Introduction The use of biomass for energy, chemicals, and materials is considered an important alternative to fossil resources (Chum et al. ; Harvey and Pilgrim ). For biomass to deliver a sizeable contribution, the availability of sufficient sustainable and affordable biomass feedstock is crucial. Assessment studies that have evaluated the current and future global availability of biomass resources show that the largest future potential contribution can come from energy crops grown on various types of land (Hoogwijk et al. ; Smeets et al. ). The most important land type is surplus agricultural land, which can be released through increased production efficiency of food, animal feed or pasture. There is, however, disagreement about the availability of surplus agricultural land. Key uncertainties in predicting this area of land are technological progress in agricultural production systems and the related increases in crop and livestock yields (Dornburg et al. ; Slade et al. ; Batidzirai et al. ). Several studies have investigated the effects of yields on the availability of surplus agricultural land and biomass potentials and impacts. For example, van Vuuren et al. ( ) assessed the impact of food crop yield changes on the global woody biomass potential in 2050. They found that an additional yield improvement of 12.5% compared to the baseline scenario resulted in an increase of the biomass potential from 150 to 230 EJ. Erb et al. ( ) found that the biomass potential in 2050 would be 79 EJ year −1 in the case of intermediate agricultural intensification and humane livestock rearing and 105 EJ year −1 in the case of greater intensification of crop and livestock production. Dornburg et al. ( ) estimated that improvements in agricultural management could account for 140 EJ year −1 of the total biomass supply potential of 500 EJ year −1 in 2050. Slade et al. ( ) derived from a review study that more than 1 Gha of high yielding agricultural land, equal to about 20% of the global agricultural land area in 2010, could be made available for bioenergy crops in 2050 if food crop yields increase at a higher rate than food demand and if the consumption of livestock products is limited. The degree of yield improvements also affects the environmental performance of biomass production. Without sufficient improvements in yields, there is a large risk of direct or indirect land use change (DLUC and ILUC, respectively), which can result in high greenhouse gas (GHG) emissions (Searchinger et al. ; Laborde ). In addition, advances in agricultural production systems may also improve the performance of the agricultural sector as a whole. For example, Tilman et al. ( ) show that there is a significant potential in agriculture to reduce global land clearing, GHG emissions and nitrogen use through improved technology and adaption and transfer of high‐yielding technologies to underyielding regions. Also, Havlík et al. ( ) show that the transition of livestock production toward more efficient systems would significantly decrease livestock‐induced GHG emissions. These emission savings are mainly a result of a reduction in land use change (Havlík et al. ). Models that assess land availability, land use change induced by biomass demand and other impacts of biomass production, such as those used in the studies mentioned above, generally base their crop yield projections on historical developments. Many of these studies also account for (a limited number of) endogenous drivers of future yields. These factors are related to, for example, climate change (Jaggard et al. ), crop or land prices (Eickhout et al. ; Rosegrant et al. ; EPA ; Khanna et al. ) or management changes like the increased use of fertilizer and other production factors (Eickhout et al. ; Beach et al. ; Mosnier et al. ). This diversity of factors reflects that in reality yield developments depend on numerous factors of various origins (e.g., economic, technological, and ecological). The question arises as to what role these different driving factors play, how they relate to each other and if their impact varies between regions. Moreover, productivity developments in the livestock sector have received much less attention in literature and modeling efforts than agricultural crops – despite the fact that livestock production accounts for 70% of the total agricultural land and one third of the arable land area is used for feed crop production (Steinfeld et al. ). For this sector, the lack of insight into the possibilities to increase yields, the rate at which this can be established and the role of different driving factors is even larger. Recently, de Wit et al. ( ) discussed what growth rates and maximum (sustainable) yields could be achieved in European agriculture. They assessed agricultural yield developments in the past five decades and compared these to policy developments, structural changes and trends in the use of production factors (inputs). De Wit et al. found that yield developments were clearly correlated to agricultural policy, but yield growth did not always coincide with more efficient use of inputs (de Wit et al. ). De Wit et al. focused on Europe and did not investigate other regions that are of critical importance in future biomass supply such as Latin America and sub‐Saharan Africa (Hoogwijk et al. ; Smeets et al. ). Given the importance of yield projections in determining biomass supply and impacts, the aim of this study is to assess for seven countries in different world regions (i.e., Australia, Brazil, China, India, United States, Zambia, and Zimbabwe): what the historical agricultural developments and their drivers are, to what extent and at what growth rate crop and livestock product yields can improve in the future, and how different settings and drivers can influence future yield developments. These insights contribute to several aspects identified as a key to improving the assessment of biomass potentials and impacts, such as (1) the use of bottom‐up analyses to enhance the understanding of current (agricultural) systems, options for improvement, the degree to which yields can be increased, drivers, and regional differences, and (2) a more explicit discussion of assumptions (including yields) (Batidzirai et al. ; Wicke et al. ). Methods Selection of agricultural products and producing countries The potential area of surplus agricultural land is expected to be largely influenced by efficiency developments in the production of major agricultural products. Therefore, the agricultural developments are assessed for five crops that are most dominant in terms of global production and cultivated area: wheat, corn, rice, sugarcane, and soybean ( FAOSTAT ). In addition, for livestock, we only take into account beef and cow milk production since cattle uses most of the agricultural area for grazing. Pig and chicken production are often landless, but land is required for producing feed crops (Seré and Steinfeld ). The area of cropland needed largely depends on the crop yields, which are already taken into account in this study. Therefore, pig and chicken production are not included in the assessment. Agricultural developments are assessed in seven countries: Australia, Brazil, China, India, the United States, Zambia, and Zimbabwe (Fig. ). There are two reasons for this selection. First, Brazil, China, India, and the United States are major producers of the selected crops and cattle products ( FAOSTAT ). Second, Australia, USA, Zambia, and Zimbabwe can potentially release a large area of agricultural land for biomass feedstock production (Hoogwijk et al. ; Smeets et al. ). de Wit et al. ( ) assessed agricultural developments in France, The Netherlands, Poland, and Ukraine. For comparison, we have included data about France in the results section. In addition, we compare the general findings from de Wit et al. with our own results. Selected countries and agricultural products. *France was earlier assessed by (de Wit et al. ), but for comparison, data for France are also presented in this study. Historical developments in driving factors The analysis starts with a description of the current status of agriculture in the selected countries and developments in driving factors that have taken place since 1961 (this part of the study is presented in Appendices 1–8 ). Based on literature, the drivers of yield developments are classified into three types (Anderson ; Hengsdijk and Langeveld ; Neumann et al. ; Piesse and Thirtle ; Smith et al. ; de Wit et al. ): technological/management, economic, and institutional. Economic drivers are, for example, market developments and agricultural R&D investments. Institutional drivers include agricultural policies and governance systems. The discussion of economic and institutional drivers is based on literature review. For technological/management drivers, the following are assessed: labor intensity and level of mechanization, irrigation, nutrient, and pesticide use. These indicators are derived from time series data (1961–2010) collected from the UN Food and Agricultural Organization statistical division ( FAOSTAT ). These statistics are aggregated on a country level, for example, annual national consumption of fertilizers (tonne year −1 ). To enable comparison of the management levels between countries, the factors are expressed in average intensity per hectare of agricultural land, for example, the national number of tractors used divided by the total area of agricultural land. For cattle, another indicator for the management level or production intensity is the proportion of ruminants to the area of meadows and pastures (hereafter the ruminant density). This is also derived from FAO statistics. It is not chosen to evaluate the cattle density, because this neglects the importance of other ruminants in the occupation of meadows and pastures for grazing and hay. As a result of the feed requirements varying between ruminant species, the number of ruminants is expressed in livestock units (LU), where one unit represents the energy requirements for maintenance and production of a typical cow in North America. The livestock unit coefficients are obtained from the FAO ( ). The ruminant density is then calculated as the number of livestock units per hectare of meadows and pastures. The ruminants included are: buffaloes, camel, cattle, goats and sheep. The area of meadows and pastures consists of the total land area available for both for grazing and for the production of conserved forages. This approach thus also accounts for systems that combine grazing and confinement. In this study, we only consider a limited number of drivers that can be actively steered. Literature shows that more factors can influence yields, see the discussion. Historical yield and productivity developments To assess historical yield trends and yield growth rates for the selected products and countries, time series data (1961–2010) are collected from FAOSTAT . The crop yield is defined as the annual production quantity per hectare of area harvested (tonne ha −1 year −1 ); the beef yield is given in terms of carcass weight (kg animal −1 ); the milk yield is the annual milk production per cow (kg animal −1 year −1 ). All numbers are national averages. The average beef and milk production per animal, however, are not the best indicators to study developments in livestock product yields. Beef and milk production can take place in different production systems ranging from pastoral to landless. But intensification does not always lead to higher beef or milk production per animal (e.g., because faster weight gain leads to shorter lifespans). Therefore, a better parameter for milk and beef yields would be the feed conversion efficiency (FCE, kg animal product per kg feed intake). The use of the FCE, however, has also limitations. These are considered in the discussion. Average annual yield growth rates are obtained by applying linear regression to the historical yield data and are presented per product, per country, per decade and for the entire period. Growth rates are both expressed in absolute growth per year (e.g., t ha −1 year −2 ) and in percentage per year (simple annual growth rate, relative to the initial year). Temporal shifts are identified for each product on country level and differences between products within a country are described. Explanations are sought by comparing the observed changes with the technological/management, economic, and institutional developments. In addition, developments in productivity of the total agricultural sector and of the total livestock sector are assessed and discussed. This productivity is defined as the proportion of aggregated outputs to aggregated inputs (output–input ratio). To derive the aggregated inputs and outputs, the trend of all inputs and outputs in physical units is calculated as an index (base year 1961). From these indices, an (unweighted) average of all inputs and all outputs is calculated for each year. For the agricultural sector. the included inputs are: agricultural land, fertilizer and tractors; the outputs are: crops, meat, milk, and eggs. For the livestock sector, the inputs are: feed crops, meadow, and pasture land; the outputs are: meat, milk, and eggs. Future yield projections and the role of driving factors Yield growth rates from projections in literature and models are compared to linear extrapolation of historical trends. It is discussed how yield projections are defined and how they can be improved based on the findings on historical driving factors. For each country, key factors are identified that may stimulate or limit future yield developments. To better understand what possible pathways could be defined for future yield developments, also the magnitude of yield gaps is taken into account. The yield gaps are derived from current yield levels and data on maximum attainable rain‐fed or irrigated yields in 2020 as derived from the Global Agro‐Ecological Zones database for the IPCC SRES B1 Scenario from the Australian Commonwealth Scientific and Research Organization (CSIRO) Mark 2 Mode ( GAEZ Global Agriecological Zones ). Results Yield and productivity developments For each country, historical developments in agricultural inputs and yields are assessed. For each country, the status of agriculture and the developments in the different driving factors are discussed in detail in Appendices 1–8 . Here a synthesis of historical yield developments and driving factors is presented for the case of Zimbabwe. Syntheses for the other countries can be found in Appendices 2–7 . The key findings for each country are presented and compared after the example of Zimbabwe. Example: developments and driving factors in Zimbabwe Zimbabwe's first green revolution by commercial farmers (Eicher ; Langyintuo and Setimela ) is clearly represented by the increase of irrigated area and fertilizer use in the 1960s and early 1970s (Fig. ). The yields of corn and soybeans only started to improve from the second half of the 1960s (Fig. ), which coincides with the shift from tobacco production to other crops because of export sanctions imposed in 1965 (Whitlow ; Eicher ) (also see Appendix 8 ). During the 1970s, corn yields declined again, while soy yields only fell down in 1979. In addition, irrigation levels stagnated and fertilizer use dropped in the 1970s. Thus, management conditions seem to be affected by the civil war (Whitlow ) and corn production suffered more from this than soybean cultivation. Sugarcane yields appear to be even less affected by economic and political changes in the 1960s and 1970s. Apart from significant fluctuations, sugarcane yields have increased from 1960 until 1986. The improvement rate of 0.9% year −1 in these years, however, is significantly lower compared to a growth rate of 3.1% year −1 for corn and 16.9% year −1 for soybeans between 1960 and 1980 (all relative to 1961), also see Table . Absolute and relative growth in crop, beef and milk yields for the period 1961–2010 and per decade (based on FAO statistics) Product Country Production Mt (kt) Yield t ha −1 year −1 /kg animal −1 year −1 Average annual yield change kg ha −1 year −2 /kg animal −1 year −2 /% year −1 Per decade , Period 2010 1961 2010 1961–70 1971–80 1981–90 1991–00 2001–10 1961–2010 1961–2010 Corn Brazil 56.1 1.3 4.4 12 21 23 85 101 55 0.9% 1.5% 1.3% 4.0% 3.2% 7.0% 1.6% China 177.5 1.2 5.5 95 111 116 24 79 93 8.0% 5.4% 3.6% 0.5% 1.6% 7.3% 1.6% France 14.0 2.5 8.8 325 33 83 179 101 131 13.4% 0.7% 1.4% 2.4% 1.2% 4.1% 1.4% USA 316.2 3.9 9.6 132 84 63 159 156 117 3.3% 1.6% 1.0% 2.2% 1.8% 2.9% 1.2% Zambia 2.8 0.9 2.6 −11 102 4 −4 117 25 −1.2% 11.2% 0.2% −0.3% 8.3% 2.8% 1.2% Zimbabwe 1.2 1.2 0.9 54 −57 33 16 −39 −15 5.4% −2.9% 2.4% 1.4% −4.3% −0.9% −1.6% Rice, paddy Australia 0.2 5.9 10.4 127 −157 172 24 27 62 2.1% −2.4% 2.8% 0.3% 0.3% 1.1% 0.7% China 197.2 2.1 6.5 127 105 108 83 56 93 5.5% 3.3% 2.3% 1.5% 0.9% 3.6% 1.3% India 120.6 1.5 3.3 18 30 80 30 48 43 1.2% 1.8% 4.2% 1.1% 1.6% 3.4% 1.3% Soybeans Brazil 68.8 1.1 2.9 8 18 15 70 20 37 0.8% 1.3% 0.9% 3.8% 0.8% 4.1% 1.4% India 12.7 0.5 1.1 −2 13 22 16 19 14 −0.4% 1.8% 3.4% 1.8% 2.0% 2.9% 1.2% USA 90.6 1.7 2.9 22 19 25 22 42 27 1.4% 1.1% 1.3% 0.9% 1.7% 1.8% 1.0% Zambia 0.0 0.9 1.6 0 −33 −40 134 100 17 (41.0) 0.0% −4.0% −3.0% 19.8% 11.0% 2.0% 1.2% Zimbabwe 0.1 0.6 1.4 130 114 43 46 −45 12 (57.3) 30.0% 8.5% 2.5% 2.9% −2.5% 0.9% 0.6% Sugarcane Australia 31.5 62.2 77.7 947 −3 211 3475 811 221 1.3% 0.0% 0.3% 4.8% 1.0% 0.3% 0.3% Brazil 716.2 43.4 79.2 399 1239 439 671 1081 775 0.9% 2.8% 0.7% 1.1% 1.5% 1.9% 1.0% India 277.8 45.6 66.1 530 329 738 930 94 593 1.2% 0.7% 1.3% 1.4% 0.1% 1.4% 0.8% Zambia 4.1 124.4 106.1 −6198 −27 −344 747 4 −152 −5.2% 0.0% −0.3% 0.7% 0.0% −0.1% −0.1% Zimbabwe 2.8 88.3 79.5 1071 1385 −649 4972 −2897 −270 1.2% 1.5% −0.6% 7.5% −2.9% −0.3% −0.3% Wheat Australia 22.1 1.1 1.6 −7 16 42 42 −27 12 −0.6% 1.4% 3.3% 2.6% −1.6% 1.0% 0.7% China 115.2 0.6 4.7 65 82 90 79 126 88 10.0% 6.5% 3.7% 2.4% 3.0% 17.0% 1.8% France 38.2 2.4 6.4 114 90 139 97 −9 98 4.3% 2.2% 2.7% 1.5% −0.1% 3.2% 1.3% India 80.7 0.9 2.8 43 26 58 45 19 47 5.8% 2.0% 3.5% 2.0% 0.7% 6.2% 1.5% USA 60.1 1.6 3.1 49 6 −3 51 46 25 3.1% 0.3% −0.1% 2.1% 1.8% 1.5% 0.8% Beef Australia 2.1 150 254 1.7 −0.9 4.0 1.5 2.5 2.1 1.1% −0.5% 2.3% 0.7% 1.1% 1.4% 0.8% Brazil 7.0 192 238 0.1 −2.4 −0.1 3.2 4.1 0.8 0.0% −1.2% 0.0% 1.6% 2.1% 0.4% 0.4% China 6.2 97 141 0.1 0.2 5.4 −2.4 1.3 1.2 0.1% 0.2% 5.8% −1.6% 1.0% 1.3% 0.8% France 1.5 186 296 1.1 2.7 3.9 0.1 1.7 2.7 0.6% 1.3% 1.7% 0.0% 0.6% 1.5% 0.9% India 1.1 80 103 0.0 0.9 1.1 0.1 0.0 0.6 0.0% 1.1% 1.3% 0.1% 0.0% 0.8% 0.6% USA 12.0 215 341 4.2 0.3 3.4 2.0 2.1 2.7 2.0% 0.1% 1.3% 0.7% 0.6% 1.3% 0.8% Zambia 0.1 190 160 −0.6 −2.7 0.0 0.0 0.0 −0.7 (60.8) −0.3% −2.0% 0.0% 0.0% 0.0% −0.4% −0.4% Zimbabwe 0.1 167 225 0.0 −2.4 6.2 3.8 0.0 1.6 (99.6) 0.0% −1.4% 4.1% 2.1% 0.0% 1.1% 0.7% Cow milk Australia 9.0 1985 5810 93 7 113 80 91 75 4.7% 0.3% 3.8% 1.9% 1.7% 4.0% 1.4% Brazil 30.7 707 1340 9 −7 8 51 19 12 1.2% −0.9% 1.2% 6.9% 1.6% 2.0% 1.0% France 23.3 2671 6278 73 47 114 93 30 84 2.8% 1.5% 3.0% 1.8% 0.5% 3.5% 1.3% China 36.0 1208 2882 6 55 −43 6 80 29 0.5% 4.6% −2.3% 0.4% 3.5% 3.0% 1.2% India 50.0 424 1284 2 7 19 27 36 17 0.4% 1.4% 3.4% 3.7% 3.8% 6.0% 1.5% USA 87.5 3307 9595 125 95 136 147 147 128 3.8% 2.1% 2.5% 2.1% 1.8% 4.1% 1.4% Zambia 0.1 300 300 0 0 0 0 0 0 (88.5) 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% Zimbabwe 0.4 406 430 −1 3 0 1 0 1 −0.3% 0.7% −0.1% 0.1% 0.0% 0.2% 0.2% Negative growth in bold. Yield in 1973. Yield in 1968. The average annual yield change in terms of percentage is given relative to the first year of the selected period, that is, the average growth rate is expressed as a percentage of the estimated yield in the first year of selected period (e.g., from 1971–1980, the average annual growth of corn yield in Brazil was 1.5% of the yield level in 1971 as derived from linear regression). Note that the yield growth rates per decade are calculated for a limited time period of 10 years. This means that the choice for a certain timeframe and outliers in the data can have significant influence on the result of the linear regression. The use of longer timeframes may show a very different trend in yield development. Relative to 2010. Development in agricultural inputs. All parameters are calculated from FAOSTAT data ( FAOSTAT ) according to the following definitions: (A) Agricultural area equipped for irrigation = Total area equipped for irrigation/Agricultural area. (B) Close‐up of panel A, presenting a selection of the data to reveal differences between the countries at the lowest irrigation levels. (C) Labor share = Total economically active population in agriculture/Total economically active population. (D) Labor intensity = Total economically active population in agriculture/Agricultural area. (E) Fertilizer = Total fertilizers/Agricultural area. (F) Pesticides = (Insecticides Total + Herbicides Total + Fungicides and Bactericides Total)/Agricultural area. (G) Tractors = Agricultural tractors/Agricultural area. (H) Close‐up of panel G, presenting a selection of the data to reveal differences between the countries at the lowest levels of tractor use. Note: when no data are shown for a certain country and/or year, no data is available for this country or year. Note: for tractors, the capacity or size of machinery is not taken into account. Historical yield developments (1961–2010) for the crops corn, paddy rice, wheat, sugarcane and soybeans ( FAOSTAT ). The introduction of smallholder support in the early 1980s led to a second green revolution (Eicher ), but this is not clearly reflected in the statistics. A major factor is the severe drought in 1983, resulting in a significant drop of corn and soybean yields. Good climate conditions in 1985 led to high yields (Eicher ). But due to the reduction of smallholder support, and maybe the decline of agricultural R&D as well (Eicher ), fertilizer use and crop yields declined in the late 1980s and the 1990s. Remarkably, the area under irrigation increased in the same period. Due to a severe drought, crop yields plummeted in 1992 (Eicher ). With the fast‐track land reform in 2000 (Matondi, ), yields and input levels dropped further and continued declining in the following years. Overall, crop yields and also agricultural productivity (Fig. ) have fluctuated considerably throughout the period 1961–2010. Productivity developments in the Zimbabwean agricultural and livestock sector and institutional, economic and technological/management developments. Because of limited data, agricultural tractors are not included in the inputs and in the output‐input ratio for the agricultural sector. ( FMD , foot‐and‐mouth disease). Considering cattle production, beef yields were stable in the 1960s and early 1970s, but declined during the civil war in the late 1970s (see Fig. in Appendix 9 ). In the 1980s and 1990s, the yields improved at an average rate of 1.8% year −1 (relative to 1981), but dropped temporarily during the drought of 1992. According to the FAO statistics, beef yields have stagnated since the hyper‐inflation and outbreak of foot‐and‐mouth disease (Marquette ; Matondi, ). Between 1960 and 1990, milk yields increased at a very low rate of 0.2% year −1 (relative to 1961). Thus, it seems that technological improvements were limited while economic and political changes did not significantly affect milk yields either. After a small drop in 1992, yields peaked in 1993 and have stabilized since the late 1990s. Summary and comparison between countries Over the past five decades, most crop yields showed an upward trend (Table ). The yield growth rates, however, varied significantly between regions. Average yield growth rates over the period investigated (1961–2010) ranged in most cases between 0.7–1.6% year −1 for crops, 1.0–1.5% year −1 for milk and 0.4–0.8% year −1 for beef (all relative to 2010). Highest rates were found for wheat in China (1.8% year −1 ), milk in India (1.5% year −1 ), and beef in France (0.9% year −1 ). The lowest rates for a crop are found for sugarcane (−0.3 to 1.0% year −1 ), for any one country the rates are lowest for Zimbabwe (−1.6 to 0.7% year −1 ). For comparison, in the European countries studied by de Wit et al. ( ), the average growth rates of wheat are 1.0% year −1 for Poland to 1.3% year −1 for France (relative to 2010). This is lower compared to the wheat growth rates in China and India, but higher compared to the figures for wheat in Australia and the USA. Absolute wheat yield growth in France and the Netherlands (approximately 100 kg ha −1 year −2 (de Wit et al. )), however, was higher compared to the four countries producing wheat in the present study. For beef, the absolute growth in France and Poland is comparable to the USA, but average growth rates in these European countries are higher than in all non‐European countries assessed in this study (0.9% year −1 for France and 1.2% year −1 for Poland). Absolute and relative yield growth figures for beef in the Netherlands are comparable to Brazil. In this study, the most observed trend over five decades for crop yield growth is linear. This is in accordance with other studies (Hafner ; Ray et al. ; Fischer et al. ). Yet, in each case, the analysis revealed periods during which yields improved at a higher rate compared to the long‐term average as well as periods during which yield growth rates were lower than this average. In each case, technological as well as economic and institutional factors have played a role and these drivers often influenced each other. Yet, the importance and the effect of a driving factor varied from case to case. Table gives an overview of the most important factors behind yield and productivity developments in each country (for more details, see Appendices 2–8 ; for France see de Wit et al. ( )). Key driving factors behind historical yield and productivity development Driving factors Effect on other driving factors Effect on crops Effect on cattle Australia ● Market reforms, trade liberalization, opening of export markets + Rice and wheat yields+ Agricultural productivity +/− Beef and milk yields ● Cattle: growth of export markets + Cattle management and technology + Milk and beef yields− Livestock productivity ● Introduction of agri‐environmental policies + Agricultural production, fertilizer use, irrigation +/− Agricultural productivity + Decline in livestock productivity slowed down Brazil ● Industrial protection and fertilizer subsidies + Fertilizer use + Corn and soybean yields − Agricultural productivity +/− Beef and milk yields ● Economic reforms − & +/− Fertilizer use − Corn and soybean yield growth rates +/− Agricultural productivity +/− Beef and milk yields ● Opening of agricultural export markets + Fertilizer use + Yield growth rates corn and soybean + Beef and milk yield growth rate & Livestock productivity increase ● ProÁlcool program + Tractor use, irrigation + Sugarcane yields China ● Agricultural reforms, public investments in infrastructure and R&D + Irrigation, fertilizer use, mechanization + Yields ● Economic reforms + Agricultural diversification − Yield growth rates + Beef and milk yields ● Increased consumption of milk and dairy products + Milk yields France ● Protection of agricultural markets, stimulation of mechanization and fertilizer use + Inputs + Yields − Agricultural productivity + Milk and beef yields ● Stimulation of modernization and scaling‐up, land reforms +/− Fertilizer use + Yields & Agricultural productivity + Milk and beef yields ● Shift to high‐yielding crops + Yields & Agricultural productivity ● Agri‐environmental policy, reform of farmer support programs and stimulation of organic farming − Inputs − Yield growth rates − Yield growth rates ● Quotation milk production − Livestock productivity India ● Public investments and subsidies + Inputs + Yields − Agricultural productivity ● Increased milk consumption + Milk yields USA ● Investment in R&D, biotechnology + Livestock technology and management + Yields + Beef and milk yields ● Trade liberalization and reform of farmer support policies − Yield growth rates (during reforms) + Yield growth rates (after reforms) ● Growing milk market + Milk yields ● Agri‐environmental programs − Fertilizer use + Agricultural productivity Zambia ● Fertilizer subsidies + Fertilizer use + Corn yields ● Economic liberalization, elimination of fertilizer subsidies − Fertilizer use+ Irrigation − Corn yields+ Soybean yields ● Conservation farming technologies + Agricultural productivity ● Fertilizer Support Program + Fertilizer use+/− Irrigation − Agricultural productivity+ Corn yields Zimbabwe ● R&D (commercial farmers) + Irrigation & Fertilizer use ● Civil war − Irrigation & Fertilizer use − Yields − Beef yields ● Economic reforms, reduction of smallholder support and of agricultural R&D − Fertilizer use − Yields ● Economic crisis and fast track land reform − Inputs − Yields ● FMD and ban on beef export − Beef yields FMD, foot‐and‐mouth disease. Effect: +, increase; − decrease; +/− stabilization. Improvements in agricultural technology and management have often led to considerable yield growth (Figs and ). Especially in China and India, large‐scale adoption of new technologies (including high‐yielding crops) resulted in high average yield growth rates (Appendices 4 and 5 ). In France and the United States, improved technologies resulted in considerable absolute yield improvements (Appendix 6 ). The technological improvements, however, included a significant increase in the use of inputs like fertilizer and often caused a decline in agricultural productivity (Fig. ). In the cattle sector, yield improvements were often achieved through the increased use of feed crops. In Australia and Zimbabwe, however, the consumption of feed crops grew faster than the production of meat and milk, which led to a reduction in the productivity of the livestock sector. Other countries, like India and the USA were able to compensate for the higher input levels by increasing the production output levels of the livestock sector at a similar or even higher rate. Comparison of developments in productivity (output‐input ratio) of the agricultural and livestock sector in the seven selected countries. The input‐output ratio is indexed to 100 for the year 1961. This means that when the ratio is higher than 100 in 1 year, the productivity has improved compared to 1961; when the ratio is lower than 100, the productivity has declined. For the agricultural sector the included inputs are: agricultural land, fertilizer and tractors; the outputs are: crops, meat, milk and eggs. For the livestock sector, the inputs are: feed crops, meadow and pasture land; the outputs are: meat, milk and eggs. Economic factors often play a vital role in the improvement of agricultural technology. Investments in R&D enabled the development of new technologies. In most countries, these were mainly public investments. In the USA, also private investments were very important (Appendix 6 ). These investments had already started in the period of industrial and agricultural protectionism. Only in Australia, industrial protectionism indirectly biased the agricultural sector and hindered improvements in production practices and crop yields (Appendix 2 ). The introduction of economic liberalization provided an incentive for many farmers to (further) improve yields and increase or stabilize agricultural productivity. In Australia, yields and yield improvement rates improved quickly after liberalization started. In Brazil and the USA, yield improvement rates reduced in the first instance but increased again after about ten years (Appendices 3 and 6 ). Agricultural production in China diversified after the reforms and yield improvements of predominant crops slowed down (Appendix 4 ). Commercial farmers in Zambia profited from liberalization as they were able to improve soybean yields at high rates, while corn yields of smallholders decreased (Appendix 7 ). For the cattle sector, the importance of commercial beef and milk production for the domestic or export market was found to be a key factor for yield improvements. In Australia and the USA, such markets already existed during the period of protectionism, while the beef market in Brazil has especially grown after economic liberalization (Appendices 2 , 3 and 6 ). In France (and the EU as a whole), dairy markets got saturated and a quota on milk production was introduced. The number of dairy cows reduced significantly, but milk yields continued to increase through improved management (Huyghe ). This, however, led to a reduction in livestock productivity in terms of the output–input ratio. In accordance with the findings of de Wit et al. ( ) for European countries, policies are found to be an important instrument in steering changes in the agricultural sector in the seven countries investigated in the present study. New technologies could be adopted by farmers because of farmer support programs, for example, in India (Appendix 5 ). Market liberalization policies created new markets for agricultural products, for example, in Australia (Appendix 2 ). In some cases, yield improvements have been attained by policies that were focused on a specific commodity: for example, in Zambia, the focus of policies on corn production during a long period resulted in significantly higher yield growth rates for corn compared to other crops (Appendix 7 ). In Brazil, the ProÁlcool program positively affected sugarcane yields (Appendix 3 ). The import substitution policy for edible oils in India, especially stimulated the increase of soybean yields. In contrast to the successful implementation of policies in the above examples, Zimbabwe also shows the impact of a lack of good governance and stimulating policies. A civil war in the 1970s and economic reforms around the 1990s disrupted agricultural production and the economy. Due to the long‐term unstable situation, crop yields and agricultural productivity have fluctuated heavily. In addition to agricultural policies aimed at economics and production, most countries have also introduced agri‐environmental policies which aimed at, for example, enhanced quality of degraded agricultural lands (Australia, China, Zambia, Zimbabwe), balanced use of inputs (China, France) and controlled use and management of natural resources (Australia, India, the United States). In the USA, China, and Zambia, this led to improved agricultural productivity (Appendices 4 , 6 , and 7 ). In Australia, the productivity did not improve considerably compared to previous years (Appendix 2 ). In India, the productivity continued to decline due to weak enforcement of agri‐environmental policies (Appendix 5 ). Yield projections Crops Models that assess biomass potentials and/or impacts of biomass production apply either only exogenous yield projections (determined by factors outside the model) or a combination of exogenously and endogenously (determined by the model based on internal factors) defined yield projections. The exogenous yield projections are based on historical trends. As mentioned in the previous section, the analysis of historical yield developments shows that the most observed trend for crop yield growth is linear. Yet, longer historical time series show that in, for example, the USA crop yield growth has not always followed the current linear trend (see Fig. in Appendix 9 ). Also, over shorter time frames, variability in the trend is found with periods of decline, stagnation and/or strong growth. Therefore, yield projections based on historical trends depend on the historical time frame taken into account. For example, Fischer et al. ( ) find global yield growth rates of 1.0% year −1 for wheat, rice, and soybean and 1.5% year −1 for corn based on the linear trend for 1991–2010. For the period 1961–2010, the present study finds global growth rates that are slightly higher for wheat, rice, and soybean (1.1–1.3% year −1 ) and lower for corn (1.3% year −1 , all relative to 2010), see Table in Appendix 9 . Although these differences seem small, they may have considerable impact on future biomass potentials and impacts. This is illustrated by Fischer et al. ( ) who state that, in order to meet the projected food demand in 2050 with limited increase of real prices of crops, the minimum global yield growth rate for staple crops between 2010 and 2050 is 1.1% year −1 relative to 2010 (Fischer et al. ). Higher growth rates of, for example, 1.3% year −1 are preferred to account for factors that may influence supply and demand of crops, including increasing biofuel demand (Fischer et al. ). In model assessments, it is therefore important to make explicit what historical time frame is considered to define exogenous yield projections. Although exogenous yield projections are based on historical trends, some models assume extrapolation of this trend (e.g., in (Jaggard et al. )), while most models assume the overall future yield growth to slow down compared to the historical trend (e.g., in (Laborde ; OECD )). Van Dijk and Meijerink ( ) give several reasons for assuming decreasing yield growth. First, the opportunities for increasing yields and exploiting existing yield gaps are more and more exhausted (Searchinger et al. ). Also, investments in agricultural R&D have declined and considerable socio‐economic constraints in many developing countries are considered to remain a limiting factor for yield growth (Alston et al. ; McIntyre et al. ; Alexandratos and Bruinsma ). Although these motivations are reasonable, these are mainly expectations about how different factors are likely to develop. There may, however, also be other possible pathways. Indeed, the present study shows that in the past 50 years, significant developments in technology, for example, mechanization, fertilizer use, and crop breeding, have driven yield improvements. But although these technologies are wide‐spread now, there still exist considerable differences in technology level and yield gaps between regions (see first part of the results and Table in Appendix 9 ). The present study and the study by de Wit et al. ( ) show that in regions where historical yield growth was high, the development and adoption of new technologies was primarily driven by policies (e.g., subsidies for farmers, trade liberalization and public investments in R&D). In regions where there is still room for considerable yield improvements, stimulating policies could thus play a vital role in materializing this potential. Similarly, other factors could also have a significant effect on future yield developments. The presence of different potential pathways shows that, in the assessment of future biomass potentials and the impacts of biomass production, it is important to investigate different scenarios and to include various endogenous driving factors. To determine the endogenous yield projections, models relate yield change to driving factors like land or crop prices, climate change, or management. According to Dietrich et al. ( ), technological change is considered to be the key driver for yield change. There is, however, little consensus about the drivers of technological change and the influence of these drivers on yield change (Dietrich et al. ; Robinson et al. ). Therefore, the number of endogenous factors is generally limited in models, often to only one or two factors. As a result, the different technical, economic, and institutional driving factors are not covered well. Furthermore, the findings from the historical analysis make clear that future yield developments largely depend on how the different driving factors develop in each region. As the number of endogenous factors is generally limited, models do not properly distinguish different driving factors between regions. Important examples of how different drivers affect yield growth potentials are the following: In the case of corn in the USA, current yields have attained almost 80% of the maximum attainable yield (see Table on yield gaps, Appendix 9 ). Thus, it is likely that the current technological limits will constrain and slow down yield growth in the near future. Continuation of the historical yield improvement trend would require significant technological progress , for example, increase in the potential agroclimatic yield through biotechnology. On the other side of the spectrum, yields of rice and soy in India and corn and soy in Zambia and Zimbabwe are less than 40% of the maximum attainable yield. In various other cases, the yield gap is smaller but still leaves room for considerable yield improvements as well. In such situations, the historical analysis shows that accelerated yield growth compared to the longer term trend is possible under favorable circumstances with regard to, for example, governance. Zambia and Zimbabwe are examples of cases where stable agricultural and trade policies are needed to improve and support the agricultural sector and market and the economy in general. Under such conditions, farmers may be able to adopt improved technologies and management practices. As more advanced technologies and practices already exist, farmers could realize a significant acceleration in yield growth compared to the average trend over the past five decades. In Southern India, rice yields could be increased significantly if management conditions and market access would be improved (see Appendix 5 on agricultural characteristics). In Northern India, rice (and also wheat) is mainly produced on irrigated lands and yields are higher compared to Southern India. Ground water depletion, however, poses a risk to future yield improvements. An important measure to attain yield improvements in an environmentally sound way is sustainable agricultural intensification . In Australia, for example, management levels have been low relative to most other countries. The same is true for yield growth rates. Although intensification could have significant environmental impact, it is found that this can also be realized while improving the productivity, that is, increasing the crop production (output) per unit resource use (input), and reducing negative effects like emissions and water pollution (see also (Fischer et al. ; Hochman et al. ; de Wit et al. )). Thus, for future modeling work, detailed regional assessment of the most important driving factors for yield development and the implementation of more drivers are needed. For this purpose, Table identifies some key drivers that may either limit or stimulate future yield improvements in each country assessed in this study. Key threats and opportunities for future yield improvements Threats to future yield improvements Opportunities for future yield improvements Australia Climate and climate change Sustainable intensification of crop and livestock sector (see e.g., (Hochman et al. )) Brazil Weak enforcement or mitigation of land conservation policies Intensification of cattle production China Decreasing water availability, loss of fertile land and land degradation Expansion of region‐specific policies, continuation or expansion of policies to improve productivity, increase of mechanization India Land degradation, decreasing water availability Improvement of market access, management and production of smallholders in Southern India, enforcement of agri‐environmental policies, improvement of productivity USA Lack of new advances in biotechnology: no significant improvements in the maximum attainable yields Significant funding for and advances in biotechnology: shift of maximum attainable yields Zambia Soil erosion, climate variability Stimulating policy; e.g., improvement of market access of smallholders (investment in infrastructure), increase in the adoption of conservation farming Zimbabwe Continuation of unstable economic situation, climate variability Re‐establishment of (beef) export markets: improvement of knowledge and production of smallholders The factors are identified based on the historical assessment presented in the first part of the results section. We highlighted the importance of stimulating policies, especially for Zambia and Zimbabwe earlier. The historical analysis showed that in all countries agricultural and trade policies play an important role in steering yield developments. Van Dijk and Meijerink ( ) show that in economic models, policies and institutions are included, but it seems that their effect on yields is not considered yet. Given our findings, it is important to include policies as a driving factor for yield changes in model assessments. For example, Dietrich et al. ( ) have attempted to implement endogenous yield change related to investments in R&D. Linking yield developments to R&D and other policy‐related drivers could be used to define scenarios for different policy pathways and to evaluate their impact on yield changes. In addition, yield gap figures are a good indicator both for the degree of technological progress that can still be attained, and for the potential yield growth rates. It is useful to apply yield gaps in the models to define the yield development projections. For that, more research is needed on, for example, the (crop and region specific) correlation between yield gaps and yield growth rates. In the historical analysis, it was also found that yields have often fluctuated in the past and this affected the average yield growth rate. In current assessment models, projections are based on historical trends and the influence of fluctuations on the long‐term trend is neglected. The regional identification of key drivers for yield developments could include an assessment of risk factors for yield fluctuations (e.g., the occurrence of extreme climate conditions), which can be taken into account in the yield projections. A comparison of yield projections from global outlook studies in van Dijk and Meijerink ( ) shows that the projected yield growth varies significantly and depends on the underlying assumptions made. The influence of underlying assumptions on yield projections is also illustrated by a comparison of yield projections from the integrated assessment model IMAGE (which is used for the assessment of biomass potentials and impacts; see van Vuuren et al. ( )) and the economic model MIRAGE models (which is used for analyses on, land use change induced by biofuel targets; see Laborde ( )). In Table (Appendix 9 ), growth rates derived from the yield projections in IMAGE and MIRAGE are compared to the extrapolation of historical trends. Both models combine exogenous yield projections with endogenously determined yield changes. The exogenous yield projections used in IMAGE and MIRAGE assume that, on the global level, yield growth rates will slow down compared to the historical linear trend (Laborde ; OECD ). Nevertheless, the two models do not always agree on whether yields in a certain region or even globally will improve at a pace higher or lower compared to the linear growth trend. Large differences in projections between the models are found for corn and soybean in Brazil and rice in China. Also, the projected global growth rates from MIRAGE for corn, rice, and soybean are higher compared to the projections based on linear extrapolation of the historical trends from 1961–2010. In IMAGE, the global projections for wheat, corn, rice, and soybean result in lower yield growth rates compared to linear extrapolation. It is most likely that these contrasting results can be explained by differences in how endogenous yield changes are modeled. The insights from this and other studies would help to make the underlying assumptions for endogenous yield projections more explicit and detailed, and help to assess how yield projections are influenced by different assumptions. In the introduction, several studies were mentioned that assess the influence of increased yields on the biomass potential. Van Vuuren et al. ( ), Erb et al. ( ) and Dornburg et al. ( ) used yield projections from the FAO (Bruinsma ), the presented yield projections from the IMAGE model are in line with these projections (OECD )). Van Vuuren et al. ( ) and Dornburg et al. ( ) take this FAO scenario as baseline and assess the extra biomass potential from additional yield increases. Erb et al. ( ) however, assume that the FAO scenario represents a high intensification scenario and baseline yield improvements are lower. This shows that the perception of the baseline varies. This again underlines the importance to make assumptions more explicit. In addition, it is important to discuss each scenario and address under what conditions the projected yields and the resulting biomass potentials and impacts can be attained; the degree to which investments have to be increased, or required changes in policy. This is considered to be highly valuable for decision making. Beef and milk As opposed to historical yield growth trends for crops often being linear, for beef and milk production, we only found linear trends for Australia, India, and the United States; these are countries where we found that yields had significantly improved over a longer timeframe because of the existence of a commercial market. The absence of a yield trend in the past makes it more difficult to define yield growth scenarios for the future. Also, compared to crops, less information can be found about the projections used in the models. Several studies, e.g. IMAGE (Bouwman et al. ; Eickhout et al. ), apply yield projections from the FAO, which are presented for aggregated world regions level in Wirsenius et al. ( ). A comparison of these projections with historical growth figures indicates that in developed regions, the average annual yield growth rate is projected to be significantly lower than in the last five decades. For example, in North America and Oceania, the increase in beef yield is projected to decline from 1.0% year −1 in the period 1961–2005 to 0.2% year −1 from 1997/99–2030. Also on the global level, yield growth of beef and milk will be slower compared to the historical trend. Acceleration of yield improvements is projected to especially take place in sub‐Saharan Africa. In this region, the yield growth rates of milk are projected to increase from −0.4% year −1 (1961–2005) to 0.8% year −1 (1997/99–2030). In addition to the FAO projections, Wirsenius et al. ( ) defined an improved livestock production (ILP) scenario, assuming faster intensification of livestock production in low‐ and medium income regions as a result of increased competition for land and stricter policies related to land use and livestock production. In this scenario, more regions (e.g., Asia) will realize accelerated yield increases compared to the past. Also on the global level, yield growth will increase from 0.9% year−1 to 1.5% year−1 for beef and from 0.5% year−1 to 2.2% year−1 for milk. The scenarios from FAO and Wirsenius et al. again illustrate regional differences, which should be taken into account in the models. To define yield projections, it is again helpful to consider the potential role of different driving factors per region. In the historical assessment, it was found that the role of commercial livestock production for the domestic or export market is an important factor for explaining yield improvements. For modelling purposes, several scenarios could be defined for market development based on assumptions regarding the size and location of beef and milk consumption and production. In addition, the speed of yield improvements may be based on other driving factors like the possibilities for technological developments and the introduction of agri‐environmental policies. Similar to crops, the technological improvement potential could be assessed through yield gap analysis. For livestock, however, no standardized methods exist to assess the yield gap ( ILRI ). One approach is similar to the conceptual framework for crops and is based on three groups of production factors; production defining (climate and animal genetic characteristics), production limiting (water and feed intake) and growth‐reducing (diseases, pollutants) (van de Ven et al. ). This method is still new; the first calculations of potential beef production were recently conducted by van der Linden et al. ( ). Discussion FAO data This study analyses a large amount of statistical data which is obtained from the FAOSTAT database. The quality of FAO data, however, can vary significantly. When available, the FAO presents official data , which means that the data are collected directly from the states. Yet, the data collection capacities and practices vary between countries and affect the reliability of the data. In addition, the concepts, definitions, coverage, and classifications used by the countries are not uniform and require harmonization to enable international comparison. When no official data is available, the FAO gives figures from secondary semiofficial or unofficial datasets or own estimations ( FAOSTAT ). With regard to crop yields used in this study, the amount of underlying data that is nonofficial data is limited and mainly restricted to Zambia and Zimbabwe. In the case of milk and beef yields, secondary and estimated data are more common and also presented on a regular basis for Brazil, China and India ( FAOSTAT ). Sometimes these data seem to be artificial as yields remain constant over one to five decades (see for example yields from beef and milk production in Zambia and Zimbabwe, Fig. ). To get an impression of the reliability of FAO yield statistics, their consistency with USDA data was analyzed (Fig. , Appendix 9 ). In some cases, FAO and USDA diverge significantly. For example, in Zambia, corn yield development between 1961 and 1982 is highly uncertain; figures from the FAO show an increasing yield trend, while the USDA data presents a downward trend. This has significant impact on the interpretation of historical developments. The FAO data suggests that corn yield improvements started in the early 1970s with the introduction of fertilizer subsidies for smallholder farmers, while the USDA data implies that the yields were negatively affected by the new pricing and subsidy policies and only started to increase after the introduction of the first new corn varieties in the late 1970s. Differences between the two statistical sources were also found for soybean in Zimbabwe, milk in Brazil and China, and beef in China and India. Not all data sets could be compared as the USDA database had no statistics on milk and beef production in Zambia and Zimbabwe and on sugarcane yields. The varying quality and reliability must be taken into account when interpreting the results. Still, the FAO database is the most complete source for yield figures currently available. Yield indicators for cattle With regard to cattle production, it is preferred to assess yield developments in terms of changes in feed conversion efficiency (FCE, kg animal product per kg feed intake) instead of beef or milk production per animal. The main problem of using the production level per animal is that this figure does not always reflect technological advancements. For example, an improved beef cattle production system may achieve faster weight gain and be able to reduce the cattle lifetime. As a result, the beef production per animal may remain constant or even reduce, while the total production can be increased. Also, de Wit et al. ( ) showed that beef yields in the Netherland decreased in the 1990s and 2000s because of the large share of dairy cows that are optimized for milk and not for meat production. In the present study, the historical data show that the average yield per animal has continued to increase in the main cattle producing countries. The rates at which these yields have increased, however, are likely to differ from improvement rates in feed conversion efficiency. Another reason why the use of the feed conversion efficiency is preferred is the underlying idea of this study that yield improvements have an important role in making land available for biomass production without increasing overall land use. While crop yields are directly related to land use, figures of beef or milk production per animal give no indication of the related land use. As feed consumption can be linked more directly to land use, the feed conversion efficiency would give a better insight in how developments in the cattle sector would influence land requirements. Ideally, the FCE is measured over the lifetime of an animal because its value is not constant over time. To analyze historical developments in average FCE, a more simple but less accurate way is to calculate the feed conversion efficiency by dividing the produced amount of beef or milk by the gross feed intake. This feed intake is based on estimated energy requirements and the amount of energy supplied by feed inputs, factors that are highly depending on the production system. Therefore, the examination of developments in feed conversion efficiencies over time would require the allocation of animal populations and production quantities to the different production systems for at least several points in time, for example, building on previous work by Seré and Steinfeld ( ) and Bouwman et al. ( ). This was not feasible for the present study. As the carcass weight and annual milk production per animal are the best available data over a longer historical time period, these figures were used here to assess beef and milk yield developments. The same data were used by de Wit et al. ( ) and Wirsenius et al. ( ) to study livestock yield growth rates. Yield projections and assessment of biomass potentials This study investigated historical developments in yields and their drivers and provides suggestions for how potential studies can better account for yield developments and driving factors. The assessment focused on three types of driving factors: technological/management, economic, and institutional. Other factors, however, may also influence yield developments. Climate change, for example, may either have a positive or negative effect on yield growth depending on the location (see e.g., Jaggard et al. ( )). Also, several studies indicate that yield improvement rates of crops are related to the GDP level of a country (Hafner ; Powell and Rutten ). This correlation, however, does not necessarily mean that GDP itself is a driver of yield development. It is more likely that GDP is an indicator of other driving factors, such as market conditions and technology levels, which are included in the present study. As shown in the section about yield projections, historical yield growth rates depend on the time frame considered. This is also seen when comparing the yield growth rates for France as calculated in this study and in de Wit et al. ( ). For wheat, for example, the present study found a growth rate of 4.3% year −1 (114 t ha −1 year −1 ) for the period 1961 to 1970 while de Wit et al. found a growth rate of 5.2% year −1 (136 t ha −1 year −1 ) for the period 1961 to 1969. Thus, the yield growth rates are highly sensitive to the timeframe applied, especially in the case of short time frames. The growth rates should thus be considered with great care and only be used as an indicator of the extent of yield growth or decline. Nevertheless, both studies show that the yield growth rates are very useful to assess the impact of driving factors on yield developments. Although the historical assessment gives important insights into how different factors may influence future yield developments, it is not possible to predict future yield growth rates. The insights can thus only be used to assess how yields may develop under certain conditions. Particularly the application of endogenous factors and scenarios is useful to assess how yield developments change under different assumptions and how this affects biomass potentials. As mentioned in the section about yield projections, it is important to translate each scenario to conditions for meeting the projected yield developments. This can help identify (regional) strategies for increasing yields. Finally, in addition to yields, there are also other factors that may affect biomass production potentials. For example, market developments and incentives could influence the balance between crop and livestock production on the one hand and biomass production on the other hand. Also, sustainability criteria could affect the area of land that is excluded from biomass production. An overview of more key factors is provided by Dornburg et al. ( ). Similar to the drivers for yield developments, it is important to make the assumptions regarding these factors explicit. Also, the application of scenarios could be useful. Conclusions Global, sustainable biomass production potentials of energy crops largely depend on the future availability of surplus agricultural lands made available through yield improvements in crop and livestock production. This study analyzed the pace and direction of historical yield developments between 1961 and 2010 in Australia, Brazil, China, India, the United States, Zambia, and Zimbabwe. Furthermore, it assessed the technological, economic, and institutional driving forces behind these developments and explored how the insights gained can help to improve the modeling of future yields. This study showed that historical yield growth (especially of crops) has often followed a linear trend. Mainly, the average yield improvement rates for crops and milk were between 0.7% and 1.6% year −1 . For beef, the rates were lower (maximum of 0.8% year −1 in Australia; all relative to 2010). In all cases, yields and yield growth rates have fluctuated to various degrees. Large fluctuations were especially found for crops when driving factors changed strongly (e.g., extreme climate conditions in Australia). Also, in each case, the analysis revealed periods during which yields improved at a higher rate compared to the long‐term average as well as periods during which yield growth rates were lower than this average. The periods of high yield growth, for example, 8.5% year −1 for soybean in Zimbabwe in the 1970s, show that relatively fast improvements can be attained in cases where the yield gap is still large. Such significant improvements can especially be realized under favorable conditions with regard to economics and governance that stimulate improvements in agricultural technology and management. The future development of yields depends on how driving factors will change in each region. The historical assessment shows that all three types of driving forces have influenced yield changes. The importance and the effect of each factor, however, is country‐ and even regional‐ specific. Overall, supporting agricultural policies have played an important role in increasing yields. Examples of successful policies are subsidies to stimulate adoption of new technologies, trade liberalization (resulting in increased demand for agricultural products which stimulated investments and innovations in the agricultural sector) and public investments in R&D. In some periods and countries, such policies were absent or eliminated (e.g., Australia in the 1960s and Zambia in the 1990s). As a result, yields stagnated or declined. Although agricultural policies led to yield increases in many cases, they failed to improve output‐input ratios (i.e., unsuccessful to realize more efficient use of resources like fertilizers). Some countries like the USA and (to a lesser extent) China were able to increase this productivity by implementing specific agri‐environmental policies. Other countries adopted such policies as well, but the result largely depended on the success to enforce these policies (e.g., productivity stabilized in Australia, but no effect was seen in India). The importance of policies in steering yields was especially high for crops. With regard to yield improvements in cattle production, a key factor was the importance of commercial beef and milk production for the national or export market. But policy and market can be closely related: in many cases, trade liberalization created new markets, which stimulated investments and resulted in improved yields as demonstrated in, for example, Brazil. Current models that assess biomass potentials and impacts only take into account one or a limited number of endogenous factors influencing yields. Also, an explicit discussion of the assumptions behind yield projections is lacking, which hampers a comparison of yield projections between the models. Several suggestions are made to improve the models and thereby our understanding of potential future pathways for agricultural yield developments and for sustainable biomass production. First, scenarios based on regional assessment of key factors for yield development, as conducted in this study, could help to gain more insight in potential pathways and regionally differentiated effects. Second, to define such scenarios, yield gap figures are an important indicator of possible technological progress and the potential rate of yield improvement. Also, different policy strategies should be included and tested in the scenarios. Finally, the assessment of important factors for yield development could help to make the underlying assumptions of yield projections more explicit. The implementation of these suggestions will help to identify policy options and preconditions for specific development pathways. Acknowledgments The authors thank Vassilis Daioglou (Utrecht University and PBL Netherlands Environmental Assessment Agency) for sharing yield data from the IMAGE model. David Laborde is thanked for sharing data from the MIRAGE model. This research was conducted within the research program “Knowledge Infrastructure for Sustainable Biomass”, which is funded by the Dutch Ministries of ‘Economic Affairs’ and ‘Infrastructure and the Environment’. Conflict of Interest None declared. Notes Assuming continuation of current trends in diet and crop land area expansion. Maximum attainable yield: the yield resulting from combining (1) the constraint‐free potential agroclimatic yield with regard to temperature, radiation, and soil moisture conditions prevailing in the specific region and (2) reduction factors related to climate (e.g., pests and diseases), soil and terrain conditions, and assumptions regarding the management level ( GAEZ Global Agri‐Ecological Zones ). Generally, it is assumed that there is a minimum yield gap where the actual yield level is equal to the economically attainable yield. Fischer et al. ( ) consider this economically attainable yield to be about 23% below the maximum attainable yield. Larger yield gaps are assumed to be ‘economically exploitable yield gaps’. These caps could (largely) be closed with existing technologies (Fischer et al. ). The high growth rate for soybean yields is also related to the drought induced yield drop in 1992. Without this outlier in the dataset, however, the improvement rate is still 12.2 % year −1 . Sugarcane yields were not considerably affected by the drought.
Food and Energy Security – Wiley
Published: Apr 1, 2015