Maize yield gaps caused by non-controllable, agronomic, and socioeconomic factors in a changing climate of Northeast China.

Closing the gap between current and potential yields is one means of increasing agricultural production to feed the globally increasing population. Therefore, investigation of the geographic patterns, trends and causes of crop yield gaps is essential to identifying where yields might be increased and quantifying the contributions of yield-limiting factors that may provide us potentials to enhance crop productivity. In this study, the changes in potential yields, attainable yields, potential farmers' yields, and actual farmers' yields during the past five decades in Northeast China (NEC) were investigated. Additionally the yield gaps caused by non-controllable, agronomic, and socioeconomic factors were determined. Over the period 1961 to 2010 the estimated regional area-weighted mean maize potential yield, attainable yield, and potential farmers' yield were approximately 12.3 t ha(-1), 11.5 t ha(-1), and 6.4 t ha(-1) which showed a decreasing tendency. The actual farmers' yield over NEC was 4.5 t ha(-1), and showed a tendency to increase (p<0.01) by 1.27 t ha(-1) per decade. The regional mean total yield gap (YGt), weighted by the area in each county dedicated to maize crop, was 64% of potential yield. Moreover, 8, 40, and 16% reductions in potential yields were due to non-controllable factors (YGI), agronomic factors (YGII), and socioeconomic factors (YGIII), respectively. Therefore, the exploitable yield gap, considered here as the difference between the potential yield and what one can expect considering non-controllable factors (i.e. YGt-YGI), of maize in NEC was about 56%. The regional area-weighted averages of YGt, and YGIII were found to have significant decreases of 11.0, and 10.7% per decade. At the time horizon 2010, the exploitable yield gaps were estimated to equal 36% of potential yield. This led to the conclusion that the yield gap could be deeply reduced by improving local agronomic management and controlling socioeconomic factors.

[1]  David B. Lobell,et al.  Remote sensing assessment of regional yield losses due to sub-optimal planting dates and fallow period weed management , 2007 .

[2]  Xue Chang-yin.  Action of irrigation on decreasing yield reduction due to drought:a risk assessment of winter wheat in North China Plain , 2003 .

[3]  Xiaomao Lin,et al.  Maize potential yields and yield gaps in the changing climate of northeast China , 2012 .

[4]  Senthold Asseng,et al.  An overview of APSIM, a model designed for farming systems simulation , 2003 .

[5]  Victor O. Sadras,et al.  On-farm assessment of environmental and management constraints to wheat yield and efficiency in the use of rainfall in the Mallee , 2002 .

[6]  O. Marinoni,et al.  Reprint of “Quantifying yield gaps in rainfed cropping systems: A case study of wheat in Australia” , 2013 .

[7]  Navin Ramankutty,et al.  Mind the gap: how do climate and agricultural management explain the ‘yield gap’ of croplands around the world? , 2010 .

[8]  J. Wolf,et al.  Yield gap analysis with local to global relevance—A review , 2013 .

[9]  C. Field,et al.  Crop yield gaps: their importance, magnitudes, and causes. , 2009 .

[10]  H. B. Mann.  Nonparametric Tests Against Trend , 1945 .

[11]  Tianyi Zhang,et al.  Low yield gap of winter wheat in the North China Plain , 2014 .

[12]  David B. Lobell,et al.  Regional importance of crop yield constraints: Linking simulation models and geostatistics to interpret spatial patterns , 2006 .

[13]  D. Tilman,et al.  Global environmental impacts of agricultural expansion: the need for sustainable and efficient practices. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[14]  N. Ramankutty,et al.  Closing yield gaps through nutrient and water management , 2012, Nature.

[15]  Kenneth G. Cassman,et al.  Limits to maize productivity in Western Corn-Belt: A simulation analysis for fully irrigated and rainfed conditions , 2009 .

[16]  L. T. Evans,et al.  Yield potential: its definition, measurement, and significance , 1999 .

[17]  Christopher J. Kucharik,et al.  Contribution of Planting Date Trends to Increased Maize Yields in the Central United States , 2008 .

[18]  T. Hodges.  Predicting Crop Phenology , 1990 .

[19]  H. Jones.  Plants and Microclimate: Other environmental factors: wind, altitude, climate change and atmospheric pollutants , 2013 .

[20]  William J. Sacks,et al.  Crop management and phenology trends in the U.S. Corn Belt: Impacts on yields, evapotranspiration and energy balance , 2011 .

[21]  C. Müller,et al.  The yield gap of global grain production: A spatial analysis , 2010 .

[22]  M. Kendall.  A NEW MEASURE OF RANK CORRELATION , 1938 .

[23]  Dat Van Tran,et al.  Closing the rice yield gap for food security [On-line] , 2001 .

[24]  Qiao Dian-feng,et al.  Main Grain Crops Structural Change and Its Climate Background in Heilongjiang Province during the Past Two Decades , 2005 .

[25]  Xiaomao Lin,et al.  Negative effects of climate warming on maize yield are reversed by the changing of sowing date and cultivar selection in Northeast China , 2013, Global change biology.

[26]  N. Ramankutty,et al.  Recent patterns of crop yield growth and stagnation , 2012, Nature Communications.

[27]  J. Doorenbos,et al.  Yield response to water , 1979 .

[28]  J. Norman,et al.  The global distribution of cultivable lands: current patterns and sensitivity to possible climate change , 2002 .

[29]  Hesong Wang,et al.  Climatic and technological ceilings for Chinese rice stagnation based on yield gaps and yield trend pattern analysis , 2014, Global change biology.

[30]  J. N. Black,et al.  Solar radiation and the duration of sunshine , 1954 .

[31]  P. Teng,et al.  Technical issues in using crop loss data for research prioritization , 1995 .

[32]  Fusuo Zhang,et al.  Understanding production potentials and yield gaps in intensive maize production in China , 2013 .