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Considering the influence of meteorological factors on maize production, in order to improve the yield of maize in Henan Province, a grey combination model is constructed to predict the yield of maize in Henan Province. Firstly, the yield of maize in 2017 is obtained by GM (1, 1) model; secondly, the trend yield of maize is obtained by HP filter method, then the meteorological yield of maize is obtained, and the yield of maize reduction is determined according to the meteorological yield. Combined with Markov model, the maize yield reduction in various cities in Henan Province is forecasted. Finally, based on the reduction of production, policy recommendations are made for maize production in Henan Province.

Henan Province is located in the middle and lower reaches of the Yellow River and the upper reaches of the Huaihe River. The land area is 167,000 square kilometers, accounting for 17.3 percent of the country’s total area. Henan is a major grain producing Province, the stability of food production is related to people’s basic needs. There are many factors affecting grain production: agricultural production technology, meteorological factors and policy price factors. Among them, as one of the main factors affecting grain production, the amount of meteorological factors investment is not controlled by humans. For meteorological disasters, all that people can do is to predict and prevent them in order to reduce their impact on grain production. The per unit area yield of maize in Henan Province was 5638 kg/hm^{2} in 2012 and fell to 5285 kg/hm^{2} in 2016 [

The following scholars used different methods to study maize production, including: Ma Shuqing et al. [

In addition, some scholars have studied the relationship between meteorological change and grain production. Wang Futang [

The above scholars predict maize yield single, or establish crop model to predict maize yield. Less prediction was made on maize yield reduction. The grey GM (1, 1) model works well for short-term prediction. The HP filter method is reasonable for separating crop trend yield. In addition, the Markov model is suitable for the prediction problem of data series with large random fluctuations. Therefore, after using the GM (1, 1) model to obtain the corn yield in Henan Province in 2017, the HP filter method is used to obtain the trend yield of maize, and then the meteorological yield of maize is obtained. Finally, markov model is used to predict the yield of maize in Henan Province. And we provided policy recommendations for future maize production in Henan Province.

In 1982, Professor Deng Julong published “The Control Problems of Grey systems” [

Grey GM (1, 1) model is mainly used in the prediction of grey system theory in prediction. Grey GM (1, 1) model requires less data, can be tested, and has a high precision in short-term prediction. It has been widely used in many scientific fields such as industry, agriculture, energy, transportation, geology, meteorology, hydrology, ecology, environment, medicine, military, economy, society and so on, and has successfully solved a large number of practical problems in production, life and scientific research. The modeling mechanism is as follows:

Let an original sequence be:

X ( 0 ) = { x ( 0 ) ( 1 ) , x ( 0 ) ( 2 ) , ⋯ , x ( 0 ) ( n ) } (1)

Of which: x ( 0 ) ( k ) ≥ 0 , k = 1 , 2 , ⋯ , n .

Perform an accumulation (1-AGO) generation on the sequence to get the sequence:

X ( 1 ) = { x ( 1 ) ( 1 ) , x ( 1 ) ( 2 ) , ⋯ , x ( 1 ) ( n ) } (2)

X ( 1 ) adjacent mean generating sequence is:

Z ( 1 ) = { z ( 1 ) ( 2 ) , z ( 1 ) ( 3 ) , ⋯ , z ( 1 ) ( n ) } (3)

Of which z ( 1 ) ( k ) = 1 2 ( x ( 1 ) ( k ) + x ( 1 ) ( k − 1 ) ) ,We call

x ( 0 ) ( k ) + a z ( 1 ) ( k ) = b (4)

the mean form of the GM (1, 1) model.

Let the model’s parameter list be a ^ = ( a , b ) T , then use the least squares method to find

a ^ = ( B T B ) − 1 B T Y

Y , B of them are,

Y = [ y ( 0 ) ( 2 ) y ( 0 ) ( 3 ) ⋮ y ( 0 ) ( n ) ] , B = [ − z ( 1 ) ( 2 ) 1 − z ( 1 ) ( 3 ) 1 ⋮ ⋮ − z ( 1 ) ( n ) 1 ] (5)

We call

d x ( 1 ) d t + a x ( 1 ) = b (6)

the whitening differential equation of x ( 0 ) ( k ) + a z ( 1 ) ( k ) = b .

Then the solution of Equation (6) is

x ^ ( 1 ) ( t ) = ( x ( 0 ) ( 1 ) − b a ) e − a ( t − 1 ) + b a (7)

Then the simulated prediction difference form of the original sequence is

x ^ ( 0 ) ( k ) = x ^ ( 1 ) ( k ) − x ^ ( 1 ) ( k − 1 ) = ( 1 − e a ) ( x ( 0 ) ( 1 ) − b a ) e − a ( k − 1 ) (8)

In this model, a is the development coefficient and b is the grey effect.

The research object of Markov Probability Matrix Prediction model is a stochastic dynamic system. Markov model is effective in predicting stochastic volatility problems. It predicts the development of the system according to the transition probability between states. The transition probability reflects the degree of influence of each random factor and the inherent regularity of each state. Markov Probability Matrix is suitable for predicting data columns with large random fluctuations [

The Markov forecasting model [

Let P i j ( k ) = P ( X m + k = j | X m = i ) , ( i , j ) ∈ I ,which denote the probability that the system is in the state j at time m + k under the condition that the system in the state i of at the moment m. That is, the transition probability of the k step is experienced; by sequentially sorting P i j ( k ) , the following matrix can be obtained.

P ( k ) = [ P 11 ( k ) P 12 ( k ) ⋯ P 1 n ( k ) P 21 ( k ) P 22 ( k ) ⋯ P 2 n ( k ) ⋮ ⋮ ⋮ P n 1 ( k ) P n 2 ( k ) ⋯ P n n ( k ) ] (9)

Then the matrix is the k-step transition probability matrix of Markov chain. Of which: ∑ j = 1 n P i j ( k ) = 1 .

If M i j ( k ) is the number of original data samples transferred from state ⊗ i to state ⊗ j through k steps, and M i is the number of original data samples in state ⊗ i , then call

P i j ( k ) = M i j ( k ) M i ( i , j = 1 , 2 , ⋯ , n ) (10)

the state transition probability. Since the final state of the data sequence is not clear, the last k data in the data sequence should be removed when calculating M i .

The HP filtering method is a decomposition method of time series in state space. It assumes that the time series consists of two parts, namely: long-term trend component and short-term fluctuation component. The HP filter method can be considered as a high-pass filter that can separate high-frequency components with periods below 8a [

Maize yield sequence { y t } is divided into trend yield g t and fluctuating yield c t by HP filter, in which g t is a stable high frequency trend signal and c t is an unstable low frequency disturbance signal. The principle is to remove the low frequency signal c t by filtering, thereby detecting the high frequency signal g t from the production sequence. HP filter decomposes y t into: y t = g t + c t .

The detection method of high frequency signal g t is often defined as the solution of the following minimization formula, namely:

min { ∑ t = 1 T ( y t − g t ) + λ ∑ t = 1 T [ ( g t + 1 − g t ) − ( g t − g t − 1 ) ] 2 } (11)

In the formula, λ is the HP filter parameter. When λ = 0 , the minimized solution of the function is { y t } sequence. With the increase of λ value, the trend of minimum solution estimation is smoother. The general experience of λ is as follows:

λ = { 100 , annualdata 1600 , quarterlydata 14400 , monthlydata (12)

Crop yield is usually decomposed into three parts: trend yield, meteorological yield and random error. The trend output reflects the long-period production component of the level of productivity development in the historical period, also known as technical output. Meteorological production is a volatility that is affected by short-period changes in meteorological factors [

Y = Y w + Y t + e (13)

where Y represents the actual yield of maize, Y t is the trend yield, which represents the contribution of social and economic conditions and technological level of productivity to grain production. Y w is meteorological production, which characterizes the contribution of climate fluctuations to actual production. e is the component of yield affected by some random factors, which accounts for a small proportion and is often neglected in practical calculation. In general, the level of agricultural technology is gradually improved, so trend yield is usually a function of time, and its influence on yield is a more smooth process in time series, especially in a larger geographical area [

The yield data set of Henan maize in 2006-2016 comes from Henan Statistics Bureau [

The trend yield of maize in 18 cities of Henan Province from 2006 to 2016 is obtained by using HP filter method. The calculated results are shown in

For the year when the meteorological yield is less than 0, we set the year of production reduction. For the year when the meteorological yield is greater than 0, we set the year of production increase. Therefore, according to the Markov prediction model, the reduction of yield in various places can be predicted. Take Zhengzhou City as an example, set a reduction of production to M 1 , and increase production to M 2 , therefore

M 11 ( 1 ) = 2 , M 12 ( 1 ) = 4 , M 21 ( 1 ) = 4 , M 22 ( 1 ) = 1

The state transition probability is

p 11 ( 1 ) = 1 3 , p 12 ( 1 ) = 2 3 , p 21 ( 1 ) = 4 5 , p 22 ( 1 ) = 1 5

2006 | 2007 | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|

Zhengzhou | 4940.38 | 4985.98 | 4870.00 | 4908.00 | 4828.00 | 4727.00 | 4818.00 | 4758.00 | 4562.79 | 4788.92 | 4593.14 | 4588.31 |

Kaifeng | 5459.22 | 5595.66 | 5505.00 | 5432.00 | 5866.00 | 5372.00 | 5465.00 | 5511.00 | 5359.33 | 5498.72 | 5246.42 | 5336.66 |

Luoyang | 4960.82 | 5210.71 | 4990.00 | 4943.00 | 4407.00 | 4652.00 | 4814.00 | 4755.00 | 4173.38 | 4796.20 | 4565.16 | 4405.71 |

Pingdingshan | 4722.73 | 4917.77 | 5062.00 | 4746.00 | 4738.00 | 4689.00 | 4872.00 | 4839.00 | 3369.38 | 4801.78 | 4617.68 | 4308.35 |

Anyang | 6140.49 | 6596.96 | 6682.00 | 6564.00 | 6461.00 | 6383.00 | 6548.00 | 6521.00 | 6464.32 | 6606.11 | 6172.56 | 6351.29 |

Hebi | 6538.60 | 6987.25 | 7080.00 | 6982.00 | 6897.00 | 6903.00 | 7005.00 | 7076.00 | 7007.03 | 7040.97 | 6830.27 | 6950.2 |

Xinxiang | 5810.60 | 6105.38 | 6158.00 | 6139.00 | 6083.00 | 6085.00 | 6338.00 | 6251.00 | 6228.04 | 6257.00 | 5481.13 | 5991.63 |

Jiaozuo | 7143.97 | 7434.48 | 7325.00 | 7307.00 | 7290.00 | 7270.00 | 7435.00 | 7409.00 | 7222.61 | 7240.23 | 6948.33 | 7128.25 |

Puyang | 6356.16 | 6684.94 | 6754.00 | 6601.00 | 6512.00 | 6437.00 | 6531.00 | 6346.00 | 6957.39 | 6592.94 | 6124.03 | 6395.79 |

Xuchang | 6235.63 | 6295.53 | 6689.00 | 6379.00 | 6450.00 | 6163.00 | 6271.00 | 6288.00 | 5891.07 | 6311.49 | 6218.68 | 6092.37 |

Luohe | 6429.01 | 6337.09 | 6185.00 | 5925.00 | 6037.00 | 6048.00 | 6450.00 | 6450.00 | 6308.65 | 6361.34 | 6495.89 | 6471.65 |

Sanmenxia | 4375.00 | 4704.17 | 4437.00 | 4384.00 | 4369.00 | 4280.00 | 4429.00 | 4334.00 | 4031.92 | 4180.53 | 4171.98 | 4060.14 |

Nanyang | 5295.35 | 5579.64 | 5569.00 | 5496.00 | 5468.00 | 5377.00 | 5551.00 | 5466.00 | 4924.29 | 5175.87 | 4910.57 | 4983.65 |

Shangqiu | 6206.25 | 6690.63 | 6693.00 | 6695.00 | 6613.00 | 6631.00 | 6664.00 | 6556.00 | 6351.80 | 6401.98 | 6150.62 | 6260.32 |

Xinyang | 5513.61 | 4943.29 | 4012.00 | 4310.00 | 4167.00 | 4384.00 | 4497.00 | 4494.00 | 4442.81 | 4476.29 | 6536.39 | 5362.3 |

Zhoukou | 6712.17 | 6119.65 | 5830.00 | 5451.00 | 5491.00 | 5954.00 | 6270.00 | 6081.00 | 5830.39 | 6241.08 | 5969.75 | 6110.87 |

Zhumadian | 5844.32 | 4955.39 | 4935.00 | 4971.00 | 5059.00 | 5225.00 | 5381.00 | 5399.00 | 5260.43 | 5441.36 | 5244.28 | 5482.4 |

Jiyuan | 4985.95 | 5344.99 | 5342.00 | 5233.00 | 5206.00 | 5219.00 | 5342.00 | 5356.00 | 5105.63 | 5198.11 | 5077.38 | 5127.7 |

Unit: kg/ha.

2006 | 2007 | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|

Zhengzhou | 4969.87 | 4935.68 | 4901.20 | 4866.64 | 4831.88 | 4797.24 | 4762.99 | 4728.70 | 4694.49 | 4660.76 | 4626.62 | 4592.44 |

Kaifeng | 5560.89 | 5551.69 | 5541.48 | 5529.68 | 5515.34 | 5496.54 | 5474.88 | 5450.70 | 5424.23 | 5396.33 | 5367.20 | 5338.05 |

Luoyang | 5052.66 | 4986.95 | 4920.31 | 4854.07 | 4790.25 | 4731.76 | 4677.65 | 4626.22 | 4577.10 | 4531.21 | 4485.45 | 4439.35 |

Pingding shanNaNn | 4924.43 | 4880.11 | 4833.77 | 4783.77 | 4730.76 | 4674.99 | 4616.81 | 4556.69 | 4497.67 | 4445.59 | 4395.02 | 4344.09 |

Anyang | 6460.09 | 6473.92 | 6484.55 | 6490.03 | 6490.35 | 6486.26 | 6478.23 | 6465.67 | 6448.71 | 6428.01 | 6404.41 | 6380.51 |

Hebi | 6835.54 | 6868.02 | 6897.53 | 6922.29 | 6942.36 | 6958.37 | 6970.51 | 6978.43 | 6982.11 | 6982.50 | 6980.81 | 6978.84 |

Xinxiang | 6047.38 | 6071.29 | 6092.84 | 6109.99 | 6121.37 | 6125.89 | 6122.09 | 6108.09 | 6084.17 | 6052.04 | 6014.85 | 5977.79 |

Jiaozuo | 7323.85 | 7323.56 | 7321.48 | 7316.90 | 7309.18 | 7297.57 | 7281.11 | 7258.59 | 7230.32 | 7198.14 | 7163.77 | 7129.40 |

Puyang | 6582.70 | 6581.14 | 6577.32 | 6570.00 | 6559.73 | 6547.36 | 6533.25 | 6516.69 | 6496.90 | 6471.44 | 6442.45 | 6413.27 |

Xuchang | 6406.48 | 6390.25 | 6372.30 | 6350.00 | 6323.83 | 6294.62 | 6264.42 | 6233.98 | 6204.10 | 6176.15 | 6148.35 | 6120.26 |

Luohe | 6214.08 | 6210.12 | 6208.31 | 6212.07 | 6224.58 | 6246.17 | 6275.26 | 6308.32 | 6343.55 | 6380.58 | 6418.67 | 6456.91 |

Sanmenxia | 4531.40 | 4495.15 | 4457.34 | 4418.49 | 4378.92 | 4338.62 | 4297.45 | 4254.73 | 4211.04 | 4167.80 | 4124.61 | 4081.20 |

Nanyang | 5551.14 | 5525.17 | 5496.63 | 5463.52 | 5424.56 | 5378.76 | 5325.62 | 5264.56 | 5197.31 | 5127.59 | 5056.37 | 4985.14 |

Shangqiu | 6583.45 | 6589.26 | 6591.29 | 6586.80 | 6574.03 | 6552.34 | 6521.44 | 6481.87 | 6435.55 | 6385.18 | 6332.59 | 6279.81 |

Xinyang | 4657.31 | 4602.79 | 4556.84 | 4531.42 | 4533.05 | 4566.04 | 4631.03 | 4726.84 | 4850.96 | 4998.55 | 5160.67 | 5323.19 |

Zhoukou | 6120.11 | 6063.61 | 6013.03 | 5974.86 | 5953.73 | 5949.07 | 5955.66 | 5968.34 | 5985.07 | 6004.97 | 6025.59 | 6046.85 |

Zhumadian | 5232.28 | 5214.10 | 5202.05 | 5199.64 | 5207.75 | 5224.96 | 5248.34 | 5275.00 | 5303.34 | 5333.03 | 5363.28 | 5394.42 |

Jiyuan | 5216.68 | 5226.61 | 5234.24 | 5238.43 | 5239.15 | 5236.29 | 5229.43 | 5217.95 | 5202.38 | 5184.61 | 5165.60 | 5146.39 |

Unit: kg/ha.

The state transition probability matrix is

P 1 ( 1 ) = [ 1 3 2 3 4 5 1 5 ]

As Zhengzhou maize is in the state of reduced production in 2017, according to the maximum probability criterion, it can be obtained

max { p 11 ( 1 ) , p 12 ( 1 ) } = p 12 ( 1 ) = 2 3

Therefore, it can be found that Zhengzhou 2018 maize will increase production.

The same principle is available that the state transition probability matrix of Kaifeng City, Luoyang City, Pingdingshan City, Anyang City, Hebi City, Xinxiang City, Jiaozuo City, Fuyang City, Xuchang City, Luohe City, Sanmenxia City, Nanyang City, Shangqiu City, Xinyang City, Zhoukou City, Zhumadian City and Jiyuan City are as follows:

P 2 ( 1 ) = [ 3 7 4 7 1 0 ] , P 3 ( 1 ) = [ 1 4 3 4 3 7 4 7 ] , P 4 ( 1 ) = [ 0 1 5 3 8 5 8 ] , P 5 ( 1 ) = [ 1 2 1 2 2 7 5 7 ] , P 6 ( 1 ) = [ 1 2 1 2 2 7 5 7 ] , P 7 ( 1 ) = [ 1 4 3 4 2 7 5 7 ] , P 8 ( 1 ) = [ 1 2 1 2 3 5 2 5 ] , P 9 ( 1 ) = [ 2 3 1 3 2 5 3 5 ] , P 10 ( 1 ) = [ 1 4 3 4 3 7 4 7 ] , P 11 ( 1 ) = [ 2 3 1 3 2 5 3 5 ] , P 12 ( 1 ) = [ 1 2 1 2 3 5 2 5 ] , P 13 ( 1 ) = [ 1 4 3 4 3 7 4 7 ] P 14 ( 1 ) = [ 1 3 2 3 1 4 3 4 ] , P 15 ( 1 ) = [ 7 8 1 8 1 3 2 3 ] , P 16 ( 1 ) = [ 2 5 3 5 1 2 1 2 ] , P 17 ( 1 ) = [ 1 2 1 2 3 5 2 5 ] , P 18 ( 1 ) = [ 1 2 1 2 3 5 2 5 ]

Thus, Kaifeng, Luoyang, Pingdingshan, Xinxiang, Jiaozuo, Xuchang, Luohe, Nanyang, Shangqiu and Xinyang may increase their maize production in 2018, while Puyang and Zhumadian will reduce production. Anyang, Hebi, Jiaozuo, Sanmenxia, Zhoukou, Jiyuan, these areas maize production increase or decrease situation can not be determined.

The geographical location of 18 cities in Henan Province is shown in

According to the forecast, it is found that most areas of Henan Province increased production in 2018, which is more in line with the actual situation. Although the meteorological anomalies will make maize yield decrease to a certain extent, with the improvement of scientific and technological level, People take precautions against meteorological anomalies in advance and compensate the yield losses caused by meteorological anomalies to a certain extent, thus increasing maize production in Henan Province on the whole.

Through improving the varieties of maize, selecting and cultivating new varieties with strong resistance, and adjusting the production structure of grain, it can improve its ability to defend against meteorological anomalies, reduce the losses caused by agricultural meteorological anomalies, and ensure stable production and increase of grain production.

We should vigorously strengthen the construction of farmland and water conservancy and do a good job in the construction of agricultural infrastructure projects such as well irrigation and water-saving irrigation. It is necessary to promote the comprehensive utilization of straw returning to the field, protect the ecological environment, avoid over-exploitation and utilization of land resources, and improve the quality of farmland.

Strengthen research on the prevention of agrometeorological disasters. It is necessary to increase the systematic analysis of disasters and explore its complex background to develop detailed supporting defense measures. Through the research on the disaster early warning system, the ability to predict and forecast disasters will be improved. Conduct in-depth research on meteorological anomalies and other disciplines to improve the response of maize to meteorological anomalies and reduce the impact of meteorological anomalies on maize yield.

In order to improve grain production and ensure national grain security, considering the influence of meteorological factors on grain production, the grain production reduction in Henan Province was predicted in order to prevent the grain production in advance. GM (1, 1) model is used to predict the yield of maize in 2017 in Henan Province, the trend yield of maize is obtained by using HP filter method, and then the meteorological yield is obtained. Finally, Markov model is used to predict the yield of maize in 18 cities of Henan Province in 2018. The results showed that maize production in most cities of Henan Province is likely to increase in 2018, and only a small part of the maize may reduce production. This has a great relationship with the improvement of science and technology and agricultural production facilities in Henan Province. Strengthening the prediction of meteorological anomalies and doing a good job of prevention will have a great impact on ensuring stable production and increasing yield of maize in Henan Province.

The authors are grateful to anonymous referees for their helpful and constructive comments on this paper. The work was supported by the Soft-science Foundation of Henan Province (172400410015), and the Philosophy and Social Program of Henan Province (2016BJJ022).

The authors declare no conflicts of interest regarding the publication of this paper.

Li, B.J. and Zhu, X.X. (2018) Forecast of Maize Production in Henan Province. American Journal of Plant Sciences, 9, 2276-2286. https://doi.org/10.4236/ajps.2018.911164