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1 doi: /nature19368 Supplementary Table 1 Detailed information on wheat and maize yields cited in Fig. 3 of main text(mg ha -1 ). Experimental Station Lead farmers County average Year Maize Maize+ Maize Maize+ Maize Maize+ 2008/ ± ± ± ± 0.7 (25) * 6.4 ± 0.7 (28) / ± ± ± ± 0.9 (45) 7.7 ± 0.7 (46) / ± ± ± ± 1.4 (53) 6.8 ± 0.9 (58) / ± ± ± ± 1.6 (65) 7.9 ± 1.2 (62) / ± ± ± ± 1.6 (69) 8.0 ± 0.6 (69) / ± ± ± ± 1.1 (70) 8.4 ± 0.7 (71) Means ± s.d. for yield. *Value in parenthesis is the number of lead farmer participants. The 2008/09 yields were based on farmers estimates (i.e. not determined through actual measurement). Irrigated yields, standard moisture content of 14% for wheat and 15.5% for maize. 1

2 RESEARCH SUPPLEMENTARY INFORMATION Supplementary Table 2 Chronology of major STB events. Activity Location of Service events object Experiment station 20 km from STB Single-factor experiment STB villages Public Multi-factor experiment STB villages Public CLUP STB, Neighbor CLUP villages member Farmer survey Countywide Selected farmer Technical gallery* STB villages Public Customized calendar* STB villages Farmers in STB Field demonstrations* STB villages Public Field days* STB villages Public Crop contests* STB, Neighbor villages Public Farmers shows* STB villages Public Winter workshop* Whole county Public Farmer field schools* STB villages Student farmers Consulting services* STB villages Public Technical broadcasts and STB villages Public cell phone messaging* Field inspections* STB villages Public Government action Whole county Public Market action Whole county Public *For details of technology dissemination and outreach activities, see Extended Data Table

3 RESEARCH Supplementary Table 3 Analysis of the variation in maize yield and climate conditions in Quzhou County in 2008 through Yield potential* Yield at Experimental Station GDD Solar radiation Precipitation (Mg ha -1 ) (Mg ha -1 ) ( C) (MJ m 2 ) (mm) ± ± ± ± ± ± *Yield potential (irrigated) was simulated using Hybrid-Maize model. In order to match the local rotation system, the harvest date was fixed at Oct. 3 rd. The data for GDD, solar radiation and precipitation were all for the maize growing season. These data were monitored by the local meteorological station. 3

4 RESEARCH SUPPLEMENTARY INFORMATION Supplementary Table 4 Comparison of wheat and maize yields between the Quzhou Experimental Station and Lead Farmers over the growing seasons, determined by linear regression of yield over time, and by two-tailed t-test for each year. Year yield (Mg ha -1 ) Maize yield (Mg ha -1 ) Expt. Sta. Lead Farmers Pr > t Expt. Sta. Lead Farmers Pr > t < < < r Slope Pr > F < < < Additionally, wheat yields were significantly greater at the Experimental Station over all years than Lead Farmers (Pr> t = ; 8.32 vs Mg ha -1 ), but maize yields over all years were not different (Pr>t = ; vs Mg ha -1 ). 4

5 RESEARCH Supplementary Table 5 Comparison of crop yield and resource and economic performance parameters between STB villages, neighboring villages and control villages. Comparisons determined by two-tailed t-tests with pooled variances. Crops Comparison Yield NUE * WUE Benefit-cost ratio Labor productivity Pr > t STB vs. neighboring villages < STB vs. control villages < < < Neighboring vs. control villages STB vs. neighboring villages < Maize STB vs. control villages < < < < Neighboring vs. control villages *NUE is the partial productivity of N fertilizer, or grain yield kg -1 chemical nitrogen fertilizer input. WUE is the water use efficiency, grain yield m -3 irrigation water input. Benefit-cost ratio is defined as net profit per unit operational cost. Labor productivity is defined as grain yield hour -1 labor input. 5

6 RESEARCH SUPPLEMENTARY INFORMATION Supplementary Discussion- Attainable Yield and Yield Gap In this study, yield gap is operationally defined as the difference between farmer s actual yield and the attainable yield. Derived from Quzhou Experimental Station, the attainable yield is representative of the local growth conditions (weather, water, soil characteristics, etc.), given the relative proximity between the Experimental Station and the STB villages (about 20 km). Management practices implemented at the Experimental Station were selected to suit the cropping system in Quzhou County. Our approach for yield gap assessment is consistent with well-established principles for local yield gap analysis 1-3. During , attainable yields (at Quzhou Experimental Station) were in the range of Mg ha -1 for maize and Mg ha -1 for winter wheat (Supplementary Table 1). For comparison, the highest recorded maize yield in the North China Plain (NCP; Quzhou is within the NCP) was 16.3 Mg ha -1 (ref.4). Modeling-based simulations for NCP had the highest potential yield as 17.6 Mg ha -1 for maize (using the Hybrid-Maize model) 4, whereas winter wheat potential yields varied from 8.0 to 14.5 Mg ha -1 (using several different models, as summarized by ref. 5). Notably, the attainable yields at Quzhou Experimental Station were approximately 70-85% of the potential yields, which conforms to the concept of attainable yield in previous studies 1,3. It is worth emphasizing that the essence of STB is finding ways to help farmers increase yields and enhance resource and economic efficiencies on the ground level. Toward this end, we believe that the attainable yield is better suited than the potential yield for estimating the yield gap and for engaging smallholder farmers. In our case, lead farmers 6

7 RESEARCH achieved, on average, 97.0% of the attainable yields ( ), while improving resource and economic performance. This achievement is beneficial to the morale of farmers as well as agricultural education-extension personnel. Of course, attainable yield is a moving target. It varies with weather conditions (largely un-controllable) and it can be further enhanced by improvement in management technologies and cultivars. Supplementary Discussion- Unique Features of the STB Model There have been a variety of approaches and mechanisms published and/or deployed in delivering knowledge and technologies in the international agriculture arena. The STB model was built upon some of the traditional and innovative approaches in designing a locally-adoptable technology tool box, similar to the concept of Prototyping 6 ; communicating with different stakeholders, as in Boundary works 7 ; and participatory activities run by and for farmers, as in farmer field schools 8. Meanwhile, STB offers some unique features that are innovative and engaging, as described in the main text and Methods Also, it is worth pointing out that the development of the STB model was an ever-evolving process. We went to Quzhou with ISSM-based technologies and some ideas about possible factors (based on our experience elsewhere); then we conducted the first survey (2009) to confirm and modify our understanding of those factors; and we discovered disparities between farmer practices vs. recommendations and various specific barriers; then we tried to address each of those barriers, such as inventing CLUP as a solution for the plow width issue, engaging government and agri-businesses with regard to the quality 7

8 RESEARCH SUPPLEMENTARY INFORMATION of seeds and fertilizers, etc. Indeed, it is improvisation, which was simultaneously challenging, exciting, and engaging for us. STB staff living among the farmers and immersing in the everyday village life made the improvisation possible. Needless to say, this is different from conventional agricultural research that typically features pre-determined study designs (treatments, replication, plot size, procedures, sequence of events, analytical approaches, etc.). About CLUP The CLUP concept initially grew out of the need to break the barrier for using deep plow tillage. The first CLUP was initiated at the winter wheat sowing time in 2009, the practice was embraced by farmers with 43 new CLUPs formed in Afterwards, we started to recognize additional benefits in helping farmers get uniform services, as described in main text. The formation of CLUP helped the adoption and implementation of recommended management practices. Among other things, results presented in Table 1 were the outcome after adopting recommended practices by lead farmers (through multi-factor experiments, some of which were conducted in fields within a CLUP), and comparison of STB villages (higher adoption rate of recommended practices, some through CLUP) vs. neighboring or control villages (lower adoption rate, limited or no access to CLUP). Supplementary Discussion- Scaling Up and Adoptability of STB in Other Countries With proper policy support and planning, further scaling up is feasible. China has roughly 40,000 townships in major agricultural regions, and 200, ,000 agricultural scientists plus graduate students. To fund STBs in all these townships would require 8

9 RESEARCH approximately $600 million per year (~$15,000 per STB). Meanwhile, the net profit for the farming community would be large (~$0.6 million per STB per year from our Quzhou experience, derived from the average increases in profit of STB villages and neighboring villages vs. control villages in Extended Data Table 5), while improvement in production output, resource efficiency, and environmental benefit would be substantial. Worldwide, there are 500 million smallholder farmers, most are poorly-educated and resource-limited 9. Helping them to achieve higher yields, greater income, and better environmental performance is key for global food security and sustainability. A number of rapidly-developing economies such as India, Brazil, Indonesia, and Pakistan share certain similarities with China. These countries will contribute more than 1/3 of the global increases in food demand 10-11, and already use 64% of the irrigation water and share 51% of tropical deforestation and 52% of excess nitrogen 12. About 55% of global cereal areas without yield improvements exist in these countries 13. Their actual crop yield only achieved up to 60% of their attainable yields 2. Closing yield gaps while increasing resource use efficiency in these countries could go a long way producing an additional 380 million tonnes grain 2, which is about 40% of global-forecast increased demand for These countries already have basic research infrastructure, market and government systems. Therefore, STB could serve as a potential model for adaption and adoption in these countries. As for Sub-Saharan Africa, current agricultural production as a whole features low-input and low-output. Their top priority resides in boosting production inputs as the first step. Even so, the key elements that made STB successful in China (e.g. the combination of top-down with bottom-up platforms, the boots-on-the-ground approach, 9

10 RESEARCH SUPPLEMENTARY INFORMATION the creation of trusting relationships) have potential utility for areas where social and political conditions may allow, such as in Kenya or Malawi 14. Supplementary Literature Cited 1. Lobell, D.B., Cassman, K.G., Field, C.B. Crop yield gaps: their importance, magnitudes, and causes. Annu. Rev. Environ. Resour. 34, (2009). 2. Mueller, N.D. et al. Closing yield gaps through nutrient and water management. Nature 490, (2012). 3. Van Ittersum, M.K. et al. Yield gap analysis with local to global relevance A review. Field Crops Res. 143, 4-17 (2013). 4. Meng, Q.F. et al. Understanding production potentials and yield gaps in intensive maize production in China. Field Crops Res. 143, (2013). 5. Li, K.N. et al. Low yield gap of winter wheat in the North China Plain. Eur. J. Agron. 59, 1-12 (2014). 6. Vereijken, P.A. Methodical way of prototyping integrated and ecological arable farming systems (I/EAFS) in interaction with pilot farms. Eur. J. Agron. 7, (1997). 7. Cash, D.W. et al. Knowledge systems for sustainable development. P. Natl. Acad. Sci. USA 100, (2003). 8. Waddington, H., White, H. Farmer Field Schools: From Agricultural Extension to Adult Education 17 (International Initiative for Impact Evaluation, 2014). 9. Fan, S.G., Brzeska, J., Keyzer, M., Halsema, A. From Subsistence to Profit: 10

11 RESEARCH Transforming Smallholder Farms (International Food Policy Research Institute Washington, DC, 2013). Available at: (20 Nov 2015). 10. OECD/FAO. OECD-FAO Agricultural Outlook (OECD Publishing and FAO,2012). Available at: (20 Nov 2015). 11. Alexandratos N, Bruinsma J. World Agriculture Towards 2030/2050: The 2012 Revision (FAO, Rome,2012). Available at: (20 Nov 2015). 12. West, P.C. et al. Leverage points for improving global food security and the environment. Science 345, (2014). 13. Ray, D.K., Ramankutty, N., Mueller, N.D., West, P.C., Foley, J.A. Recent patterns of crop yield growth and stagnation. Nat. Commun. 3, (2012). 14. Denning, G. et al. Input subsidies to improve smallholder maize productivity in Malawi: toward an African green revolution. PLoS Biol. 7, (2009). 11