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Attribution| 4.0 International LicenseStudy on the Temporal and Spatial Variability of Maize Yield in Precision Operation Area
Yueling Zhao, Haiyan Han, Liying Cao, Li Ma, Guifen Chen
To cite this version:
Yueling Zhao, Haiyan Han, Liying Cao, Li Ma, Guifen Chen. Study on the Temporal and Spatial
Variability of Maize Yield in Precision Operation Area. 8th International Conference on Computer and
Computing Technologies in Agriculture (CCTA), Sep 2014, Beijing, China. pp.714-719, �10.1007/978-
3-319-19620-6_77�. �hal-01420288�
________ 1 Study on the temporal and spatial variability of maize yield in
precision operation area
Yueling Zhao
1, Haiyan Han
2, Liying Cao
1, Li MA
1,Guifen Chen*
11 Jilin agricultural university , Jilin 130118; 2 Changchun university , Jilin 130000,
Jilin province is an important agricultural production resource in our country. Study on the temporal and spatial variability of maize yield are significant to understand the potential nutrients in soil, and guide agricultural practice for the recent years in precision operate area. The every year yield distribution belonged to the normal distribution by testing K-Sand the coefficient of variation of them were 16.33%, 11.85%, 12.90% respectively. That precise operation can reduce the yield difference in every operate cell. As a result, the temporal and spatial variability of maize yield can be successfully applied to yield prediction in Jilin Province. Partition results can not only guide soil nutrient evaluation, and can be used to implement variable input and precise fertilization recommendation.
Key Words: temporal and spatial variability,maize yield,precision agriculture
1. INTRODUCTION
It is directly affect agricultural production regenerative potential, that yield prediction status are understood, so the maize yield level of the comprehensive analysis and evaluation is very necessary, it is the use of agricultural resources and technology in an effective way. In precision fertilization can be employed in management zones instead of grid sampling technology, and the extensive application[1-3]. In recent years, with the precision production
technology system improvement and application in our country, some researcher used crop yield data of many years in comprehensive analysis in agricultural management partition extraction research.
With the precise operation technology in agricultural production in China continues to expand and application of crop production, continuously improved, the maize problem is getting more and more attention under the background, how to predict the future of food production for a period of time has become a research hotspot[4]. To investigate the relationship between the precision maize yield and time series, and the results of the comprehensive analysis and study is very necessary, it is a way of effective use of agricultural resources and technology. Through the analysis of
precision for variation characteristics of maize yield, on the one hand can be scientific, reasonable, effective evaluation on it, on the other hand, the workers engaged in agricultural production not only can improve the use efficiency of soil nutrients, but also save the resources, obtain better economic benefits, to protect the ecological agriculture resources and the quality of environment. The temporal and spatial variability of maize yield were studied for some years in precision area in the paper.
2 MATERIALS AND METHODS
2.1 the Situation of the Study Area
The study area is located in yushu City of Jilin Province. The researcher area was located at latitude 44.999369 - 45.002761, longitude 126.3139 -126.31671. The genus sub-humid temperate continental monsoon climate, annual average temperature is 4.6 ℃ -5.6 ℃, annual precipitation is the middle of in 500 -600mm, and the accounting for 90% of annual rainfall is concentrated in the warm season, the soil is more fertile, soil types are suitable for planting maize, bean, rice and other various crops. The distribution map of soil samples can be shown from the Fig.1.
2
5—D6 5—D5 5—D4 5—D3 5—D2 5—D1
5—C6 5—C5 5—C4 5—C3 5—C2 5—C1
5—B6 5—B5 5—B4 5—B3 5—B2 5—B1
5—A6 5—A5 5—A4 5—A3 5—A2 5—A1 1—F5
1—F4
1—F3
1—F2
1—F1 1—E8
1—E7
1—E6
1—E5
1—E4
1—E3
1—E2
1—E1 1—D8
1—D7
1—D6
1—D5
1—D4
1—D3
1—D2
1—D1 1—C8
1—C7
1—C6
1—C5
1—C4
1—C3
1—C2
1—C1 1—B8
1—B7
1—B6
1—B5
1—B4
1—B3
1—B2
1—B1 1—A8
1—A7
1—A6
1—A5
1—A4
1—A3
1—A2
1—A1
7-M9 7-M8
7-M7 7-M6
7-M5 7-M4
7-M3 7-M2
7-M1 7-L9
7-L8 7-L7
7-L6 7-L5
7-L4 7-L3
7-L2 7-L1 7-K9
7-K8 7-K7
7-K6 7-K5
7-K4 7-K3
7-K2 7-K1 7-J9
7-J8 7-J7
7-J6 7-J5
7-J4 7-J3
7-J2 7-J1 7-I9
7-I8 7-I7
7-I6 7-I5
7-I4 7-I3
7-I2 7-I1 7-H9
7-H8 7-H7
7-H6 7-H5
7-H4 7-H3
7-H2 7-H1 7-G9
7-G8 7-G7
7-G6 7-G5
7-G4 7-G3
7-G2 7-G1 7-F9
7-F8 7-F7
7-F6 7-F5
7-F4 7-F3
7-F2 7-F1 7-E9
7-E8 7-E7
7-E6 7-E5
7-E4 7-E3
7-E2 7-E1 7-D9
7-D8 7-D7
7-D6 7-D5
7-D4 7-D3
7-D2 7-D1 7-C9
7-C8 7-C7
7-C6 7-C5
7-C4 7-C3
7-C2 7-C1 7-B9
7-B8 7-B7
7-B6 7-B5
7-B4 7-B3
7-B2 7-B1 7-A9
7-A8 7-A7
7-A6 7-A5
7-A4 7-A3
7-A2 7-A1
3-L9 3-L8 3-L7 3-L6 3-L5 3-L4 3-L3 3-L2
3-K9 3-K8 3-K7 3-K6 3-K5 3-K4 3-K3 3-K2 3-K1
3-J9 3-J8 3-J7 3-J6 3-J5 3-J4 3-J3 3-J2 3-J1
3-I9 3-I8 3-I7 3-I6 3-I5 3-I4 3-I3 3-I2 3-I1
3-H9 3-H8 3-H7 3-H6 3-H5 3-H4 3-H3 3-H2 3-H1
3-G9 3-G8 3-G7 3-G6 3-G5 3-G4 3-G3 3-G2 3-G1
3-F9 3-F8 3-F7 3-F6 3-F5 3-F4 3-F3 3-F2 3-F1
3-E9 3-E8 3-E7 3-E6 3-E5 3-E4 3-E3 3-E2 3-E1
3-D9 3-D8 3-D7 3-D6 3-D5 3-D4 3-D3 3-D2 3-D1
3-C9 3-C8 3-C7 3-C6 3-C5 3-C4 3-C3 3-C2 3-C1
3-B9 3-B8 3-B7 3-B6 3-B5 3-B4 3-B3 3-B2 3-B1
3-A9 3-A8 3-A7 3-A6 3-A5 3-A4 3-A3 3-A2 3-A1
3-L103-K103-J10 3-I11 3-I10
3-H11 3-H10
3-G11 3-G103-B103-F103-E103-D103-C10
³
Fig.1 distribution map of soil samples 2.2 Process and Methods
We used DGPS(Differential Global Satellite Positioning System) devices in USA company Trimble for accurate positioning, set point, draw the boundary line and on the acquisition of land area. Then the researchers are back to the lab with the data by using ArcGIS software to the plots of the data collected by the spacing is according to 40m*40m as a unit area of the anchor point, classification, determination sampling points and sampling points map making. The actual measured area of a sample, the number of samples of corn plants, rough calculation can count per plant grain number per unit area yield of maize, the calculation according to the following formula: 1000 grain weight (grams) * Mu * panicle number /1000000 = kg / mu, and the yield of unit conversion into kg.ha-1. The results can be shown from Tab.1
2.3 Data Analysis
All the experimental data were analysed using SPSS 18.0 and ArcGIS as tools in the processing. At first, the samples data were standardized, then in order to achieve data sharing, the maize yield results are be saved to the excel table and input to the precision agricultural database.
A quantitative tool of geostatistics as a soil variable space analysis, can be analysed by variation function, discrimination, contrast observation data, it is used to describe the spatial difference between the variables of the form of function, it is a kind of distance measure function H. It describes the similarity between the two sampling points, the similarity decreases as the distance between sampling points. When the distance between them to a certain extent, they often have no correlation, this time they will become two independent. It is a tool to describe the function of the spatial variability of soil properties in different distance between observation and reflect the changes of values, so the half of the definition of semi variance function to describe the variance the difference between the two sampling points, the formula is as follows[5-6]:
( )1
)]
2( ) ( ) [ ( 2 ) 1 (
h N
i
i
i
h z x
x h z
h N
In the formula, h is the space distance of the sample points, also known as step or potential difference (Lag); N (h) represents the spatial isolation distance apart as the number of samples h. The value of Z (Xi) and Z (xi+h) were measured in two position. γ(h) is the value of the semi-variance function. Variation as a function of distance is the semi variance of H, and increases with the increase of H in the semi variance function within a certain range, but when the maximum distance between the observation point is greater than the distance between the regional variables, the values of the function tends to a constant, and contains steady state.
Tab.1 The measured data of output in 2004
NO. LON LAN Yield
(kg.ha-1) NO. LON LAN Yield
(kg.ha-1) 3-A1 126.31574 45.002761 7812.4 3-C8 126.31573 44.999805 7869.4 3-A2 126.31586 45.002414 7354.1 3-C9 126.31585 44.999458 8077.2 3-A3 126.31598 45.002066 7561.8 3-C10 126.31427 45.002498 8102.7 3-A4 126.3161 45.001718 6921.5 3-D1 126.31439 45.00215 7589.6 3-A5 126.31623 45.001371 6219.5 3-D2 126.31451 45.001803 7687.5 3-A6 126.31635 45.001023 6423.7 3-D3 126.31463 45.001455 6986.4 3-A7 126.31647 45.000676 6635.4 3-D4 126.31475 45.001107 7152.8 3-A8 126.31659 45.000328 7324.1 3-D5 126.31488 45.00076 7354.6
3-A9 126.31671 44.99998 6523.7 … … …. ….
3-A10 126.31525 45.002674 6952.2 3-K3 126.31242 44.998843 9482.7 3-A1 126.31574 45.002761 7812.4 3-K4 126.31083 45.001884 8897.7 3-A2 126.31586 45.002414 7354.1 3-K5 126.31095 45.001536 8046.2
… … …. …. 3-K6 126.31107 45.001188 8157.9
3-C7 126.31561 45.000152 8260.6 3-K7 126.31119 45.000841 7982.5 3-C8 126.31573 44.999805 7869.4 3-K8 126.31132 45.000493 8534.6
3 RESULTS AND ANALYSIS
3.1 the Descriptive Statistics of Soil Nutrient Data
In the study of soil science, the magnitude coefficient of variation (CV) usually represented spatial variability strength of soil nutrient characteristics . It is usually weak variability if variation coefficient of CV≤ 0.1, 0.1<CV<1 as moderate
variability, CV ≥ 1 strong variability [7-8]. Study on the variation characteristics of maize yield is each are not identical, soil of different yield properties have bigger difference.
Table 2 Attribute characteristics in different years of production
Case Mean SD Variance Kurtosis CV% Min Max K-S 2004 8422.668 1375.178 0.605 0.31 16.33 6005.8 12328.2 0.702 2007 9069.098 1170.657 -0.3 -1.02 12.9 6606.4 10986.4 0.139 2009 7303.635 865.239 0.105 0.33 11.85 5182.23 9765.11 0.966
The cell yield of these three years by K-S test, its distribution accords with the normal distribution, The average maize yield is the mean 8422.67kg.ha-1 in 2004, the average maize yield is average 9069.1 kg.ha-1 in 2007, the average maize yield is 7303.63 kg.ha-1in 2009 from the table 2.The coefficient of variation of them were 16.33%, 11.85%, 12.90%, coefficient of variation, as can be shown in Figure 2.The yield decreased gradually, and engaged in precision during the operation, the coefficients of variation were less than 20%, belongs to the medium variation.
4
3.2 the temporal and spatial variability of maize yield
Observation of 2004 to yield spatial distribution map of 5-14 in 2009 2004, can be seen in the cell changes in output frequency is larger, and with the precision of the year by year, change of output frequency in cells decreased obviously, the first two years of the analysis of production trends can be seen, the east-west direction on the production line is steeper, after several wheel precise operation, each cell analysis of the yield curve changes in the east-west direction, they yield trend line close to the line is smooth, it also shows that the spatial variation of yield decreased, difference in. That precise operation can reduce the yield difference within a cell.
Fig. 2 Three years of variation coefficient of yield comparison chart
Fig.3 The spatial attribute graph of output in 2004
Fig.4 The spatial attribute graph of output in 2007
Fig.5 The spatial attribute map of output in 2009
4 RESULTS AND DISCUSSION
In the actual process of agricultural production, researchers know Soil Nutrient timing variability exists, don't also aware of the crop yield in the study area also exists obvious spatio-temporal variation. The application of traditional time the total output of grain in Shandong province of the prediction model and the ARIMA model predicts that the regression equation by Zhang Xiaojie et al. The prediction effect is relatively good [9]. Of course, the variability of maize yield is affected by many factors, such as variety, environment, climate, plant diseases and insect pests, cultivation mode and management mode, through the analysis of precision operation area in recent years, soil nutrient and yield data, mining and grasp the changes of crop yield in space and time, the characteristic of temporal variation of maize yield was given in recent years.
5 ACKNOWLEDGMENTS
This work is supported by the Jilin province education science planning projects (Grant No. GH13180) ; Jilin Agricultural University general topic (Grant No. 2012xjyb048); department of education of Jilin Province (Grant No.
2013(68) ); Science and TechnologyBureau of Changchun city ( Grant No. 13KG71 ). The authors also gratefully acknowledge the helpful comments and suggestions of the reviewers, which have improved the presentation.
References and Notes
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2. Schepers A R, Shanahan J F, Liebig M K, Schepers J S, Johnson S H, Jr A L. Appropriateness of management zones for characterzing spatial variability of soil properties and irrigated corn yields across years. Agronomy Journal, 96:195- 203.(2004).
3. Wu xiao-lei, Wang da-qing, Xu bo, et al. Spatial Variability of Black soil Nutrients in Hilly Land Chinese journal of Soil Science 41(4) :825-829,(2010).
4. Heidy Sanchez Lizarraga, Carlo G. Lai.Effects of spatial variability of soil properties on the seismic response of an embankment dam.Soil Dynamics and Earthquake Engineering, 2014,64(9), 113-128
5.Tiejun Wang, Trenton E. Franz, Vitaly A. Zlotnik,et al. Investigating soil controls on soil moisture spatial variability:
numerical simulations and field observations Journal of Hydrology[J].Accepted Manuscript, Available online 16 March 2015
6.Miriam Glendell, Steve J. Granger, Roland Bol, et al. Quantifying the spatial variability of soil physical and chemical properties in relation to mitigation of diffuse water pollution[J].Geoderma, 2014, 214–215(2), 25-41.
7.C. Narendra Babu, B. Eswara Reddy .A moving-average filter based hybrid ARIMA–ANN model for forecasting time series dataOriginal Research Article Applied Soft Computing, 2014, 23(10):27-38
8. George C. Tiao Time Series: ARIMA Methods International Encyclopedia of the Social & Behavioral Sciences (Second Edition), 2015, 316-321
9. Zhan xiao jie, Zhang ziliang .Application of time series analysis model on total corn yield of Shandong province [J].
Research of water conservation , 2007,14(3):309-311.