Agricultural Policy, Climate Factors and Grain Output: Evidence From Household Survey Data in Rural China

This paper estimates a stochastic frontier function using a panel data set that includes 4 961 farmer households for the period of 2005-2009 to decompose the growth of grain production and the total factor productivity (TFP) growth at the farmer level. The empirical results show that the major contributor to the grain output growth for farmers is input growth and that its average contribution accounts for 60.92% of farmer's grain production growth in the period of 2006-2009, whereas the average contributions sourced from TFP growth and residuals are only 17.30 and 21.78%, respectively. The growth of intermediate inputs is a top contributor with an average contribution of 44.46%, followed by the planted area (18.16%), investment in fixed assets (1.05%), and labor input (-2.75%), indicating that the contribution from the farmer's input growth is mainly due to the growth of intermediate inputs and that the decline in labor inputs has become an obstacle for farmers in seeking grain output growth. Among the elements consisting of TFP growth, the contribution of technical progress is the largest (32.04%), followed by grain subsidies (8.55%), the average monthly temperature (4.26%), the average monthly precipitation (-0.88%), the adjusted scale effect (-5.66%), and growth in technical efficiency (-21.01%). In general, the contribution of climate factors and agricultural policy factor are positive and significant.

Weight Determining of Factors Influencing Grain Output Based on Entropy Weight Method

This article selects 8 main factors(the number of rural employees,total power of agricultural machinery,effective irrigation area of crops,growing area of grain crops,fertilizer consumption,electricity consumption in rural areas,area affected and area covered) as the factors influencing grain output,and offers the method of determining weight of factors influencing grain output using entropy weight method.According to the relevant data in the period 1985-2005,we analyze the weight of factors influencing grain output in China by example.The results show that the electricity consumption in rural areas has the greatest impact on grain output,followed by total power of agricultural machinery,fertilizer consumption and area covered.To increase grain output,we must enhance the degree of mechanization,free people from the former process of direct cultivation,strengthen water conservancy construction,and do a good job in disaster prevention and mitigation.

Input-Occupancy-Output Technique and Its Applications in China' s Grain Output Prediction and Township and Village Enterprises Study

Under the support of National Natural Science Foundation of China including international cooperative research project, key project and other project, professor Chen Xikang from Academy of Mathematics and Systems Science under the Chinese Academy of Sciences, together with his colleagues, put forward in-put-occupancy-output technique and then used it in national grain output prediction approach. The main achievements are as follows:

Policy Suggestions for the Slowdown in Per Capital Output of Grain Growth in China

China is a large agricultural and the most populous country,so it is a crucial importance for the food security. The basic situation of food in our country is self-sufficiency,a certain amount of imports and reserves. The output of grain in our country has increased annually,however,as a result of policy adjustments and other factors like environmental pollution,together with a large population base and the trend of excessively populous growth,the growth of grain output is relatively slow,which will be harmful for our grain security as well as the healthy development of the national economy. In this case,several solutions could be tried,such as improving the level of science and technology,adjusting industrial policies and controlling environmental pollution. Besides,we ought to strongly advocate thrifty behavior to increase income and reduce expenditure.

省际粮食贸易隐含污染与环境比较优势研究

为协调粮食生产、贸易与面源污染的矛盾,实现绿色农业发展、区域间利益共赢,在测算省际粮食贸易量的基础上,运用灰水足迹方法计算各省隐含氮污染和污染强度,构建了粮食贸易隐含氮污染网络,解析了省际粮食生产隐含氮污染的环境比较优势。结果表明:中国省际粮食贸易网络格局由“条块式”转向“团块式”,贸易集中化趋势明显;省际粮食贸易隐含污染和污染强度呈下降趋势,但地区差异明显加剧了环境不公平;环境比较优势网络表现出“两头小,中间大”的橄榄形分布,环境比较优势最小的地区集中在华北地区,最大为黑龙江。

湖北省县域粮劳变化耦合模式与乡村发展类型

利用湖北省2003—2017年81个县域的粮食产量和农业劳动力数据,运用粮劳弹性系数模型方法,分析了湖北省粮劳时空变化特征及对应耦合模式。结果表明,2003—2017年湖北省粮食生产整体呈现稳中有增态势,呈“中高周低”“中增周减”空间特征,高产区集中在鄂西北中部与江汉平原南部;农业劳动力总量持续减少,呈“东多西少”“东增西减”空间特征,密集区集中在江汉平原东部和鄂东北丘陵西部。2003—2017年湖北省最典型的粮劳耦合变化模式为粮增劳减型(60.49%),其次为粮劳同减型(23.46%)和粮劳同增型(11.11%),粮减劳增型占比最低(4.94%)。受地理条件和农业政策的影响,湖北省乡村发展类型呈现多样化并存的格局:中部多分布外延扩张型和农业现代化型县域,东部和西部多分布产业转移型和种养一体化型县域;2010年前后两时段县域乡村发展类型转换较为复杂,维持不变相对较少。

钱越多越好吗?农村信贷与农村产业升级的时空关系——基于SAR模型与SEM模型的实证分析

农村信贷为农村经济发展提供资金支撑,是农村产业升级的重要保障。本文利用1978—2019年30省份面板数据,在构建农村信贷对农村产业升级影响的理论框架基础上,建立空间自回归模型(SAR)和空间误差模型(SEM),实证分析农村信贷与农村产业升级的时空关系。研究结果表明:农村产业升级在地理位置上呈现空间集聚性;农村信贷规模扩大到一定程度会对农村产业升级产生抑制作用,呈“倒U型”非线性关系;其中,受农业资源差异影响,整体上东部区和中部区存在“倒U型”关系,西部区并不存在“倒U型”关系;进一步分析发现,粮食主产区“倒U型”关系最显著,三大粮食产区中黄淮海地区和长江流域地区呈明显“倒U型”关系,东北地区则不存在“倒U型”关系。最后,本文提出发挥农村产业升级空间集聚作用、农村信贷坚持因地制宜原则等政策建议。

基于超效率Output-DEA模型的主产区粮食生产能力评价

应用超效率Output-DEA模型,对我国2008年13个粮食主产省区的粮食生产能力进行了研究。结果表明,主产区处于技术有效的省份仅有4个,其超效率值存在较大差距。多数省区技术无效率是规模效率低造成的。如果能够提高无效率省区的技术效率,发挥有效率省份的生产潜力,对提升全国的粮食生产能力将产生十分明显的效果。各无效率省区还存在投入的冗余,减少这些投入冗余对于降低粮食生产成本具有重要作用。我国政府应该把提高粮食生产效率作为粮食政策的首要目标,促进粮食生产区域的合理布局,优化粮食生产支持政策。

The changing regional distribution of grain production in China in the 21st century

This paper examines the changing regional distribution of grain production in China. Based on the analysis of data from county statistics for the period 2000–2003, major differences in the main grain-output regions in China can be observed. The main grain-producing areas have shifted from the south to the north of China. New grain production regions have been also added to western China since the late 1990s. The per capita grain consumption in one third of China’s main grain-producing counties has fallen below 400 kg; most of these areas are located in southern China. In the new millennium, Northeast China, the central-south North China, and the arid and semi-arid regions of Northwest China produced three quarters of the surplus grains. Most of these areas are located in regions susceptible to environmental change. The amount of grain production in these regions shows high fluctuations. It is argued here that further studies of recent environmental changes as well as a risk assessment of China’s food security in main grain-output regions are needed.

FORECAST OF CHINA'S GRAINPRODUCTION DEVELOPMENT AND ITSSELF-SUPPORT RATE ANALYSIS

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China’s grain production: status and prospects

In this paper, we first look back at China’s grain production since reform and opening up and review the main factors of grain production at different stages. Secondly we find that the improvement of the per unit yield has become a major factor in the increase of grain production, and that corn has become a major grain production support variety; compared with the other varieties, paddy rice is most vulnerable in production; grain production has increasingly become dependant on scientific and technological progress and the role of infrastructure. Thirdly, the transformation of low-yielding fields in the future, the development of reserve land resources and acceleration of scientific and technological progress – these factors together promote China's great potential for grain production; but through adjusting variety structure to promote grain production is nearly impossible. Finally, the main constraints of China’s future grain production are as follows: reduction of arable land and water shortages is becoming increasingly constrained; agricultural comparative advantage is low; opportunity cost is high and accelerating at the same time; and the uncertainties of (1) whether the agricultural inputs by government at all levels of can significantly increase the intensity; (2) whether support of the reform of grain production and systems can achieve breakthrough; and (3) whether the risk of the development of grain production can be effectively prevented. On this basis, we draw the relevant conclusions and policy recommendations.

劳动力价格上涨、粮食生产机械化及其产出效应

农业机械化是实现现代农业和保障粮食安全的重要基础。基于2001-2020年中国省级面板数据,实证分析劳动力价格对粮食生产机械化的影响以及粮食生产机械化的产出效应,并考察该影响及效应在耕种收各环节上的差异性。研究结果表明:劳动力价格上涨显著正向影响粮食生产综合机械化水平、机耕水平及机收水平;农机总动力对粮食产出具有显著的空间溢出效应;大中型农机动力及大中型农机机耕、机播及机收动力均对粮食产量具有显著的直接影响和空间溢出效应,且其空间溢出效应均大于直接效应。研究建议加快突破粮食生产的薄弱环节,促进农业社会化服务发展,推动农机资源在省际间合理流动与科学调配,加强小农生产与现代农业的衔接,加速实现粮食生产全程机械化与农业农村现代化。

中国粮食生产环境效率的时空演化特征及其影响因素分析

粮食生产对环境的影响日益受到关注,将粮食生产中产生的非期望产出纳入粮食生产效率测算中,指导实现粮食生产可持续性,对保障国家粮食安全具有重要意义。依据改革开放以来1980~2018年粮食生产省级层面数据,分别用SBM模型及面板回归模型等分析内地31个省市区粮食生产环境效率及其时空演化特征和影响因素,结果表明,中国的粮食生产环境效率整体呈现V型演化形态;中国中部粮食生产环境效率高于东部,均远高于西部,东中西部粮食生产环境效率均呈现U型演化形态;中国31个省市区时间演化形态多样:以低位水平波动的居多,其后是L型和整体下降的省市区,粮食生产环境效率高的省市区分布从改革开放初期的南方迁移到“十三五”期间的北方,较低等级粮食生产环境效率的省市区空间分布与低等级的省市区具有一定聚集特征,低等级粮食生产环境效率水平的省市区空间分布相对稳定;农村人均收入、财政支持农业、城镇化率、农村劳动力转移、农村机械化水平、化肥使用量、成灾率对粮食生产环境效率有显著负向作用,灌溉面积比例不显著,其余具有显著正向影响。因此,从科学规划粮食产业区域布局、转变农业增长方式、改善农业投入、调整经济结构、建立科学分配机制和提高农业生产资源的流动等方面提出粮食生产可持续发展、提高粮食生产环境效率建议。

基于Output-DEA模型的粮食生产效率分析——以安徽省三大粮食生产功能区为例

文章是基于粮食主产省安徽省2017年的16个地级市面板数据,采用Output-DEA模型对安徽省的水稻、小麦、玉米三大粮食生产功能区的综合效率、纯技术效率、规模效率与规模报酬以及冗余状况进行归类分析。研究发现,横向比较综合效率小麦生产区>水稻生产区>玉米生产区,纵向比较纯技术效率更接近DEA有效,而劳动力冗余问题突出,化肥施用存在过量。据此,提出加速城镇产业化进程、提升农业从业人员科技素养、科学布局粮食生产功能区等对策。

非农就业、农机服务外包对粮食产量影响

基于全国31个省(市)省际面板数据,探究非农就业、农机服务外包以及两者的交互项与粮食产量的关系。研究结果表明:非农就业对粮食产量具有显著的负面影响;农机服务外包对粮食产量的增产效应显著;非农就业和农机服务外包的交互项对粮食产量有正向效应,且农机服务外包对粮食产量的正向效应大于非农就业对粮食产量的负面效应。据此提出合理引导农户非农就业,确保农业生产力;完善农机服务外包体系,推动农业现代化;合理配置粮食生产要素投入,充分挖掘粮食产能等建议,以期促进粮食安全。

沿海地区粮食产量变化及其驱动因素分析

水土资源是粮食生产的基础资源,而农业产值是促进粮食生产的驱动力,在此基础上探究沿海地区粮食产量的主要影响因素,明确粮食产量与各影响因素的协调发展程度对评价区域粮食产量发展现状,保障粮食产量持续稳定增长具有重要意义。通过粮食产量、农业水土资源及农业产值等数据,构建了粮食产量恒等式模型,利用LMDI指数分解法探究了江苏沿海地区粮食产量的主要影响因素,利用耦合协调度模型探究粮食产量与各影响因素的均衡性。结果表明:①2009-2015年江苏沿海地区粮食产量呈稳定增长趋势,2016年和2017年有所跌落,2018年恢复增长状态,2009-2020年期间共增产119.31万t。②2009-2020年间,影响沿海地区粮食生产的主要因素依次是农业产值、农业产值耗水量、粮食复种指数、农业水土资源和粮食单产水平。各因素年际变化表现为正负波动变化,且区域间各时间段粮食产量影响因素差异显著。③自2009年到2020年,粮食产量与各影响因素的耦合协调度由严重不协调状态(D∈(0.0,0.3])逐渐稳定在基本协调状态(D∈(0.5,0.8])和高级协调状态(D∈(0.8,1.0])。因此政府在粮食增产计划中,要因地制宜,突出农业产值和农业用水效率的重要性,以农业产值的正向效应促进粮食种植面积的稳定增长,以节水技术实施和中低产田改造项目促进作物种植结构的良性调整,实现粮食产量、水土资源和农业产值的协调、可持续增长。

品种管理制度改革的粮食增产效应研究

粮食安全是“国之大者”,但学界关于粮食增产问题的研究缺乏来自种业领域的证据。基于2010—2019年全国31个省(区、市)的平衡面板数据,采用连续型双重差分模型,以2015年为政策冲击时间,实证检验品种管理制度改革对粮食生产的影响。研究表明:1.品种管理制度改革显著促进了粮食增产。2.粮食单产水平的提升是品种管理制度改革促进粮食增产的主要路径。品种管理制度不仅简化了审定程序,提高了良种的更新换代速度;而且拓宽了实验渠道,加速了品种的推广应用。3.品种管理制度改革的粮食增产效应具有异质性。从气候条件来看,湿润区明显更有助于品种管理制度改革效果的发挥;从地貌特征来看,相较于山地、高原地区,品种管理制度改革对平原、丘陵地带存在更为显著的增产效果;从土壤条件来看,品种管理制度改革的粮食增产效应随着农田生产潜力的增加而增强;从区域结构来看,政策的粮食增产效应在主产区更为突出。

粮食安全背景下贵州省粮食生产影响因素分析

粮食是人民群众最基本的生活资料,保障粮食安全是满足民众获得感、幸福感、安全感的前提。贵州作为典型的喀斯特山区省份,生态环境脆弱,粮食生产问题不可忽视。文章结合2010—2020年贵州省粮食总产量进行统计分析,归纳出影响粮食生产的因素,进一步运用多元线性回归模型对贵州省粮食生产影响因素进行全面分析,分析结果表明,粮食作物播种面积、有效灌溉面积和农业生产资料价格指数对贵州粮食总产量存在显著影响,并基于此提出了推动贵州省粮食生产进一步发展的对策和建议。

全方位夯实粮食安全根基的意义、现状与推进路径

党的二十大报告中提出,要全方位夯实粮食安全根基,这既是对保障国家粮食安全的更高要求,也是对牢牢把握粮食安全主动权的战略部署。从全球范围来看,粮食安全是人类共同面临的重大课题,从我国发展实际状况来看,粮食安全依然处于紧平衡态势。新时代以来,虽然我国粮食安全保障在制度建设、稳产增量、储备能力和流通体系等方面取得出色成绩,但耕地质量下降、自然灾害严重、育种技术落后和国际粮食市场复杂等要素仍然冲击着粮食安全。在科学分析我国粮食安全重大意义的基础上,客观剖析我国粮食安全面临的严峻挑战并探求相应的突破方法是全方位夯实粮食安全重要根基的必要举措。

不同氮素籽粒生产效率类型籼稻品种氮素分配与运转的差异

在群体水培条件下,以国内外不同年代育成的常规籼稻代表品种(2001年为88个、2002年为122个)为材料,测定干物重、氮素含量、产量及其构成因素等,采用组内最小平方和的动态聚类方法将供试品种的氮素籽粒生产效率(NUEg)从低到高依次分为A、B、C、D、E和F 6种类型,研究不同氮素籽粒生产效率类型品种氮素分配与运转的基本特点。结果表明,供试品种间NUEg的差异很大(244%、325%),A、B、C、D、E和F类品种的平均NUEg,2001年分别为20.51、31.04、35.64、39.46、43.55、50.92 g g-1,2002年分别为24.33、31.61、35.83、39.06、43.51、50.00g g-1;NUEg高的品种抽穗期、成熟期茎鞘叶中氮素比例小、穗中氮素比例大,成熟期更明显,结实期茎鞘叶氮素运转量大、运转率高;提高结实期茎鞘叶氮素运转量和运转率有利于提高结实期茎鞘叶干物重运转量和经济系数。