Spatial pattern of windbreak effects on maize growth evaluated by an unmanned aerial vehicle in Hokkaido, northern Japan

Effects of tree windbreaks on crop production vary from field to field. However, considerable labor is required to evaluate the effects of windbreaks by conducting yield surveys in multiple fields. The present study aimed to clarify whether the spatial pattern of windbreak effects on maize growth can be evaluated using an unmanned aerial vehicle (UAV) in a field with a windbreak in Hokkaido, northern Japan. We mapped normalized difference vegetation index (NDVI) distribution in the field using a UAV and conducted micrometeorological observation and survey of growth and yield. We found that the windbreak positively affected maize growth by increasing soil temperature at 3–5 H (H = windbreak height) and negatively by shading at approximately 1 H. NDVI estimated using the UAV was high in June and low in September, suggesting that the growth rate of maize at 3–5 H was higher than that at 6–12 H. NDVI was significantly correlated to dry matter content of maize, although the area shaded by the windbreak during observation could not be used for analysis. These results indicate that a UAV was successful to grasp spatial pattern of windbreak effects on maize growth. This study shows the potential of a UAV to be useful for saving time and money when we evaluate the effects of windbreaks on crop growth.

[1]  A. Mochizuki,et al.  Rice Growth Monitoring Using Small UAV and SfM-MVS Technique , 2016 .

[2]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[3]  N. A. Fairey.  YIELD, QUALITY AND DEVELOPMENT OF FORAGE MAIZE AS INFLUENCED BY DATES OF PLANTING AND HARVESTING , 1983 .

[4]  William E. Easterling,et al.  Modelling the effect of shelterbelts on maize productivity under climate change : An application of the EPIC model , 1997 .

[5]  M. Mkhabela,et al.  Early maize yield forecasting in the four agro-ecological regions of Swaziland using NDVI data derived from NOAA's-AVHRR , 2005 .

[6]  W. O. Willis,et al.  Windbreak Influence on Water Relations, Growth, and Yield of Soybeans 1 , 1974 .

[7]  Pablo J. Zarco-Tejada,et al.  Thermal and Narrowband Multispectral Remote Sensing for Vegetation Monitoring From an Unmanned Aerial Vehicle , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[8]  Jiaojun Zhu,et al.  Assessment of the effects of shelterbelts on crop yields at the regional scale in Northeast China , 2016 .

[9]  S. Kume,et al.  Effects of Maturing Stage of Corn Hybrids on Silage Yield, Feeding Value for Dairy Cows and Milk Production in a Cold Region of Japan , 2007 .

[10]  P. Peri,et al.  Windbreaks in southern Patagonia,Argentina: A review of research on growth models, windspeed reduction, and effects oncrops , 2002, Agroforestry Systems.

[11]  John B. Solie,et al.  Expression of Variability in Corn as Influenced by Growth Stage Using Optical Sensor Measurements , 2007 .

[12]  Comparison of Empirical Regression Models for Estimating LAI of Paddy Rice from Several Vegetation Indices Derived from UAV Images , 2016 .

[13]  Easson,et al.  Effects of plastic mulch, sowing date and cultivar on the yield and maturity of forage maize grown under marginal climatic conditions in Northern Ireland , 2000 .

[14]  R. C. Muchow,et al.  Model analysis of temperature and solar radiation limitations to maize potential productivity in a cool climate , 1995 .

[15]  John B. Solie,et al.  Growth Stage, Development, and Spatial Variability in Corn Evaluated Using Optical Sensor Readings , 2005 .

[16]  J. Coors,et al.  Kernel Milkline Stage and Corn Forage Yield, Quality, and Dry Matter Content , 1993 .

[17]  Dan Sun,et al.  Early growth of six native Australian tree species in windbreaks and their effect on potato growth in tropical northern Australia , 1997 .

[18]  Hiroyuki Torita,et al.  Relationship between shelterbelt structure and mean wind reduction , 2007 .

[19]  R. C. Muchow,et al.  Environmental control of phenology and leaf growth in a tropically adapted maize , 1989 .

[20]  K. Hubbard,et al.  The Response of Muskmelon Growth and Development to Microclimate Modification by Shelterbelts , 1999 .

[21]  T. Makino,et al.  Barrenness and Changes in Tassel Development and Flowering Habit of Hybrid Maize Associated with Low Air Temperatures , 2015 .

[22]  Stephan J. Maas,et al.  Large‐Area Maize Yield Forecasting Using Leaf Area Index Based Yield Model , 2005 .

[23]  早崎 有香,et al.  Rice Growth Monitoring Using Small UAV and SfM-MVS Technique , 2016 .

[24]  P. Bird.  Tree windbreaks and shelter benefits to pasture in temperate grazing systems , 2004, Agroforestry Systems.

[25]  S. Sankaran,et al.  Low-altitude, high-resolution aerial imaging systems for row and field crop phenotyping: A review , 2015 .

[26]  X. H. Zhou,et al.  Windbreaks in North American agricultural systems , 2004, Agroforestry Systems.

[27]  N. Silleos,et al.  The use of multi-temporal NDVI measurements from AVHRR data for crop yield estimation and prediction , 1993 .

[28]  K. Hubbard,et al.  Growth and Yield of Snap Beans as Affected by Wind Protection and Microclimate Changes due to Shelterbelts and Planting Dates , 2004 .

[29]  J. Kort.  9. Benefits of windbreaks to field and forage crops , 1988 .

[30]  E. W. Klosterman,et al.  Corn Plant Maturity. I. Changes in Dry Matter and Protein Distribution in Corn Plants1 , 1966 .

[31]  W. H. Paulson,et al.  Estimating Corn Growth, Yield, and Grain Moisture from Air Growing Degree Days and Residue Cover1 , 1987 .

[32]  K. Osaki.  Appropriate Luminance for Estimating Vegetation Index from Digital Camera Images , 2016 .