农产品监测预警模型集群构建理论方法与应用

doi:10.3864/j.issn.0578-1752.2020.14.010

摘要/Abstract

摘要：

【目的】农产品供给与需求的准确分析测定,是农业监测预警能力提升的重要表现。构建产品多品种多环节模型集群理论方法,可高效解决单一环节或单一模型难以解决的分析技术难题。【方法】在农产品供需的重要要素即生产量、消费量、贸易量、价格等分析预测过程中,针对农产品品种间关联性强,自然、社会、经济诸多影响因素纠缠,模型多变量强耦合、非线性、参数时变的特点,提出多品种农产品“因素分类解耦、参数转用适配”方法,以构建多时空维度的监测预警模型集群。【结果】利用“因素分类解耦、参数转用适配”技术方法,研究构建了不同农产品的生产类、消费类、贸易类、价格类的模型集群。这些模型集群可用于对不同时空维度的水稻、玉米、小麦、肉类等主要农产品供需的长中短期的分析预测,支撑形成了农业展望中的主要农产品平衡表,其中主要农产品全国年度生产量6年平均预测精度高于97%。【结论】研究提出的农产品监测预警模型集群构建理论及其方法,有效提升了农产品多品种模型集群的求解效率和准确率,增强了农产品供需分析预测的系统性与智能性,为系统揭示农产品复杂的时空供需变化特征、促进农产品市场调控科学性和可预见性,提供了新技术方法。

关键词: 农产品, 多品种, 供需预测, 因素分类解耦, 参数转用适配, 模型集群

Abstract:

【Objective】The accurate prediction and evaluation of agricultural product supply and demand is an important manifestation for the improvement of agricultural monitoring and early warning capabilities. A multi-variety multi-link model cluster to construct that can efficiently solve analytical technical problems which are difficult to solve with single links or single models.【Method】 The methodology characterized by "factor classification decoupling, parameter conversion adaptation" for multi-variety agricultural products was proposed to build a multi-temporal dimension monitoring and early warning model cluster, which took into account the important factors of agricultural products supply and demand, namely production, consumption, trade volume, price, etc., the strong linkage among commodities, the entangled complex natural, social and economic factors, and the multivariate strong coupling, non-linear, time-varying characteristics of parameters in the model development.【Result】 The model clusters were developed covering production, consumption, trade and price for different agricultural products, based on the "factor classification decoupling, parameter conversion adaptation" methodology. These model clusters could be used to analyze and project the supply and demand situation of major agricultural products including rice, corn, wheat and meat in different spatial and temporal dimensions, and to support the generation of major agricultural products balance sheets in the China Agricultural Outlook Report. The 6-year average forecast accuracy was higher than 97%.【Conclusion】The methodology of agricultural monitoring and early warning model cluster proposed in the paper has effectively improved the solution efficiency and accuracy of agricultural product multi-variety model clusters, enhanced the systematic and intelligent analysis and projection of agricultural supply and demand. The research provided a new technical method for systematically revealing the complex characteristics of supply and demand of agricultural products in time and space, and promoting the scientific and predictable regulation of agricultural products market.

Key words: agricultural products, multi varieties, supply and demand forecast, factor classification decoupling, parameter conversion and adaptation, model cluster

许世卫, 邸佳颖, 李干琼, 庄家煜. 农产品监测预警模型集群构建理论方法与应用[J]. 中国农业科学, 2020, 53(14): 2859-2871.

XU ShiWei, DI JiaYing, LI GanQiong, ZHUANG JiaYu. The Methodology and Application of Agricultural Monitoring and Early Warning Model Cluster[J]. Scientia Agricultura Sinica, 2020, 53(14): 2859-2871.

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图/表 4

表1

农产品监测预警模型集群构建需考虑的主要影响因素"

模型变量 Model variable f(x)			影响因素Influence factor(x_i)
生产量 Production quantity (QP)	作物单产 Yield	气象单产 Meteorological yield	温度、日照时数、降水量等 Temperature, sunshine duration, precipitation, etc.
		投入单产 Input yield	成本收益情况、费用和用工情况、化肥种子投入、科技等 Cost-benefit situation, expenses and employment situation, fertilizer and seed input, technology, etc.
		管理单产 Management yield	投入政策、支持政策、保护政策、科技政策等 Input policy, support policy, protection policy, science and technology policy, etc.
	收获面积 Harvested area	价格竞争面积 Price competition area	上一期投入产出效益、其他竞争农产品上一期投入产出效益、上一期种植面积等 Input-output benefits of the previous period, input-output benefits of the previous period of other competitive agricultural products, planting area of the previous period, etc.
		调查面积 Survey area	调查问卷等 Questionnaire, etc.
		遥感面积 Remote sensing area	NVDI植被指数、物候期等 NVDI vegetation index, phenology, etc.
	畜禽产量 Livestock production		生育期因素、效益成本、管理因素、调查因素等 Fertility factors, benefit costs, management factors, survey factors, etc.
消费量 Consumption quantity (QC)	食用（口粮）消费 Food use consumption		人口数、人均收入、均衡价格等 Population, per capita income, equilibrium price, etc.
	工业消费 Industrial consumption		生产价格、人均国民生产总值和工业增长率等 Production prices, GDP per capita, industrial growth rate, etc.
	饲用消费 Feed consumption		畜产品产量、料肉比、饲料价格、投入品和产出品的价格 Production of livestock products, feed-to-meat ratio, feed prices, prices of inputs and outputs
	种用消费 Seed consumption		播种面积、每亩种子用量 Seeded area, seed per mu
	损耗 Wastage		产量、损耗系数 Production, wastage factor
贸易量 Trade (T)	进出口量 Import and export		国内外价差、关税、进出口配额、产需缺口和汇率等 Domestic and foreign price differences, tariffs, import and export quotas, production and demand gaps, exchange rates, etc.
价格 Price (P)	均衡价格指数 Equilibrium price index		生产因素、消费因素、政策因素、偶发因素等 Production factors, consumption factors, policy factors, incidental factors, etc.

表1

表2

模型集群生产、消费、价格和贸易通用模型形式及变量说明"

模型形式 Model form		主要变量 Main variable	主要参数 Main parameter
生产量 Production quantity (QP)	QP_crop= f (Y_m, Y_i, Y_ma, HA)	QP_crop：作物产量;Y_m：气象单产;Y_i：投入单产;Y_ma：管理单产;A_s：收获面积 QP_crop: Production quantity of crop; Y_m: Meteorological yield; Y_i: Input yield; Y_ma: Management yield; HA: Harvested area
	Y_m=δ(T, S, P)	T：温度;S：日照时间;P：降水量 T: Temperature; S: Sunshine duration; P: Precipitation.	δ：气象因子系数 δ: Meteorological factor coefficient
	Y_i=θ(CE, FE, FI)	CE：成本收益因子;FE：费用和用工因素;FI：肥料投入因素 CE: Cost-benefit factors; FE: Cost and employment factors; FI: Fertilizer input factors	θ：投入因子弹性系数 θ: Input factor elastic coefficient
	Y_ma =μ(Pol, Man)	Pol：政策指数;Man：政策因素 Pol: Policy indexs; Man: Policy factors	μ：政策系数 μ: Policy coefficient
	HA=ε(P, P_subs)	P：作物价格指数;P_subs：竞争作物价格指数 P: Crop price index; P_subs: Competitive crop price index	ε：竞品价格指数系数 ε: Competitive price index coefficient
	QP_animal= f(YLD × SL × CR)	YLD：单只动物出栏活重;SL：动物出栏数量;CR：动物出栏率 YLD: Single animal slaughter live weight; SL: Number of animals slaughtered; CR: Animal slaughter rate	畜禽产量系数 Livestock production coefficient
消费量Consumption quantity (QC)	QC = g(FC, IC, FEC, SEC, W)	FC：食用（口粮）消费;IC：工业消费;FEC：饲用消费;SEC:种用消费;W：损耗 FC: Food use consumption; IC: Industrial consumption; FEC: Feed use consumption; SEC: Seed use consumption; W: Wastage	各消费细项系数 Coefficients of various consumption items
贸易量 Trade (T)	IM = h(QP, QC) EX = f( QP, QC)	QP：产量;QC：消费量 QP: Production quantity; QC: Consumption quantity	产量、消费量、价格影响系数 Coefficient of influence of production, consumption, prices
价格 Price (P)	$\left\{ \overrightarrow{P}\left\| \forall {{S}_{i}}(\overrightarrow{P}) \right.-{{D}_{i}}(\overrightarrow{P})=0 \right\}$	$\overrightarrow{P}$：均衡价格向量;S_i：供给端价格向量;D_i：需求端价格向量 $\overrightarrow{P}$: Equilibrium price vector; S_i: Supply price vector; D_i: Demand price vector	多产品均衡价格指数 Multi-product equilibrium price Index

表2

表3

利用多因素分类解耦技术构建的小麦监测预警模型集群方程形式"

预测变量 Predicted variable	模型方程形式 Model equation form	变量说明 Variable description
供需平衡 Supply- demand balance	S_WT_,_t = D_WT_,_t	S_WT：小麦总供给量 The total supply of wheat in the current period D_WT：小麦总需求量 The total demand of wheat in the current period
总供给 Supply (S)	S_WT_,_t = QP_WT_,_t + IM_WT_,_t + OS_WT_,_t	QP_WT：小麦生产量 Current production of wheat IM_WT：小麦进口量 Current import of wheat OS_WT：小麦期初库存 Opening stock of wheat
生产量 Production quantity (QP)	QP_WT_,_t = YLD_WT_,_t× HA_WT_,_t	YLD_WT：小麦单产 Wheat yield HA_WT：小麦收获面积 Wheat harvested area
单产 Yield (YLD)	YLD_WT_,_t = w₁Y_m_,_WT_,_t+w₂Y_i_,_WT_,_t+ w₃Y_ma_,_WT_,_t	Y_m_,_WT、Y_i_,_WT、Y_ma_,_WT：小麦气象单产、投入单产和管理单产 Current wheat meteorological yield, input yield and management yield w₁、w₂、w₃：小麦Y_m、Y_i、Y_ma单产对应的赋值系数 Corresponding value coefficients of Y_m、Y_i、Y_ma of wheat
气象单产 Meteorological yield (Y_m)	Y_m_,_WT_,_t = w₁Y^T_m_,_WT_,_t + w₂Y^S_m_,_WT_,_t+ w₃Y^P_m_,_WT_,_t	Y^T_m_,_WT、w₂Y^S_m_,_WT、w₃Y^P_m_,_WT：小麦温度单因素气象单产、日照单因素气象单产以及降水量单因素气象单产 Current wheat temperature single factor meteorological yield, sunshine single factor meteorological yield and precipitation single factor meteorological yield w₁、w₂、w₃：小麦3个气象单因素模型对应的赋值系数 Corresponding value coefficients of each meteorological yield model
投入单产 Input yield (Yi)	${{Y}_{i,WT,t}}=\log ({{\alpha }^{Yi}}+\beta _{1}^{{{Y}_{i}}}\ln {{P}_{t-1}}+\beta _{2}^{{{Y}_{i}}}\times $ $\ln CE_{t}^{\varepsilon }+\beta _{3}^{{{Y}_{i}}}\ln FE_{t}^{\varepsilon }+\beta _{4}^{{{Y}_{i}}}\ln FI_{t}^{\varepsilon })$	P_t_-1：小麦上一期价格 Wheat price in the previous year CE^ε_t：成本收益情况因素向量 Cost-benefit factor vector FE^ε_t：费用和用工情况因素向量 Cost and employment factor vector FI^ε_t：肥料投入因素向量 Fertilizer input factor vector
管理单产 Management yield (Y_ma)	Y_ma_,_WT,t=(1+γ_t)×YLD^′_WT_,_t	YLD^′_WT：小麦基础单产 Wheat basic yield γ：管理因子赋值系数 Management factor assignment coefficient
面积 Harvested area (HA)	HA_WT_,_t=(w₁HA_competition_,_t+w₂HA_survey_,_t+ w₃HA_rs_,_t)-kAD_t	HA_competition：小麦价格竞争面积 Price competition area of wheat HA_survey：小麦调查面积 Survey area of wheat HA_rs：小麦遥感面积 Remote sensing area of wheat w₁、w₂、w₃：小麦三种预测面积对应的权重系数 Corresponding value coefficients of each harvested area model AD：小麦的成灾面积 Disaster area of current wheat k：灾情指数,在0—1之间的一个数值,越大表示灾情越严 Disaster index, a value between 0—1, the greater the severity of the disaster
价格竞争面积 Price competition area(HA_competition)	lnHA_competition_,_t= α+β₁lnHA_competition_,_t_-1+ β₂lnP_t_-1+β₃lnP_subs_,_t_-1	HA_{competition,t}_-1：小麦上一期播种面积 Wheat harvested area in the previous year P_t_-1：小麦上一期价格 Wheat price in the previous year P_subs,t_-1：竞争相关性作物的上一期价格 Price of competitively related crops in the previous year
调查面积 Survey area (HA_survey)	$H{{A}_{survey,t}}=(\sum\limits_{i=1}^{n}{{{w}_{i}}\times H{{A}_{i,survey,t}}})\times k\times \frac{1}{r}$	HA_survey：调查得出的某地区小麦面积 Surveyed area of wheat in a certain area HA_i,survey：某地区第i个村的小麦调查面积 Surveyed area of wheat in the i-th village in a certain area w_i：第i个村的权重 Weight of the i-th village k：某地区调查县所有村数量与抽样框包含的所有村数量的比值 The ratio of the number of all villages in a survey county to the number of all villages included in the sampling frame in a certain area r：国家调查县小麦面积占全省所有县小麦面积的比率 The ratio of the area of counties under national survey to the area of all counties in the province
预测变量 Predicted variable	模型方程形式 Model equation form	变量说明 Variable description
进口量 Import (IM)	lnIM_WT_,_t=α^WT^,^IM+β₁^WT^,^IMlnQP_WT_,_t+ β₂^WT^,^IMlnP_WT_,_IM_,_t+β₃^WT^,^IMlnXR_t	IM_WT：小麦进口量 Wheat import QP_WT：小麦生产总量 Total wheat production P_WT,IM ：以当地货币计价的小麦进口价格 Import prices of wheat in local currency XR：人民币对美元汇率 RMB against the U.S. dollar α_WT,IM：小麦进口量误差项 Errors of wheat import
期初库存 Opening stock (OS)	OS_WT,t= ES_WT,t-₁	ES_WT,t-₁：上一期小麦期末库存 The ending stock of wheat in the previous period
总需求 Demand (D)	D_WT_,_t = QC_WT_,_t + EX_WT_,_t + ES_WT_,_t	QC_WT：小麦消费量 Wheat consumption EX_WT：小麦出口量 Wheat export ES_WT：小麦期末库存 Ending stock of wheat
消费量Consumption quantity(QC)	QC_WT_,_t = FC_WT_,_t + IC_WT_,_t + FEC_WT_,_t + SEC_WT_,_t + W_WT_,_t	FC_WT：小麦口量消费量 Food use consumption of wheat IC_WT：小麦工业消费量 Industrial consumption of wheat FEC_WT：小麦饲用消费量 Feed use consumption of wheat SEC_WT：小麦种用消费量 Seed use consumption of wheat W_WT：小麦损耗 Wastage of wheat
口粮消费量 Food use consumption (FC)	FC_WT_,_t = PC_WT_,_rural_,_t× POP_WT_,_rural_,_t + PC_WT_, _{urban, t}× POP_WT, _urban_,_t	PC_WT_,_rural ：农村人均小麦口量消费量 Rural per capita FC of wheat POP_WT_,_rural：农村总人口数 Total rural population PC_WT_, _urban：城镇人均口量消费量 Urban per capita FC of wheat POP_WT_,_urban：城镇总人口数 Total urban population
农村人均口粮消费量 Rural per capita consumption (PC_rural)	PC_WT_,_rural,t=exp(α₁lnDPI_WT_,_rural_,_t+ α₂lnP_RT_,_t+α₃lnP_WT_,_t+α₄lnP_MA_,_t+ α₅lnP_SB_,_t+b)	DPI_WT_,_rural：农村人均可支配收入 Rural per capita disposable income P_RI：稻米均衡价格 Rice equilibrium price P_WT：小麦均衡价格 Wheat equilibrium price P_MA：玉米均衡价格 Corn equilibrium price P_SB：大豆均衡价格 Soybean equilibrium price
城市人均口粮消费量Urban per capita consumption (PC_urban)	PC_WT_,_urban,t=exp(α₁lnDPI_WT_,_urban_,_t+ α₂lnP_RT_,_t+α₃lnP_WT_,_t+α₄lnP_MA_,_t+ α₅lnP_SB_,_t+b)	DPI_WT_,_urban：城镇人均可支配收入 Urban disposable income per capita
饲用消费量 Feed use consumption (FEC)	FEC_WT_,_t = FER_WT_,₁×(QP_PK_,_t+ QP_BV_,_t + QP_MU_,_t) +FER_WT_,₂×QP_PT, _t + b	QP_PK：猪肉产量 Pork production QP_BV：牛肉产量 Beef production QP_MU：羊肉产量 Mutton production QP_PC：禽肉产量 Poultry production
工业消费量 Industrial consumption (IC)	IC_WT_,_t = exp (alnGDP + b)	GDP：国内生产总值 Gross Domestic Product
种用消费量 Seed use consumption (SEC)	SEC_WT_,_t = SPM_WT_,_t × HA_WT_,_t	SPM_WT：每亩小麦种子用量 Wheat seed dosage per acre
损耗 Wastage (W)	W_WT_,_t= KLW_WT_,_t×QP_WT_,_t	KLW_WT：小麦损耗率 Wheat loss rate
出口量 Export (EX)	lnEX_CHN,WT,t=α₁^WT,EX+β₁^WT,EX× lnEX_CHN,WT,t-1 +β₂^WT,EX× ln FOB_CHN,WT,t	EX_CHN,WT,t：全国小麦出口量 National wheat export EX_CHN,WT,t-1：上一期全国小麦出口量 National wheat exports in the previous period FOB_CHN,WT,t：以当地货币计价的小麦离岸价格 FOB price of wheat in local currency

表3

表4

模型集群的主要变量参数及求解"

模型主要变量 Model variable f(x)			模型方程变量系数的求解方法 Method for solving variable coefficients of model equation
产量 Production quantity (QP)	作物单产 Yield	气象单产 Meteorological yield	历史数据建立回归方程求解各系数 Use historical data to establish regression equations to solve coefficients
		投入单产 Input yield	历史数据建立回归方程求解各系数 Use historical data to establish regression equations to solve coefficients
		管理单产 Management yield	历史数据、专家经验确定各系数 Historical data and expert experience determine the coefficients
		综合单产 Comprehensive yield	专家人工设置和智能训练赋值各单产权重 Expert manual setting and intelligent training assign the weight of each yield
	收获面积 Harvested area	价格竞争面积 Price competition area	历史数据建立回归方程求解各系数 Use historical data to establish regression equations to solve coefficients
		调查面积 Survey area	调查数据 survey data
		遥感面积 Remote sensing area	监测数据 Monitoring data
		综合面积 Comprehensive area	专家人工设置和智能训练赋值各面积权重 Expert manual setting and intelligent training assign the weight of each area
	畜禽产量 Livestock production		历史数据建立回归方程求解各系数 Use historical data to establish regression equations to solve coefficients
消费量 Consumption quantity (QC)	食用（口粮）消费 Food use consumption		历史数据建立回归方程求解各系数 Use historical data to establish regression equations to solve coefficients
	工业消费 Industrial consumption		历史数据建立回归方程求解各系数 Use historical data to establish regression equations to solve coefficients
	饲用消费 Feed use consumption		历史数据建立回归方程求解各系数 Use historical data to establish regression equations to solve coefficients
	种用消费 Seed use consumption		历史数据建立回归方程求解各系数 Use historical data to establish regression equations to solve coefficients
	损耗 Loss		历史数据建立回归方程求解各系数 Use historical data to establish regression equations to solve coefficients
贸易量 Trade( T )	进出口量 Import and export		历史数据及专家预测 Historical data and expert forecasts
价格 Price( P )	均衡价格指数 Equilibrium price index		局部均衡模型求解 Local equilibrium model solution

表4

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