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«——【Preamble】】 ——»
Animal husbandry is not only a basic industry to cope with the upgrading of residents' consumption structure and ensure food security in the mainland, but also the key to prospering the rural economy, increasing the income of farmers and herdsmen, and realizing rural revitalization.
In order to help the development of agriculture and rural economy and meet the needs of people's livelihood, the state has issued a series of policies to promote the development of animal husbandry, especially grass-fed animal husbandry.
Ningxia Hui Autonomous Region is an important base for the breeding of high-quality dairy cows, the production and processing of high-quality beef and mutton and dairy products in the mainland.
In 2015, the output value of animal husbandry in Ningxia accounted for 27.9% of the total output value of agriculture, forestry, animal husbandry and fishery, which has maintained growth for five consecutive years, and has reached 35.1% by 2020, higher than the national average of 29.2%.
Among them, beef and sheep industry and dairy industry are Ningxia's agricultural characteristic and advantageous industries, in order to further promote the development of animal husbandry, especially the development of the three advantageous industries.
Ningxia needs to rely on the resource endowment and industrial base, optimize the layout of livestock breeding, integrate advantageous elements, and improve the production capacity of animal husbandry.
The socio-economic system and ecological environment system on which animal husbandry production depends have two dimensions of time and space, and only by analyzing the spatio-temporal characteristics of animal husbandry production can we guide animal husbandry production according to different environmental and economic conditions and reduce the negative impact of animal husbandry on the environment.
In the study of animal husbandry in mainland China from the perspective of time and space, scholars mainly focus on the geographical agglomeration of animal husbandry, the industrial layout of some livestock breeds, and the impact of animal husbandry on the environment.
It is found that since the 21st century, the geographical agglomeration of animal husbandry in mainland China has been continuously strengthened, and the center of gravity of agglomeration has gradually shifted to the north. In addition, affected by factors such as natural endowment, social and economic development level and technological level, the pig industry, beef cattle industry and meat sheep industry also show obvious spatial agglomeration characteristics.
With the rapid development of large-scale and intensive animal husbandry industry, the environmental problems are becoming more and more serious, and the academic community has conducted in-depth research on the spatiotemporal characteristics of livestock and poultry pollutant emissions, soil carrying capacity, animal husbandry carbon emissions and grassland livestock carrying pressure.
So in the process of animal husbandry development, whether different livestock products develop synchronously or show different laws of change, what are the reasons behind it?
In order to provide a reference for optimizing the spatial distribution of animal husbandry, rationally adjusting the structure of livestock breeds and increasing the supply of livestock products, the livestock breeding structure of Ningxia and its counties was calculated by using the animal husbandry data of Ningxia from 1978 to 2020.
The visual analysis method is used to reveal the temporal variation characteristics of livestock feeding structure in Ningxia, and the reasons for the change of livestock feeding structure are explained by analyzing the animal husbandry policy in Ningxia.
The coefficient of variation method and standard deviation ellipse method were used to analyze the spatial distribution characteristics of livestock feeding structure, and the driving factors of spatial distribution characteristics of feeding structure were analyzed by using the geographic detector model.
«——【Materials and Methods·】——»
Ningxia is located in the upper reaches of the Yellow River in the western part of the mainland, which is a typical continental climate, a temperate semi-arid region and a semi-humid region, with an average annual precipitation of 166.9~647.3mm, annual sunshine hours of 2247.20~3083.50h, and a sunshine percentage of 51%~70%.
The average altitude of the whole area is 1100m, the terrain is high in the south and low in the north, and the descending step shape. The whole area is divided into three major plates, the northern Yellow River irrigation area has flat terrain and fertile soil, the central part is arid and rainless, the soil is poor, the southern mountainous area is full of hills and ravines, and some areas are humid and cold. Ningxia has a total area of 66,400 km2 and 22 county-level administrative divisions (Figure 1).
In recent years, Ningxia's animal husbandry has achieved rapid development, and its importance in agricultural production has become increasingly prominent. The supply capacity of important livestock products has been steadily improved, and the total output of meat, eggs and milk in the region reached 34 percent in 2020. 10,000, 138,000 and 2,153,400 tons.
Industries with special advantages have achieved high-quality development, with the number of beef cattle and Tan sheep raised reaching 1.926 million and 12.21 million respectively. Taking into account the different breeding cycles of cattle, sheep and pigs and the availability of data.
In this study, the effective feeding of cattle, sheep and pigs in Ningxia and counties in Ningxia were calculated by using the annual slaughter and year-end inventory data of livestock.
The geographic detector model can detect spatial heterogeneity and its driving factors, and its core idea is to divide the study area into several sub-regions, and if the sub-region variance and less than the total variance of the region, there is spatial heterogeneity.
If there is significant similarity between the explanatory variable and the explanatory variable in spatial distribution, there is a statistical correlation between the two, and the explanatory variable has an important impact on the spatial heterogeneity of the explanatory variable.
Compared with traditional regression methods, the geographic detector model has the following advantages, and does not need to consider the multicollinearity of explanatory variables.
The geographic detector has four detection modules, namely: factor detection, interaction detection, risk area detection and ecological detection.
«——【Results and Analysis·】——»
From 1978 to 2020, the effective feeding of cattle and sheep and the total effective feeding of livestock showed an overall upward trend, while the effective feeding of pigs first increased and then decreased, and then gradually stabilized (Figure 2).
Around 1994, the growth rate of effective feeding of three types of livestock accelerated. Before 2002, the effective feeding of cattle, sheep and pigs was very close, and the gap increased after 2002. In addition, the effective rearing of sheep and pigs, as well as the total effective rearing of livestock, fluctuated in 2007.
From 1978 to 2020, the proportion of cattle and sheep raised generally increased, but fluctuated greatly (Figure 3). The proportion of pigs raised fluctuates relatively little, and has decreased significantly in the past 20 years.
In the later period, policies were introduced to promote the large-scale and standardized development of animal husbandry production, optimize the layout of animal husbandry, and improve the production efficiency and quality of animal husbandry.
The mainland's animal husbandry policy plays an important role in the development of the animal husbandry industry and affects the development direction of the animal husbandry industry, and Ningxia is no exception.
After the reform and opening up, Ningxia's animal husbandry industry began to gradually change from natural stocking under the conditions of collective economy to decentralized operation of one household under the household contract responsibility system, and achieved a certain degree of development.
In 2000, the implementation of the strategy of developing the western region brought new opportunities for the restructuring of Ningxia's animal husbandry industry.
Ningxia began to establish leading industrial clusters and characteristic industrial belts with regional characteristics. In 2002, Ningxia made it clear that it would make dairy and halal beef and mutton industries bigger and stronger.
It can be seen that during this period, cattle and sheep breeding became the focus of the development of animal husbandry in Ningxia, and the livestock breeding structure in Ningxia began to undergo significant changes. According to the analysis results of the coefficient of variation of the selected time nodes, the spatial differentiation of the feeding ratios of cattle, sheep and pigs varied (Table 2).
The highest coefficient of variation of cattle feeding ratio occurred in 1990 and has been declining ever since. The highest value of the coefficient of variation of sheep feeding ratio occurred in 1978, and then the coefficient of variation of pig feeding ratio decreased first and then increased, reaching the highest value (0.89) in 2020.
From 1978 to 2020, the proportion of cattle raised in most areas of Ningxia increased, and the proportion of cattle kept at a high level in the southern part of Ningxia, with the highest proportion in Jingyuan County, the southernmost part of Ningxia (Figure 4).
During this period, the proportion of cattle in northern Ningxia also gradually increased, and reached a high level after 2011, and the proportion of cattle in the central and western regions also increased, but it was still at a relatively low level.
Similarly, from 1978 to 2020, the proportion of sheep breeding in most areas of Ningxia also increased, and the areas with a consistently high proportion of sheep breeding were concentrated in the central and eastern parts of Ningxia, among which Yanchi County reached more than 60% after 2011.
In the western region, the proportion of rearing increased between 1978 and 2011, but decreased significantly from 2011 to 2020.
In 2000, the proportion of sheep raised in northern Ningxia counties generally reached the highest level since 1978, and has since declined. From 1978 to 2020, the proportion of pigs raised in Ningxia counties showed a downward trend as a whole.
Before 2011, the counties with a high proportion of pig breeding were concentrated in the northwest of Ningxia, among which the proportion of pig breeding in Shapotou District, Zhongning County and Qingtongxia City has always remained above 40%.
After 2011, the proportion of pigs raised in the northwest region decreased significantly, and by 2020, the proportion of pigs in most counties and districts in Ningxia was less than 20%.
From 1978 to 1990, the distribution center of cattle feeding ratio shifted from north to south, then from south to north, and gradually shifted from Tongxin County to Hongsibao District.
This results show that after 1990, the proportion of cattle in northern Ningxia has gradually increased, and the azimuth angle has increased slightly since 1990, which indicates that the pattern of south-north distribution of cattle proportion is relatively stable, and only shows a slight trend of northeast-southwest direction.
The difference between the major axis and the minor axis of the standard deviation ellipse of cattle feeding ratio was the largest among cattle, sheep and pigs, indicating that the spatial distribution of cattle feeding ratio was the most obvious (Table 3).
The spatial distribution of sheep breeding ratio also showed a south-north distribution pattern (Fig. 5), but compared with the cattle breeding ratio, its azimuth angle was larger, that is, the spatial distribution direction was more northeast-southwest.
The azimuth change trend of sheep feeding ratio was consistent with that of cattle, showing a slow northeast-southwest trend. The center of the elliptical circle changed greatly in 2000, and the rest of the years were located at the junction of Litong District and Hongsibao District, which was more northerly than Niu.
The long and minor axes of the ellipse fluctuated from 1978 to 2020, with the long axis increasing by 14.82 km and the minor axis increasing by 4.3 km over the 43 years, indicating that the proportion of sheep was expanding to the four sides as a whole (Table 3).
In addition, the difference between the long and short axes of the standard deviation ellipse of sheep feeding ratio was common for calves and pigs in each year, indicating that the spatial distribution of sheep feeding ratio showed a relatively small directionality.
The spatial distribution pattern of pig feeding ratio is close to that of sheep, and it is generally distributed in the south-north direction, but its distribution center is more southwestern than that of sheep, and its spatial distribution direction is bounded by 2000, showing different trends.
From 1978 to 2000, the azimuth changed from south to north, and from 2000 to 2020, the azimuth changed to northeast to southwest. The long axis of the pig feeding ratio is larger than that of sheep, smaller than that of cattle, and the short axis is the largest, indicating that the dispersion of pig feeding ratio is the strongest in the east-west direction (Table 3).
In the process of animal husbandry development, natural resource conditions determine the adequacy of livestock breeding space and material conditions; Socio-economic conditions affect livestock.
The area of arable land and grassland is related to the adequacy of fodder, while climatic conditions affect the growth of grain and forage, as well as the survival rate of young animals and the fattening of livestock.
Therefore, in this study, five indicators were selected to characterize the natural resource conditions affecting the livestock feeding structure, including cultivated land area, grassland area, precipitation, average temperature and sunshine hours.
In terms of socio-economic conditions, this study selects three indicators: per capita GDP, rural population, and living consumption expenditure, which affect the production capacity of animal husbandry and consumption of livestock products, and the proportion of Hui population that affects the consumption habits of livestock products.
Therefore, according to the development layout of dairy cow, beef cattle and beef sheep industry in the plan, 0~1 virtual policy variables were set up, and the value of the policy variable in the counties and districts in the advantageous industrial layout area was 1, and the value of the policy variables in the other counties and districts was 0.
The factor detector was used to analyze the influence of each driving factor on the feeding ratio of cattle, sheep and pigs, and the q value reflecting the degree of influence is shown in Table 4. The core driver of the spatial heterogeneity of cattle feeding ratio is living expenditure.
The core driving factors of the spatial heterogeneity of sheep feeding proportion were living consumption expenditure (0.69), Hui population proportion (0.59) and regional distribution of meat sheep (0.56).
The core driving forces of the proportion of pig feeding are GDP per capita (0.69) and the proportion of Hui population (0.63), in addition to the two natural factors of average temperature and sunshine hours.
The driving factors of the spatial heterogeneity of cattle and sheep feeding ratios are the same and the driving force ranking is the same. The strongest driving factor for the spatial heterogeneity of pig feeding ratio is GDP per capita.
In order to further explore the interaction of different driving factors on the spatial heterogeneity of cattle, sheep and pig feeding ratios, the core driving factors screened out in the factor detection were selected for interaction detection (as shown in Table 5).
Among the core driving factors of the spatial heterogeneity of cattle feeding ratio, except for the interaction type between average temperature and sunshine hours, average temperature and regional layout of beef cattle, the interaction type is a single factor nonlinear weakening, and the rest are two-factor enhancement.
These results indicate that the superposition effect between sunshine hours, living consumption expenditure, Hui population proportion, and regional distribution of beef cattle strengthens the spatial heterogeneity of cattle feeding ratio.
«——【Discuss this study】——»
The influencing factors of the current livestock structure in Ningxia include average temperature and sunshine duration, such natural resource factors, socio-economic factors such as per capita GDP and the proportion of Hui population, and policy factors.
Among them, socio-economic factors have an impact on the proportion of cattle, sheep and pigs, but natural resource factors only affect the proportion of cattle. From the analysis results of this study, it can be seen that the factor that has the greatest impact on the livestock feeding structure in Ningxia is the economy.
The economy is an important reason for the structural transformation of the agricultural system, and the increase of income and consumption capacity will bring about the upgrading of the dietary structure, and the breeding structure will be adjusted according to local conditions.
At the same time, we should pay attention to the inter-regional linkage role, use the core area to drive the development of the surrounding areas, improve the overall development quality of animal husbandry in Ningxia, and develop efficient and sustainable grass and animal husbandry.
According to Ningxia's current animal husbandry development policy, the proportion of grass-fed livestock will be further increased in the future, and most counties and districts will maintain a grass-fed livestock breeding structure.
In this case, how to ensure the quantity and quality of forage supply is very important, and it is recommended to develop the forage industry in an appropriate way according to the resource and environmental conditions of different regions while developing the animal husbandry industry.
Encourage all localities to create distinctive regional characteristics, strong market competitiveness, and well-known characteristic animal husbandry brands, relying on brand power to lead the rapid development of characteristic industries.