo, 2020); http://hydro.iis.u-tokyo.ac.jp/~sujan/research/gswp3/soil-texture-map.html

Breiman, L. Random Forests. Mach. Learn. 45 , 5–32 (2001).

Frieler, K. et al. Assessing the impacts of 1.5 °C global warming—simulation protocol of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2b). Geosci. Model Dev. 10 , 4321–4345 (2017).

ESGF Project (Lawrence Livermore National Laboratory, 2020); https://esgf-node.llnl.gov/projects/esgf-llnl/

Portmann, F. T., Siebert, S. & Döll, P. MIRCA2000-Global monthly irrigated and rainfed crop areas around the year 2000: a new high-resolution data set for agricultural and hydrological modeling. Global Biogeochem. Cycles 24 , GB1011 (2010).

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Acknowledgements

This work was supported by the ANR under the ‘Investissements d’avenir’ programme with the reference ANR-16-CONV-0003 (CLAND) and by the INRAE CIRAD meta-program ‘GloFoods’.

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Y.S. wrote the main manuscript text and B.G. and D.M. modified it. Y.S., B.G. and D.M. worked together to prepare the figures and tables. Y.S. collected the data. Y.S., B.G. and D.M. designed and built the models to process the data. Y.S. worked on the model cross-validation. All authors reviewed the manuscripts.

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Peer review information Nature Climate Change thanks Marc Corbeels, Krishna Naudin, Christian Thierfelder and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended data fig. 1 accumulated fraction of the cropping area as a function of the probability of yield gain under ca (+f+wd -irrigation) versus ct–r–sc (+f+wd -irrigation) systems in different climate regions..

Accumulated fraction of the cropping area as a function of the probability of yield gain under CA (+F+WD – Irrigation) versus CT–R–SC with fertilization (+F) and weed and pest control (+WD) without irrigation (–Irrigation) for eight major crops (a–h) and different climate zones. The results are based on the average climate conditions over 2021–2020 simulated by the Ipsl-cm5a-lr climate model and RCP 4.5 scenario.

Extended Data Fig. 2 Relative importance ranking of the model inputs.

The importance was defined by the mean decrease in accuracy in the ‘cforest’ model. Where ‘PB’ indicates precipitation balance over crop growing season; ‘Tmax’ indicates maximum air temperature over crop growing season;’Tave’ indicates average air temperature over crop growing season; ‘Tmin’ indicates minimum air temperature over crop growing season; ‘Crop’ indicates the crop species;’ST’ indicates soil texture; ‘SCNT’ indicates soil cover management under the variants of no tillage systems; ‘SCCT’ indicates soil cover management under CT systems; ‘RNT’ indicates crop rotation management under the variants of no tillage systems; ‘RCT’ indicates crop rotation management under CT systems; ‘FNT’ indicates management of crop fertilization under the variants of no tillage systems; ‘FCT’ indicates crop management of crop fertilization under CT systems; ‘WDNT’ indicates management of weed and pest control under the variants of no tillage systems; ‘WDCT’ indicates crop management of weed and pest control under CT systems.

Extended Data Fig. 3 The accumulated fraction of the cropping area in different level of change on the probability of yield gain under CA (+F+WD) versus CT–R–SC (+F+WD) under different crops, climate models and RCP scenarios.

The accumulated fraction of the cropping area in different level of change on the probability of yield gain under CA (+F+WD) versus CT–R–SC (+F+WD) for different crops, climate models and RCP scenarios. The results are based on the average climate data in different RCP scenarios (RCP 2.6, RCP 4.5, RCP 6.0, RCP 8.5) in Ipsl-cm5a-lr model, and RCP 4.5 scenario in different climate models (Ipsl-cm5a-lr, Gfdl-esm2m, Hadgem2-es, Miroc5) for both current (2021–2020) and future (2051–2060) scenarios.

Extended Data Fig. 4 Distributions of experiment site for each crop.

This map and the corresponding dataset are presented in ref. 11, 42. This figure was generated by MATLAB R2020a (Version 9.8.0.1451342, https://fr.mathworks.com/products/matlab.html ). In this meta-dataset (ref. 11 ), 4403 paired yield observations were extracted from NT and CT for 8 major crop species, including 370 observations for barley (232 observations for spring barley and 138 for winter barley), 94 observations for cotton, 1690 observations for maize, 195 observation for rice, 160 observations for sorghum, 583 observations for soybean, 61 observations for sunflower, 1250 observations for wheat (1041 observations for winter wheat and 209 observations for spring wheat) in 50 countries from 1980 to 2017.

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Supplementary Table 1, Figs. 1–8 and references.

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Su, Y., Gabrielle, B. & Makowski, D. The impact of climate change on the productivity of conservation agriculture. Nat. Clim. Chang. 11 , 628–633 (2021). https://doi.org/10.1038/s41558-021-01075-w

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• Systematic Map Protocol
• Open access
• Published: 29 October 2018

Evidence for the impacts of agroforestry on agricultural productivity, ecosystem services, and human well-being in high-income countries: a systematic map protocol

• Sarah E. Brown 1 ,
• Daniel C. Miller 1 ,
• Pablo J. Ordonez 2 &
• Kathy Baylis 2  

Environmental Evidence volume  7 , Article number:  24 ( 2018 ) Cite this article

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A Systematic Map to this article was published on 17 March 2022

Agroforestry bridges the gap that often separates agriculture and forestry by building integrated systems that address both environmental and socio-economic objectives. Agroforestry can improve the resiliency of agricultural systems and mitigate the impacts of climate change. Existing research suggests that integrating trees on farms can prevent environmental degradation, improve agricultural productivity, increase carbon sequestration, generate cleaner water, and support healthy soil and healthy ecosystems while providing stable incomes and other benefits to human welfare. Although these claims are becoming more widely accepted as the body of agroforestry research increases, systematic understanding of the evidence supporting them remains lacking for high-income countries. This systematic map will address this research need by providing a tool for identifying and visualizing the existing evidence demonstrating the impacts of agroforestry practices and interventions on agricultural productivity, ecosystem services, and human well-being. The results will be useful for informing policy decisions and future research by making the evidence easily accessible and highlighting the gaps in knowledge as well as areas with enough evidence to conduct systematic reviews.

This systematic map will identify, collect, display, and describe available evidence on the impacts of agroforestry on agricultural productivity, ecosystem services, and human well-being in high-income countries. The search strategy will cover 5 primary databases and 24 organizational websites using a pre-defined search string designed to capture studies relating agroforestry practices and interventions to outcomes in high-income countries. The searches will all be conducted in English. We will screen the identified studies for inclusion or exclusion in stages, first on title and abstract and then on full-text. We will collect data from studies included at the full-text stage to form the map and associated database. For inclusion, the study in question must assess the impacts of the deliberate promotion and/or actual integration of woody perennials (trees, shrubs, palms, bamboos, etc.) on the same land management unit as agricultural crops and/or animals.

Agroforestry has risen to prominence as a land-use strategy to help address global climate change and provide other environmental, economic, and social benefits [ 1 , 2 , 3 , 4 , 5 , 6 ]. However, systematic knowledge on the human–environment impacts of agroforestry practices and interventions remains lacking. Agroforestry is promoted for its potential for carbon sequestration, soil erosion and runoff control, and improved nutrient and water cycling, as well as for offering socio-economic benefits and greater agricultural productivity [ 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 ]. While researchers and policy makers have long studied and supported agroforestry practices in low- and middle-income countries (L&MICs), particularly in tropical regions, recognition and promotion of agroforestry in the temperate climates typical of developed countries gained steam only more recently [ 2 , 12 ]. As the conversation discussing the potential and future for agroforestry continues to evolve, we note an increased study of and policy support for agroforestry in high-income countries (HICs) [ 2 , 8 , 13 , 14 ]. Evidence of the socio-economic and biophysical impacts of various agroforestry interventions and practices in HICs spans many disciplines and addresses a broad range of outcomes, thus creating an opportunity and need to synthesize the evidence for easier exchange of knowledge and ideas.

This study therefore aims to assemble the research showing the impacts of agroforestry practices and interventions in HICs to provide an evidence map of the literature to aid researchers and policy-makers in developing strategies for future research initiatives and policy formation. This systematic map (SM) directly parallels an evidence gap map (EGM) of the impacts of agroforestry in L&MICs that is currently in progress by members of same research group [ 15 ]. This protocol draws heavily from the L&MIC EGM protocol since the methods are intentionally aligned. Although these two maps are intended to directly parallel one another, we acknowledge that there are differences in the types of agroforestry practiced and studied between HICs and L&MICs. These differences may in part be explained by greater wealth and resources associated with the socio-political histories in HICs than in L&MICs as well as different types of climates, with HICs being predominantly in temperate climates and L&MICs being predominantly in tropical climates. Furthermore, we note that the L&MIC EGM has a stronger emphasis on agroforestry interventions since it was conducted through the International Initiative for Impact Evaluation (3ie), which focuses more on synthesizing evidence on the impacts of interventions. This SM intends to capture studies on the impacts of both agroforestry interventions as well as agroforestry practices in general, without placing emphasis on one or the other.

Simply defined, agroforestry is the intentional integration of woody vegetation, such as trees and shrubs, with crops and/or livestock simultaneously or sequentially on a land management unit. This integration is intended to diversify production systems to create environmental, economic, and social benefits through complementary interactions between the system components [ 16 , 17 , 18 ]. The general types of agroforestry include agrisilviculture (also called silvoarable, defined as trees integrated with cropping systems), silvopasture (trees integrated with livestock systems), agrosilvipasture (trees integrated with both crops and livestock as a system), forest farming (crop or livestock production within a forested area), urban agroforestry (often referred to as homegardens, defined as integrating trees with crops near the homestead), and other types, such as integrating trees in fisheries or beekeeping operations [ 18 ]. Common agroforestry practices are identified and presented in Table  1 . We note that these practices are meant to be mutually exclusive (i.e., an agrosilvopasture practice would not also be classified as an agrisilviculture practice and a silvopasture practice).

We further define several types of interventions that may be used to promote any one or more of these agroforestry practices. Agroforestry intervention types are described in Table  2 , and they represent types of support policy-makers could provide to promote adoption of one or more of the agroforestry practices described in Table  1 .

This systematic map will denote whether a study is an impact evaluation of an agroforestry-related intervention or is an evaluation of the impact only of an agroforestry practice.

Agroforestry research began with the study of the existing traditional practices of local populations, which formed the basis for conducting more rigorous experimental research [ 22 ]. As agroforestry research developed, researchers found a high potential for agroforestry to address many current environmental and social concerns, such as climate change and food security [ 22 ]. From this knowledge base, agroforestry advocates began pushing for the establishment of policies and programs to support the integration of trees on agricultural lands.

Broadly speaking, however, governmental policies for landowners have often lacked incentives to take up agroforestry practices [ 23 ]. Historically, there was the assumption that land must remain segregated between agriculture and other uses to optimize planning and productive efficiency (as opposed to establishing integrative land management techniques), which has limited the development of agroforestry [ 23 ]. To support the progress of industrial agriculture, governments designed national policies to promote specialization and intensification, which works to enforce this strategy of separation [ 24 ]. Industrial agriculture, however, is now associated with many negative social and environmental consequences [ 25 , 26 , 27 , 28 ]. Agroforestry has the potential to help address these consequences, and thus individuals familiar with agroforestry have started proposing and implementing a range of education and extension programs, financial incentives and cost-sharing initiatives, and support for the creation of markets for non-timber forest products to facilitate agroforestry adoption [ 16 , 21 , 29 , 30 ]. Such interventions have the potential to provide the incentives and support necessary to establish agroforestry as a thriving alternative land use strategy, by way of the following conceptual framework.

Figure  1 , developed and presented previously in [ 15 ], illustrates a generic theory of change which may underlie an effective agroforestry intervention. It identifies two initial preconditions: (1) successful mobilization and engagement of farmers; and (2) facilitating farmer capacity development and/or access to appropriate tree germplasm. The first of these and, in many cases, both, are required for significant and effective adoption of promoted agroforestry practices. Following such adoption, several intermediary outcomes are then expected. For example, farmers may see improved soil health and other ecosystem services, such as water infiltration, which then increase crop productivity or reduce production costs and, therefore, increase returns. Some participants may find that increased use and availability of tree/shrub fodder leads to increases in milk production and returns. Selling other agroforestry products such as timber, firewood, and fruit, can increase and diversify income and food sources [ 3 , 31 , 32 ]. These changes may have differential effects depending on gender, socio-economic status, race/ethnicity, or education/literacy level. Together, these intermediate outcomes are expected to bolster resilience to shocks, as well as boost overall household income and food security. These positive benefits along with features of the broader context in which participants operate will shape household investment in agroforestry. Our theory of change diagram presents positive pathways linking agroforestry interventions, adoption, and beneficial impacts; however, we also note that there are potentially negative tradeoffs to agroforestry, such as a reduction in area of crop production and negative tree-crop interactions.

Illustrative theory of change for an agroforestry (AF) intervention, as adapted from [ 15 ]

By mapping the existing evidence of agroforestry practices in high-income countries with their impacts on agricultural productivity, ecosystem services, and human well-being, we will create an easily-navigable database of relevant research related to agroforestry impacts as well as form a clearer picture on key areas of interest for further research. The results will encompass research from all high-income countries, which will allow policymakers to utilize knowledge gained from around the globe as well as make the study relevant to all developed nations.

Why this systematic map is important to do for high income countries

A large body of evidence around agroforestry has accumulated over the past three decades through research across the high-income countries (HICs) of the world [ 5 , 6 , 7 , 33 ]. These HICs are listed in Additional file 1 , as defined by the World Bank for the 2018 fiscal year [ 34 ]. Figure  2 provides a map showing the global HICs.

Geographic map showing high-income countries (HICs), as defined by [ 34 ]

To date, however, there has not been a comprehensive synthesis of evidence of what agroforestry practices and interventions have been effective, under what circumstances, and by what measures in HIC contexts. Recent literature reviews have given overviews of the evidence for the impacts of agroforestry on ecosystem services and environmental benefits, climate change adaptation and mitigation, carbon sequestration, biomass production, soil health, and food production [ 5 , 9 , 10 , 11 , 35 , 36 ]; however, they did not follow systematic review protocols. There are several recent efforts to systematically map and review aspects of agroforestry. Notably, one group mapped the evidence on agroforestry impacts on biodiversity and ecosystem services across Europe [ 7 , 37 ]. Other systematic reviews include aspects of agroforestry, such as a systematic map on the impacts of vegetated strips—including windbreaks, hedgerows, and shelterbelts—on nutrients, pollutants, socioeconomics, biodiversity, and soil retention in boreo-temperate systems [ 38 ]. Another study maps the impacts of Ecological Focus Area options (including agroforestry) in European farmed landscapes on climate regulation and pollination services [ 39 ]. Finally, we note that a systematic map of the effects of nature conservation on human well-being [ 40 ] and one on forests and poverty globally [ 41 ] include some studies on the impacts of agroforestry. We are not aware, however, of any effort to systematically map evidence on the impacts of agroforestry interventions and practices on the broad range of agricultural productivity, ecosystem services, and human well-being outcomes across HICs. Lack of such evidence synthesis constrains the ability of policymakers, practitioners, and researchers to make effective decisions relating to agroforestry.

Though it is easy to find examples of agroforestry practiced throughout the world, the initiatives to create policies and programs that formalize and promote agroforestry are relatively new in most HICs. International groups have invested in agroforestry projects in low- and middle-income countries (L&MICs) for decades (emerging in the 1960s and 1970s) as a solution to address environmental degradation, boost food security, and contribute to a range of other development policy objectives [ 3 , 42 ]. By contrast, agroforestry policy in the US, for instance, was first introduced in the mid-1980s (though promotion of windbreaks to reduce soil-erosion during the 1930s Dust Bowl era may be considered a precursor), with more formalized agroforestry policy emerging only in the 1990s with the Forest Stewardship Act of 1990 establishing a Center for Semiarid Agroforestry (renamed the National Agroforestry Center in 1994, broadening its scope to include the entire country). Similarly in the EU, agroforestry promotion began in the early 1990s with the 1992 reform of the EU Common Agricultural Policy (CAP), which formerly encouraged practices that discouraged farmers from integrating trees on farms [ 43 ]. Only within the last decade has there been a significant uptake of agroforestry projects in HICs in the context of institutionalized support for agroforestry as an alternative land use approach to address conservation and sustainable agricultural development objectives [ 2 ].

Major agroforestry initiatives in high-income countries include the USDA Agroforestry Strategic Framework Plan (FY 2011–2016) in the United States [ 16 ], the European Commission’s AGFORWARD program in Europe (FY 2014–2017) [ 21 ], Agriculture and Agri-Food Canada’s Agroforestry Development Centre in Canada [ 44 ], and the Farm Forestry National Action Statement and the Master TreeGrower Program (supported by the Australian Agroforestry Foundation) in Australia [ 45 ]. In Japan, the Satoyama Initiative includes agroforestry concepts, though it covers a broad range of practices [ 46 ].

One of the initial goals set out by these projects is to map out trees on farms and existing agroforestry practices within their respective countries. Several countries produced formalized documentation of the existing extent of agroforestry in their respective regions, such as the USDA in the United States [ 20 ] and the AGFORWARD project in Europe [ 47 ]. The USDA report, however, is limited to practices established with USDA technical and financial aid and a comprehensive mapping is yet to be completed and released. This SM will add to the toolset of resources supporting these initiatives by compiling existing knowledge of agroforestry impacts, identifying research needs, and making evidence accessible and customizable for diverse audiences. Furthermore, the SM will highlight any existing studies evaluating the impacts of these projects or any other agroforestry-related programs and policies in HICs.

There is evidence showing that agroforestry offers many ecological benefits—environmental, economic, and social—which give reason to incentivize and empower landowners to adopt such practices; however, it is also important to consider the evidence of the tradeoffs associated with agroforestry [ 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 23 , 48 ]. There is a growing interest in the potential of agroforestry and an increasing awareness of the role agroforestry can play in creating a diversified, multi-dimensional farming system [ 2 , 14 , 36 , 49 ]. Nevertheless, viewed in broader perspective, the integration of agroforestry into practice is still relatively low. For instance, the USDA estimates that agroforestry is applied on less than 1% of agricultural land with the potential for agroforestry through USDA assisted programs [ 20 ]. This SM will therefore provide important evidence synthesis that may support initiatives to disseminate agroforestry knowledge and promote broader adoption of agroforestry as an alternative land use strategy across different HIC contexts. Additionally, it will help to find evidence of potential tradeoffs that come with the establishment of agroforestry practices.

There are two primary audiences for this SM. First, we expect that researchers on agroforestry and broader sustainability issues will use the results to inform further investigations on these topics, including new empirical research, as well as systematic reviews of specific linkages and further evidence synthesis. Results should be of wide interest to researchers in a range of institutions, particularly national programs (USDA, AGFOWARD, etc.), national and regional agroforestry associations and extension programs, and universities. The second main anticipated audience is decision-makers for whom agroforestry is already or potentially of interest. This includes relevant government ministries and agencies, non-governmental organizations (NGOs), and other advocacy and implementing organization staff.

Stakeholder engagement

In developing the parallel L&MIC EGM, our team engaged with an advisory group comprised of 3ie members, donor agency staff, International Development Coordinating Group (IDCG) members and other evidence synthesis experts, International Centre for Research in Agroforestry (ICRAF) scientists and other agroforestry subject experts. We published the L&MIC EGM protocol with the Campbell Collaboration [ 15 ]. In preparing that protocol, we coordinated with the advisory group as well as colleagues involved in two related evidence maps [ 40 , 50 ], and we presented the work at several conferences with opportunities for discussion, see [ 15 ]. The HIC SM protocol was presented as a poster presentation at the Green Lands Blue Waters conference in Madison, Wisconsin in November 2017 and discussed with interested agroforestry experts. Feedback and suggestions given to the authors were incorporated into the HIC and L&MIC protocols. Finally, we expect to engage with additional reviewers through our efforts to publish this protocol and the resultant SM in peer-reviewed journals.

Objective of the map

The primary aim of this systematic map is to identify, map and describe existing evidence on the effects of agroforestry practices and interventions on agricultural productivity, ecosystem services, and human well-being in HICs.

In doing so, it addresses the following research questions:

What are the extents and characteristics of empirical evidence on the effects of agroforestry practices and interventions on agricultural productivity, ecosystem services, and human well-being in HICs?

What are the major gaps in the primary evidence base?

What are the agroforestry practice/intervention and outcome areas with potential for evidence synthesis?

To address these research questions, the scope is defined by the Population (Subject), Intervention (or Practice), Comparator, and Outcome (PICO) components to be examined, which are presented in Table  3 .

The methods for the searches, screening, and eligibility criteria replicates those used for the L&MIC EGM [ 15 ], with modifications to adapt the process to account for differences between HIC and L&MIC concepts of agroforestry.

Search strategy

We will undertake a comprehensive search across 5 bibliographic databases and 24 organizational websites for grey literature to best capture an unbiased representation of existing literature. Studies from January 1, 1990 to the time of the search (mid-2018) will be included in the search. We begin the study period in 1990 as this is roughly the time that HICs saw increased support for agroforestry and other approaches designed to further environmental goals, as discussed earlier. The search will be done through use of search engines, based on key words within the identified databases. When such a strategy is not possible (e.g. for some topical databases and organizational websites), hand searches will be performed to extract all potentially relevant studies. Due to resource constraints, the focus will be on studies published in English, which we acknowledge as a limitation of the comprehensiveness of this study.

The bibliographic databases that will be searched for publications are:

EBSCO: Agricola, Econlit

Web of Science: Core Collection

CAB Abstracts and Global Health

The search terms to be used in full to conduct a topic search in the Web of Science: Core Collection and CAB Abstracts and Global Health databases are presented in Table  4 . The search string includes each of the agroforestry practices from Table  1 . We note that the intervention types are more generic, including topics well beyond agroforestry. Our search terms will therefore focus on practices, but in doing this, we capture the range of relevant interventions studied as well. We include relevant study area terms (study country terms) to limit the number of search results returned, and this decision did not affect our study retrieve performance, as detailed in the section on Assessing Retrieval Performance below. We make use of the Boolean operators and wildcards where possible, as shown in Table  4 and described in further detail in Additional file 2 . Search strings are simplified for databases that have limited characters or lack Boolean functionality. The search strings used for each of the databases are detailed in Additional file 2 .

Additionally, to identify the existing grey literature, the websites of various organizations that are likely to produce published and unpublished research will be searched, using the search terms from We include relevant study area terms (study country terms) to limit the number of search results returned, and this decision did not affect our study retrieve performance, as detailed in the section on Assessing Retrieval Performance below. We make use of the Boolean operators and wildcards where possible, as shown in Table  4 and described in further detail in Additional file 2 . Search strings are simplified for databases that have limited characters or lack Boolean functionality. The search strings used for each of the databases are detailed in Additional file 2 .

The list of relevant research organizations (Table  5 ) has been constructed from cross-validation of websites listed in the systematic mapping protocols of agroforestry related studies [ 41 , 51 ]. To optimize the scope of the search while ensuring transparency in our methods, we will follow the approach developed by Haddaway et al. [ 52 ], which will allow us to search multiple websites simultaneously and to extract the relevant information from each website into a single database.

A search of literature through web-based search engines will also be performed. A search in Google Scholar, using the search terms from Table  4 will be performed and the first 300 results sorted by relevance will be reviewed, following the findings from Haddaway et al. [ 53 ]. The search string for Google Scholar will be simplified to not include the list of high-income countries (relevant study locations). The online literature review and reference management software, EPPI-Reviewer 4, will be used to upload relevant titles and abstracts for candidate studies identified through the search strategy. We will create a project workspace using Box ( https://www.box.com/home ; accessed 2 September 2018) to assist in organizing and managing documentation files as well as a project workspace using Slack ( https://slack.com/ ; accessed 2 September 2018; [ 54 ]) to manage communication on decisions, changes, and questions and provide a platform for all team members to have access to all relevant documents.

Assessing retrieval performance

The comprehensiveness of the search string was evaluated based on a test list of studies that meet the eligibility criteria. This test list consists of 44 studies, of which 22 are impact studies (18 primary studies, 4 systematic meta-analyses, and 1 farmer-managed field trial) and 22 researcher-managed field trials (which would not be included in this systematic map, per above, but which we would like to identify in the screening process for future work). The list was created based on personal knowledge and a snowballing method reviewing bibliographies and citations of known agroforestry papers. The test list was formed independently, without using the search string. The search string was tested and modified as needed by running it against this test list. The test list and results of the scoping process are reported in Additional file 2 . The search string retrieved 43 of the 44 test studies (97.7% of the studies in the test list), which was deemed acceptable.

We designed our search string to balance between specificity (proportion of relevant information) and sensitivity (finding all relevant information), per [ 55 ]. Our decision to include the country search terms is to avoid duplication of efforts from the parallel L&MIC EGM study and reduce the number of studies retrieved by our search to make the study feasible. We reviewed a sample of 500 studies omitted and found that the decision to include country terms omits primarily studies relating to L&MICs along with studies not relevant to our SM. Of the 500 omitted studies reviewed, we only found two relevant for inclusion (0.4%). Limiting the search by country terms reduces returned results from 92,293 to 30,014 (see Additional file 2 ). Furthermore, our retrieval rate of our test list studies does not change when the countries are removed from the search string (the one study that was missed did not contain any agroforestry-related terms that could be added to the search string). The topic search in Web of Science searches for the search terms in the title, abstract, keywords, and Web of Science KeyWords Plus. We found that with this type of topic search, a country term is almost always picked up, returning a sufficient percentage of the body of HIC agroforestry literature. Our assessment of study retrieval performance gives us confidence that we are capturing the majority of literature while not extending the scope of our study beyond feasibility.

Article screening and study eligibility criteria

We will first review search results at the level of title and abstract to determine inclusion or exclusion. Any study that we are unsure of whether it should be included or not during the title and abstract stage will be included for full-text review. We will keep a full list of excluded studies and record reasons for exclusion for each. Studies that meet the eligibility criteria at both the title and abstract stages will be reviewed at the full text stage. Those excluded at this stage will also be recorded along with reasons for the exclusion. A full list of studies excluded at full-text will be provided with the systematic map, along with the reasons for exclusion.

We will use double screening for a small subset of 100 training studies at the title and abstract stage and then use the approach in Snilstveit et al. for securing agreement among coders [ 56 ]. We will use a training set consisting of 100 studies randomly selected from an initial search using our search string in Web of Science to assess agreement among coders. The reviewers will discuss any discrepancies between coding for this subset to reach agreement. Based on a training set of studies screened by all reviewers, inter-rater reliability will be calculated using a Kappa statistic for all studies double screened at title and abstract levels [ 57 ]. If the Kappa test agreement falls below 0.6, indicating moderate agreement, an additional reviewer will be consulted and an additional set of 100 test studies will be screened by all reviewers, as in [ 39 , 51 ].

During the screening process, when a rater is uncertain about study eligibility, the relevant study will be marked for a second opinion and screening by a second rater will be conducted. The lead reviewer will check the consistency of coding periodically throughout the coding process for a subset of studies at the title and abstract screening phase and at the full-text screening stage. At both the title and abstract screening phase and the full-text data extraction stage, a subset of 10% of the studies will be assessed by at least two reviewers. Studies where there is inconsistency or disagreement will be marked as “Re-evaluate” in EPPI-Reviewer 4 and will be discussed by reviewers to reach agreement.

Eligibility criteria

Relevant type of study.

Given that we seek to provide a resource for decision-makers, as well as identify gaps and well-researched areas in the current evidence base, we will include both primary studies and systematic reviews. Primary studies that measure the effect of agroforestry practices and interventions on the different outcomes of interest will be included, as will systematic reviews of the literature that synthesize and analyze these same relationships. We will include English-language studies conducted from 1990 onwards, through until the time of search (mid-2018).

Included studies must explicitly examine the outcomes of specific agroforestry practices and interventions on farm settings. Further, they must use a comparator, which may be temporal, spatial, between group, or some combination of these (see below). We will exclude theoretical or modeling studies (unless they include a relevant empirical example with design that meets eligibility criteria), and editorials and commentaries. Experimental trials managed by researchers will not be included due to time and resource constraints and since the population of interest for this systematic map is farmers and farmer’s land. These experimental off-farm trials, however, will be excluded into a separate bin in EPPI-Reviewer 4 and be available as a base for future work and synthesis. On-farm field trials will be included if all other eligibility criteria are met.

Relevant types of study design

We will include four kinds of studies: (1) quantitative impact evaluations, (2) systematic reviews, (3) on-farm field trials that test specific agroforestry techniques and approaches, and (4) observational studies on the effect of agroforestry practices.

Impact evaluations are studies that measure changes that occur due to an intervention. Such studies will use an experimental or quasi-experimental study design to conduct a counterfactual analysis to allow for attribution of changes in an outcome to a specific intervention, or compare the effects of different types of programs [ 58 ]. Specifically, we will include the following types of impact evaluation studies:

Studies where participants or sites/plots of land (farmers, or land management areas on a farmer’s land) are randomly assigned to treatment and comparison group (experimental study designs);

Studies where assignment to treatment and comparison groups is based on other known allocation rules, including a threshold on a continuous variable (regression discontinuity designs) or exogenous geographical variation in the treatment allocation (natural experiments);

Studies with non-random assignment to treatment and comparison group that include pre-and post-test measures of the outcome variables of interest to ensure equity between groups on the baseline measure, and that use appropriate methods to control for selection bias and confounding. Such methods include statistical matching (for example, propensity score matching, or covariate matching), regression adjustment (for example, difference-in-differences, fixed effects regression, single difference regression analysis, instrumental variables, and ‘Heckman’ selection models).

Studies with non-random assignment to treatment and comparison group that include post-test measures of the outcome variables of interest only and use appropriate methods to control for selection bias and confounding, as above.

Ideally, studies would include baseline and post-intervention data, but due to our expectation of a small number of studies meeting this criterion, we will include studies with post-intervention outcome data only as long as they use some method to control for selection bias and confounding factors.

Reviews examine the effects of different interventions using transparent and systematic methods to identify, appraise and synthesize findings from studies addressing a specific issue [ 59 ]. We will include systematic reviews, meta-analyses, and literature reviews that describe methods used for search, data collection, and synthesis. Literature reviews that do not describe methods used for search, data collection and synthesis will not be included; however, they will be identified and included in a separate bin in EPPI-Reviewer 4 and made available as a list with the report. Systematic maps and evidence gap maps identified where agroforestry is an area of interest will also be noted in the final report.

Field trials in agroforestry are designed to test the effects of experimental treatments or other variables on crop yield or other outcomes of interest in conditions similar to the actual growing conditions experienced by farmers who may adopt the treatment [ 14 , 60 ]. While impact evaluations measure the changes due to an intervention, field trials measure the changes due to a practice. As for agronomy more generally, field trials can be divided into three types: (1) Researcher managed and researcher implemented; (2) researcher managed and farmer implemented, and (3) farmer managed and farmer implemented. We will include only studies implemented on a farmer’s land, and only if they pertain directly to some aspect of agroforestry, include an experimental research design, and describe the effects of an intervention, technique, or practice on an outcome category relevant to the current study.

Finally, we will include observational studies on the effects of agroforestry practices, provided they are quantitative and include at least one comparison as described below (e.g. before/after; study group/non-study group). We include such studies given that we anticipate a number of potentially interesting studies will not examine the impacts of an agroforestry intervention per se, but a specific practice or set of practices.

The SM will include both completed and ongoing studies, and the ongoing studies will be coded as ongoing and not yet completed.

Relevant subject

The subject of interest will be farms and/or the people that live and farm on them that are incorporating any agroforestry practices into their farming system within the high-income countries (Additional file 1 ).

Relevant intervention or practice

Our study will capture studies evaluating the impacts of interventions to support agroforestry as well as those evaluating agroforestry practices alone. From a policy perspective, it is especially useful to know what kinds of interventions might most effectively promote agroforestry practices to yield desired social-ecological outcomes. Although impact evaluations on agroforestry-related interventions (Table  2 ) are of particular interest for policy-makers, our study will also include studies on the impacts of specific agroforestry practices (Table  1 ) without a policy intervention, which will broadly capture the impacts of agroforestry practices. This SM will therefore include any study that meets our criteria that evaluates the impacts of one or more agroforestry practice or intervention. In our map, we will indicate studies that include an evaluation of an agroforestry-related intervention, versus studies that evaluate the impact of only an agroforestry practice without a policy intervention. We will conduct analyses on the body of agroforestry practice impact studies as well as on the body of studies evaluating the impacts of specific agroforestry interventions.

Relevant comparator

Farm or household that does not adopt a given practice identified in Table  1 , or is not exposed to a specific agroforestry intervention,

Farm or household before adopting a given agroforestry practice, or being exposed to a specific agroforestry intervention,

Farm or household that adopts a different agroforestry practice, and/or that is exposed to a different specific agroforestry intervention,

Primary forests, secondary forests, or managed forestry/plantations that not exposed to a specific agroforestry intervention,

A combination of two or more of the above. We will not include studies that only compare agroforestry practices with other agroforestry practices (i.e. studies that only evaluate different implementation of the same agroforestry practice, or studies that only evaluate multiple types of agroforestry practice).

Relevant outcomes

The columns of the SM matrix will be comprised of three broad outcome categories: (1) agricultural productivity, (2) ecosystem services, and (3) human well-being.

Studies that focus exclusively on the adoption of a particular agroforestry technique or species without reference to impact will be excluded. We will, however, note the number of adoption-related studies (and their geographic location) excluded due to lack of evidence on outcomes. The primary outcomes are the three stated above (agricultural productivity, ecosystem services, and human well-being), and secondary outcomes are adoption and behavior change, which will only be reported if the study also reports primary outcomes.

Specific outcome categories under agricultural productivity will comprise farm productivity, including yield, and profitability.

Ecosystem services outcomes will first be classified under three broad categories: (a) provisioning, (b) regulation and maintenance, and (c) cultural services. Outcomes will be further divided into a number of specific categories following the Common International Classification of Ecosystem Services (CICES) developed by the European Environment Agency [ 61 ] and presented in Table  6 . CICES builds from the seminal Millennium Ecosystem Assessment [ 62 ], The Economics of Ecosystems and Biodiversity [ 63 ], and other ecosystem services classification schemes.

For human well-being, the final broad outcome we will examine, we adapt the classification published in [ 40 ] to identify a set of key policy-relevant domains of human well-being (Table  7 ). Based on likely policy interest and goals typically articulated by proponents of agroforestry, we will focus on five dimensions of human well-being: income and household expenditure, housing and material assets, food security and nutrition, health, and cultural and subjective well-being. We will also include the category of “other” which may group some studies focusing on the other dimensions of human well-being identified in McKinnon et al. [ 40 ]. In this last category, we will note in particular any mention of adaptive capacity or resilience, especially with reference to the impacts of climate change.

We will present the three outcomes in the SM main matrix in two ways: (1) a simplified typology of broad agroforestry practice/intervention and outcome categories and (2) a more detailed version with the specific agroforestry practice/intervention and outcome categories.

Types of settings

We expect that the agroforestry interventions and outcomes will take place in a range of settings in HICs. These settings will cover a range of ecoregions and are likely to be primarily rural, but potentially also urban areas (e.g. city gardens). We also expect much of the evidence to pertain to smallholders, but some may describe agroforestry practices among larger landholders.

Study quality assessment

Systematic maps do not tend to provide much information on study quality, but rather simply provide the broad overview of knowledge and highlight areas where there is the potential for further review and literature quality assessment [ 55 ]. Therefore, we will not conduct study quality assessments on the studies included in this SM.

However, our study will include information about type of study design, referring to the types of study design presented above, including quantitative impact evaluations (experimental or quasi-experimental), systematic reviews, on-farm field trials (farmer-managed or researcher-managed), and observational studies on the effect of agroforestry practices. Furthermore, the type of quasi-experimental methods used, if applicable, will be documented. This data is not intended to offer an assessment of study quality, but rather provide basic information to get a broad perspective of the type of research being conducted in each area of the typology. As in the L&MIC EGM, we will break our results into three sections: a discussion of all included primary studies, a discussion of the subset consisting of only quantitative impact evaluation studies on interventions, and a discussion of included systematic reviews. We will present the distribution of study types for included studies and provide a list of all studies included at full-text with their assigned study type.

We do not expect to find any included studies authored by the coders of this systematic map. However, in the case that a study authored by one of the reviewers is included, those involved with authoring the studies will not be involved in decisions regarding inclusion or critical appraisal of that study.

Data coding strategy

Our research team will be led by the first author of this protocol (SEB), and the point of contact for any disputes on coding strategy will be the second author (DCM). The research team will consist of SEB, DCM, and between two and three hired research assistants, who are students at the University of Illinois at Urbana-Champaign. We will use a standardized data extraction form, attached as Additional file 3 , to extract descriptive data from all studies meeting our eligibility criteria. We will create a codebook describing the scope of each question in the data extraction form. We will conduct a pilot with a small subset of studies by everyone in the research team to ensure consistency and to resolve any issues or ambiguities. Given the likely volume of studies (based on previous SR and SM experience such as in [ 15 , 40 ]), we do not plan to carry out extensive side-by-side double extraction of data at the full text stage. Instead, we will conduct random spot checks of a small percentage of included articles to ensure consistency between raters. We will measure consistency using percent disagreement of spot-checking with the primary rater. We note that in our test studies, we found that some studies only specify a general practice (silvoarable, silvopasture, etc.) without detailing a specific practice (alley-cropping, improved fallow, riparian buffer strips, etc.). We address this in our data extraction spreadsheet by allowing a selection of “not specified” for the practice type.

Study mapping and presentation

We expect to perform several analyses based on the data collected and to summarize results visually and in various written forms to effectively communicate with intended audiences. A final report will present the map as a detailed data set with figures of descriptive statistics derived from the data set, as in [ 64 ]. We intend to publish our systematic map in Environmental Evidence and upload the dataset online as an open-access, interactive site, as in [ 41 ]. To communicate our results and visualize our data, we intend to create at minimum the following:

We will create a flow diagram of the systematic mapping process, detailing the number of studies returned by our search, included and excluded at each stage, and the number of studies included at the final data extraction stage.

We will create a tabular visualization reflecting agroforestry practices on the intervention axis and outcomes of the interventions on the outcome axis.

We will create a second tabular visualization reflecting agroforestry interventions on the intervention axis and outcomes of the interventions on the outcome axis.

We will also show the distribution of studies for each country on a geographic map, as in [ 40 , 50 , 64 ].

We will provide descriptive statistics on geographical distribution of study location by country and world region, the type of studies, and quality of the SRs.

We will visually present the included studies in a matrix. The matrix will be stylized as a topography that notes whether the study is a review/SR, an impact evaluation of agroforestry interventions, or if the paper studies the impacts of specific agroforestry practices.

Based on these maps, we will perform gap analysis to identify areas for systematic review or primary research.

We will create heatmaps as in [ 64 ] to visualize and identify knowledge gaps and clusters.

We will upload our dataset online on an open-access, interactive map server, as in [ 41 ]. Users will be able to visualize our results, filter the dataset per our data coding criteria, and automatically interact with the most updated version.

Furthermore, the reviewers will formally discuss and collectively identify areas of knowledge gaps and clusters of higher-quality literature based on their experience from screening full-texts after coding is completed. We will also comment specifically on the extent to which the literature examines interventions vs. specific practices or both simultaneously.

Smith J, Pearce BD, Wolfe MS. Reconciling productivity with protection of the environment: is temperate agroforestry the answer? Renew Agric Food Syst. 2012;28:80–92.

Article   Google Scholar  

Jose S, Gold MA, Garrett HE. The future of temperate agroforestry in the United States. In: Nair PKR, Garrity D, editors. Agroforestry—the future of global land use. Dordrecht: Springer Netherlands; 2012. p. 217–45.

Chapter   Google Scholar  

Waldron A, Garrity D, Malhi Y, Girardin C, Miller DC, Seddon N. Agroforestry can enhance food security while meeting other sustainable development goals. Trop Conserv Sci. 2017;10:1–6.

Garrity DP. Agroforestry and the achievement of the millennium development goals. Agrofor Syst. 2004;61:5–17.

Google Scholar  

Jose S. Agroforestry for ecosystem services and environmental benefits: an overview. Agrofor Syst. 2009;76:1–10.

Jose S, Bardhan S. Agroforestry for biomass production and carbon sequestration: an overview. Agrofor Syst. 2012;86:105–11.

Fagerholm N, Torralba M, Burgess PJ, Plieninger T. A systematic map of ecosystem services assessments around European agroforestry. Ecol Ind. 2016;62:47–65.

Wilson M, Lovell S. Agroforestry—the next step in sustainable and resilient agriculture. Sustainability. 2016;8:574.

Holzmueller EJ, Jose S. Biomass production for biofuels using agroforestry: potential for the North Central Region of the United States. Agrofor Syst. 2012;85:305–14.

Tsonkova P, Böhm C, Quinkenstein A, Freese D. Ecological benefits provided by alley cropping systems for production of woody biomass in the temperate region: a review. Agrofor Syst. 2012;85:133–52.

Udawatta RP, Jose S. Agroforestry strategies to sequester carbon in temperate North America. Agrofor Syst. 2012;86:225–42.

Buck LE. Agroforestry policy issues and research directions in the US and less developed countries: insights and challenges from recent experience. Agrofor Syst. 1995;30:57–73.

Lassoie JP, Buck LE, Current D. The development of agroforestry as an integrated land use management strategy. In: Gene Garrett HE, editor. North American agroforestry: an integrated science and practice. 2nd ed. Washington: American Society of Agronomy; 2009. p. 1–24.

Lovell ST, Dupraz C, Gold M, Jose S, Revord R, Stanek E, Wolz KJ. Temperate agroforestry research: considering multifunctional woody polycultures and the design of long-term field trials. Agrofor Syst. 2018;92:1397–415.

Miller DC, Ordonez PJ, Baylis K, Hughes K, Rana P. Protocol for an evidence and gap map. The impacts of agroforestry on agricultural productivity, ecosystem services, and human well-being in low- and middle-income countries: an evidence and gap map. In: Campbell collaboration; 2017.

USDA. USDA agroforestry strategic framework, fiscal year 2011–2016. Washington, D.C.: U. S. Department of Agriculture; 2011.

Leakey RRB. Definition of agroforestry revisited. Agrofor Today. 1996;8:5–7.

Atangana A, Khasa D, Chang S, Degrande A. Definitions and classification of agroforestry systems. In Tropical agroforestry. Dordrecht: Springer; 2014. p. 35–47.

Huxley P, van Houten H. Glossary for agroforestry. Nairobi: World Agroforestry Center (ICRAF); 1997.

USDA. Agroforestry USDA reports to America, fiscal years 2011–2012—comprehensive version. Washington, D.C.: Department of Agriculture; 2013.

Mosquera-Losada MR, Santiago-Freijanes J, Pisanelli A, Rois M, Smith J, Herder M, Moreno G, Malignier N, Mirazo JR, Lamersdorf N, et al. Extent and success of current policy measures to promote agroforestry across Europe. 2016.

Nair PKR, Garrity D. Agroforestry—the future of global land use. 2012.

Garrett HE, Buck L. Agroforestry practice and policy in the United States of America. For Ecol Manag. 1997;91:5–15.

IPES-Food. From uniformity to diversity: a paradigm shift from industrial agriculture to diversified agroecological systems. International Panel of Experts on Sustainable Food Systems; 2016.

Matson PA, Parton WJ, Power AG, Swift MJ. Agricultural intensification and ecosystem properties. Science. 1997;277:504–9.

Article   CAS   Google Scholar  

Tilman D, Cassman KG, Matson PA, Naylor R, Polasky S. Agricultural sustainability and intensive production practices. Nature. 2002;418:671.

Lobao L, Meyer K. The great agricultural transition: crisis, change, and social consequences of twentieth century US farming. Ann Rev Sociol. 2001;27:103–24.

Foley JA, DeFries R, Asner GP, Barford C, Bonan G, Carpenter SR, Chapin FS, Coe MT, Daily GC, Gibbs HK, et al. Global consequences of land use. Science. 2005;309:570–4.

Valdivia C, Barbieri C, Gold MA. Between forestry and farming: policy and environmental implications of the barriers to agroforestry adoption. Can J Agric Econ. 2012;60:155–75.

Jacobson M, Kar S. Extent of agroforestry extension programs in the United States. J Ext. 2013;51:4RIB4.

Mbow C, Van Noordwijk M, Luedeling E, Neufeldt H, Minang PA, Kowero G. Agroforestry solutions to address food security and climate change challenges in Africa. Curr Opin Environ Sustain. 2014;6:61–7.

Sharma N, Bohra B, Pragya N, Ciannella R, Dobie P, Lehmann S. Bioenergy from agroforestry can lead to improved food security, climate change, soil quality, and rural development. Food Energy Secur. 2016;5:165–83.

Garrett HE. North American agroforestry: an integrated science and practice. Washington: American Society of Agronomy; 2009.

Book   Google Scholar  

World Bank Country and Lending Groups. https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups . Accessed 28 June 2018.

Wolz KJ, Lovell ST, Branham BE, Eddy WC, Keeley K, Revord RS, Wander MM, Yang WH, DeLucia EH. Frontiers in alley cropping: transformative solutions for temperate agriculture. Glob Chang Biol. 2018;24:883–94.

Dollinger J, Jose S. Agroforestry for soil health. Agrofor Syst. 2018;92:213–9.

Torralba M, Fagerholm N, Burgess PJ, Moreno G, Plieninger T. Do European agroforestry systems enhance biodiversity and ecosystem services? A meta-analysis. Agric Ecosyst Environ. 2016;230:150–61.

Haddaway NR, Brown C, Eales J, Eggers S, Josefsson J, Kronvang B, Randall NP, Uusi-Kämppä J. The multifunctional roles of vegetated strips around and within agricultural fields. Environ Evid. 2018;7:14.

Ottoy S, Angileri V, Gibert C, Paracchini ML, Pointereau P, Terres J-M, Van Orshoven J, Vranken L, Dicks LV. Impacts of selected ecological focus area options in European farmed landscapes on climate regulation and pollination services: a systematic map protocol. Environ Evid. 2018;7:10.

McKinnon MC, Cheng SH, Dupre S, Edmond J, Garside R, Glew L, Holland MB, Levine E, Masuda YJ, Miller DC, et al. What are the effects of nature conservation on human well-being? A systematic map of empirical evidence from developing countries. Environ Evid. 2016;5:8.

Cheng SH, Ahlroth S, Onder S, Shyamsundar P, Garside R, Kristjanson P, McKinnon MC, Miller DC. What is the evidence for the contribution of forests to poverty alleviation? A systematic map protocol. Environ Evid. 2017;6:10.

Garrity DP, Akinnifesi FK, Ajayi OC, Weldesemayat SG, Mowo JG, Kalinganire A, Larwanou M, Bayala J. Evergreen agriculture: a robust approach to sustainable food security in Africa. Food Secur. 2010;2:197–214.

Mosquera-Losada MR, Moreno G, Pardini A, McAdam JH, Papanastasis V, Burgess PJ, Lamersdorf N, Castro M, Liagre F, Rigueiro-Rodríguez A. Past, Present and Future of Agroforestry Systems in Europe. In: Nair PKR, Garrity D, editors. Agroforestry—the future of global land use. Dordrecht: Springer Netherlands; 2012. p. 285–312.

Thevathasan NV, Gordon AM, Bradley R, Cogliastro A, Folkard P, Grant R, Kort J, Liggins L, Njenga F, Olivier A, et al. Agroforestry research and development in Canada: the way forward. In: Nair PKR, Garrity D, editors. Agroforestry—the future of global land use. Dordrecht: Springer Netherlands; 2012. p. 247–83.

Reid R. Developing farmer and community capacity in agroforestry: is the Australian Master TreeGrower program transferable to other countries? Agrofor Syst. 2016;91:847–65.

Takeuchi K. Rebuilding the relationship between people and nature: the Satoyama initiative. Ecol Res. 2010;25:891–7.

den Herder M, Moreno G, Mosquera-Losada RM, Palma JHN, Sidiropoulou A, Santiago Freijanes JJ, Crous-Duran J, Paulo JA, Tomé M, Pantera A, et al. Current extent and stratification of agroforestry in the European Union. Agric Ecosyst Environ. 2017;241:121–32.

Montambault JR, Alavalapati JRR. Socioeconomic research in agroforestry: a decade in review. Agrofor Syst. 2005;65:151–61.

Jordan N, Warner KD. Towards multifunctional agricultural landscapes for the Upper Midwest Region of the USA. In: Steve Wratten HS, Ross C, Robert C, editors. Ecosystem services in agricultural and urban landscapes. New York: Wiley; 2013. p. 139–56.

Cheng SH, MacLeod K, Ahlroth S, Onder S, Perge E, Shyamsundar P, Rana P, Garside R, Kristjanson P, McKinnon MC, Miller DC. What is the contribution of forests to poverty alleviation? A systematic map of global evidence. Environmental Evidence in review.

Haddaway NR, Brown C, Eggers S, Josefsson J, Kronvang B, Randall N, Uusi-Kämppä J. The multifunctional roles of vegetated strips around and within agricultural fields. A systematic map protocol. Environ Evid. 2016;5:18.

Haddaway NR, Collins AM, Coughlin D, Kirk S. A rapid method to increase transparency and efficiency in web-based searches. Environ Evid. 2017;6:1.

Haddaway NR, Collins AM, Coughlin D, Kirk S, Larsen P, Ins MV, Pautasso M, Noorden R, Khabsa M, Giles C, et al. The role of google scholar in evidence reviews and its applicability to grey literature searching. PLoS ONE. 2015;10:e0138237.

Perkel JM. How scientists use Slack. Nat News. 2017;541:123.

James KL, Randall NP, Haddaway NR. A methodology for systematic mapping in environmental sciences. Environ Evid. 2016;5:7.

Snilstveit B, Stevenson J, Villar PF, Eyers J, Harvey C, Panfil S, Puri J, McKinnon MC. Land-use change and forestry programmes: evidence on the effects on greenhouse gas emissions and food security. In: Evidence gap map report 3. London; 2016.

Cohen J. A coefficient of agreement for nominal scales. Educ Psychol Meas. 1960;20:37–46.

Ferraro PJ. Counterfactual thinking and impact evaluation in environmental policy. New Dir Eval. 2009;2009:75–84.

Waddington H, White H, Snilstveit B, Hombrados JG, Vojtkova M, Davies P, Bhavsar A, Eyers J, Koehlmoos TP, Petticrew M, et al. How to do a good systematic review of effects in international development: a tool kit. J Dev Eff. 2012;4:359–87.

Nair PKR. State-of-the-art of agroforestry research and education. Agrofor Syst. 1993;23:95–119.

The Common International Classification of Ecosystem Services (CICES). http://cices.eu . Accessed 28 June 2018.

Millennium Ecosystem Assessment. Ecosystems and human well-being: synthesis. Washington, DC: Island Press; 2005.

Kumar P. The economics of ecosystems and biodiversity (TEEB) ecological and economic foundations. London, Washington: Earthscan; 2010.

Miller D, Ordonez P, Brown S, Forrest S, Nava N, Hughes K, Baylis K. The impacts of agroforestry on agricultural productivity, ecosystem services, and human well-being in low- and middle-income countries: an evidence gap map. In: 3ie evidence gap map report. London: International Initiative for Impact Evaluation (3ie); (in review) .

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Authors’ contributions

All authors developed the original idea, participated in discussions defining the search and eligibility criteria and data coding strategy, and decided the study mapping and presentation design. SEB contributed to developing the background section and testing the search criteria. DCM, PJO, and KB developed the theory of change. PJO and SEB developed the data coding table. SEB had the lead of writing the manuscript, with contributions from all authors. All authors read and approved the final manuscript.

Acknowledgements

We thank Karl Hughes and Pushpendra Rana for helping shape this protocol through their development of a parallel project creating an Evidence Gap Map (EGM) for agroforestry impacts in low- and middle- income countries (L&MICs), supported by 3ie [ 15 ]. We also thank Festus Amadu and Katia Nakamura for comments on an earlier version of this protocol. Finally, we thank the editor of Environmental Evidence, Dr. Andrew Pullin, and three anonymous reviewers for comments on earlier drafts of this manuscript that have greatly improved this protocol.

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The authors declare that they have no competing interests.

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This project is funded by the USDA National Institute of Food and Agriculture, Hatch Project #1009327.

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Additional files

Additional file 1..

List of high-income countries (according to the World Bank 2018 fiscal year classification [ 34 ]). Tabular list of high-income countries according to the World Bank 2018 fiscal year classification.

Additional file 2.

Test studies and scoping results. List of test studies and results of search string scoping process.

Additional file 3.

SM data extraction spreadsheet. Data extraction spreadsheet describing the data coding components for creating the systematic map.

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Brown, S.E., Miller, D.C., Ordonez, P.J. et al. Evidence for the impacts of agroforestry on agricultural productivity, ecosystem services, and human well-being in high-income countries: a systematic map protocol. Environ Evid 7 , 24 (2018). https://doi.org/10.1186/s13750-018-0136-0

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Received : 01 May 2018

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Published : 29 October 2018

DOI : https://doi.org/10.1186/s13750-018-0136-0

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Climate change influences on agricultural research productivity

• Published: 20 April 2013
• Volume 119 , pages 815–824, ( 2013 )

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• Xavier Villavicencio 1 ,
• Bruce A. McCarl 1 ,
• Ximing Wu 1 , 2 &
• Wallace E. Huffman 3  

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This paper investigates the impacts of climate change on US returns to research investments on agricultural productivity. We examine this using a historical data set in a panel time-series econometric model of state agricultural productivity. The fitted model allows derivation of the rate of return to research investments and the effects of climate change thereon. We find climate change is altering the rate of return to public agricultural research in a spatially heterogeneous manner. Increases in precipitation raise returns to research, while the impact of higher temperatures varies by region, are negative in Southern areas, particularly the Southern Plains, and positive in northern areas. We simulate the impact of projected climate change and find cases where agricultural productivity is reduced, for example in the Southern Plains. Finally, we consider the amount of research investment that is needed to adapt to overcome the impacts of climate change on agricultural productivity. Under the 2100 scenario, a 7–17 % increase in total US research investment is needed to adapt, but effects by region differ greatly—some requiring little changes and the Southern Plain requiring an increase as high as 57 %.

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Google Scholar  

Ball VE, Butault J-P, Nehring R (2002) U.S. Agriculture, 1960-96: a multilateral comparison of total factor productivity. In: Ball VE, Norton GW (eds) Agricultural productivity: measurement and sources of Growth. Kluver Academic Publication, Boston, pp 11–36

Chapter   Google Scholar  

Evenson RE (2001) Economic impacts of agricultural research and extension. In: Gardner BL, Rausser G (eds) Handbook of agricultural economics, vol 1. Elsevier Science, pp 574–628

Hamilton JD (1994) Time series analysis. Princeton University Press, Princeton

Huffman WE (2010) Measuring Public Agricultural Research Capital and Its Impact on State Agricultural Productivity in the U.S. Iowa State University, Department of Economics, Staff Paper #09022, Revsied Aug. 2010

Huffman WE, Evenson RE (1993) Science for agriculture: a long-term perspective. Iowa State University Press, Ames

Huffman WE, Evenson RE (2006a) Do formula or competitive grant funds have greater impacts on state agricultural productivity? Am J Agric Econ 88(4):783–798

Article   Google Scholar  

Huffman WE, Evenson RE (2006b) Science for agriculture: a long-term perspective, 2nd edn. Blackwell Publishing, Ames

Book   Google Scholar  

Huffman WE, Just RE (1994) An empirical analysis of funding, structure, and management of agricultural research in the United States. Am J Agric Econ 76:744–759

Huffman WE, McCunn A, Xu J (2006) Public agricultural research with an agricultural productivity emphasis. Data for 48 States, 1927–1995. Iowa State University, Department of Economics, Staff Paper

IPCC (2007) Climate change 2007: fourth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge

James JS, Alston JM, Pardey PG, Andersen MA (2009) Structural changes in U.S. agricultural production and productivity. Choices, 4th Quarter 2009 | 24(4)

McCarl BA (2007) Adaptation Options for Agriculture, Forestry and Fisheries, A report to the UNFCCC Secretariat Financial and Technical Support Division, http://unfccc.int/files/cooperation_and_support/financial_mechanism/application/pdf/mccarl.pdf

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Pardey PG, James J, Alston J, Wood S, Koo B, Binenbaum E, Hurley T, Glewwe P (2007) Science, Technology and Skills. Background Paper for the World Bank’s World Development Report 2008. St. Paul, Rome and Washington D.C.: University of Minnesota, CGIAR Science Council and World Bank

Villavicencio X (December 2009) Essays on the effect of climate change over agriculture and forestry. Ph.D. Dissertation, Texas A&M University

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Xavier Villavicencio, Bruce A. McCarl & Ximing Wu

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Department of Economics, Iowa State University, Ames, USA

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Seniority of authorship is shared among Xavier Villavicencio, Bruce A. McCarl and Ximing Wu.

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Villavicencio, X., McCarl, B.A., Wu, X. et al. Climate change influences on agricultural research productivity. Climatic Change 119 , 815–824 (2013). https://doi.org/10.1007/s10584-013-0768-6

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Received : 12 April 2011

Accepted : 06 April 2013

Published : 20 April 2013

Issue Date : August 2013

DOI : https://doi.org/10.1007/s10584-013-0768-6

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