Impact of Operating Scale on Factor Inputs in Grassland Animal Husbandry—Intermediary Effects Based on Market Risk
Abstract
:1. Introduction
2. Theoretical Analysis
2.1. Operating Scale and Bias in the Input of Production Factors
2.2. The Mediating Role of Market Risk
3. Methodology and Data
3.1. Study Area
3.2. Methods
3.2.1. Cost Contribution Model
3.2.2. Cobb–Douglas Production Function Model
3.2.3. Multiple Linear Regression Model
3.2.4. Mediation Effect Model
4. Empirical Study
4.1. Analysis of the Cost Contribution
4.2. Analysis of the Bias of Production Factors Input from the Economic Optimal Result
4.3. Analysis of the Impact of Operating Scale on the Bias of Production Factors
4.3.1. Regression Results Analysis
4.3.2. Mediation Effect Analysis
5. Conclusions and Prospects
5.1. Conclusions
- (1)
- The study reveals that forage, labor, and machinery costs are the primary factors influencing the total production cost of herdsmen, contributing 41.27%, 6.41%, and 0.22%, respectively. Of these, the cost contribution of forage is much higher than that of the other factors of production, accounting for nearly half. Therefore, one of the keys to realizing sustainable development of grassland animal husbandry lies in controlling the cost of forage.
- (2)
- Various types of production factor inputs have a certain promoting effect on the output value of grassland animal husbandry, and the output elasticity is demonstrated by (in descending order) machinery (0.30), labor (0.21), corn (0.12), and hay (0.09). Machinery has the highest output elasticity but makes a small cost contribution, and the focus should be on improving mechanization in grassland animal husbandry. Labor is also an important contributor to output. However, in the face of the impact of rising labor prices and an aging workforce in today’s society, raising labor inputs is clearly not a sustainable approach. The contribution of forage was lower. However, forage cannot be replaced, as it is an indispensable part of grassland animal husbandry production; therefore, we should start by reducing the price of the forage supply.
- (3)
- In grassland livestock production, there is a deviation from the economic optimum in the inputs of factors of production, namely, labor, machinery, hay, and corn (in descending order), and the mean values of deviations were −109.65 days, CNY 486.70, −3.11 tonnes, and 5.67 tonnes, respectively. As the livestock size or pasture size increases, factor deviations show a decreasing trend. This decreasing trend makes large-scale herders more competitive than small-scale herders.
- (4)
- As ranchers’ scales of operation increase, the degrees of market risk they face for hay, maize, labor, and machinery inputs gradually increase. Market risk plays a mediating effect in the influence pathway of operational scale on variable factor input bias, but does not play a mediating role in the effect of operation size on invariant factor input bias.
5.2. Policy Implications
- (1)
- Forage occupies an extremely important position in the production costs of grassland animal husbandry and has a direct impact on the sustainable development of grassland animal husbandry. Therefore, a series of measures should be taken to cope with the pressure of rising forage costs. Firstly, in areas where grass resources are scarce, it is possible to promote the implementation of industrial policies such as the “grain-to-fodder” policy, which replaces part of the demand for forage by planting food crops, thus reducing the pressure on the supply and cost of forage. Secondly, strengthening subsidies for forage planting is another important initiative, whereby the government can promote the planting of forage crops through direct subsidies, tax incentives, or policy support to reduce their planting costs and increase their planting income, thus encouraging farmers to increase the supply of forage, stabilize feed market prices, and reduce the economic pressure on herdsmen. In addition, scientific research institutions and enterprises can be encouraged to increase the level of research and development, and promote forage planting technology; in addition, the introduction of good varieties, improved planting technology, and efficient management modes and other measures can lead to improving the yield and quality of forage crops, reducing the cost of feeding, and increasing the profitability of herdsmen, thus promoting a more stable and sustainable development of grassland animal husbandry.
- (2)
- Machinery and labor play a crucial role in the development of grassland livestock farming. Labor has a high output elasticity and is critical to livestock production, but faces challenges such as rising labor prices and an aging workforce. These factors have given rise to the demand for mechanized equipment, and mechanization can replace labor to a certain extent, helping to reduce production costs and improve efficiency. Therefore, the government can promote the promotion and utilization of mechanical technology in grassland livestock husbandry by formulating relevant policies. One effective policy is to provide subsidies for the purchase of agricultural machinery and equipment, which can help herders obtain advanced agricultural machinery and equipment at a lower cost and improve production efficiency. In addition, the government can subsidize the construction of pens to improve the conditions of barns and enhance the environment for livestock production, thereby improving the productivity and health of livestock. In short, the government should increase the promotion of grassland livestock production machinery technology, effectively alleviate the impact of labor shortage and aging on livestock production, and lay a more solid foundation for the sustainable development of grassland animal husbandry.
- (3)
- Although there is a certain degree of deviation in the input of factors of production by large-scale herding households, the degree of deviation is significantly lower relative to that of small-scale herding households. This shows that scale operation has important practical significance in the development of grassland animal husbandry. Grassland transfer is an important way to realize moderate-scale operation. First, government departments should further improve relevant laws and regulations, provide tax incentives and financial support, set up special funds for pasture integration and transfer, and provide transfer subsidies and incentives to motivate herdsmen to actively participate in pasture transfer and to promote the large-scale development of animal husbandry. Second, government departments should establish a pasture resource trading market to provide a platform with transparent information and convenient transactions to promote the orderly flow of pasture resources. Third, government departments should strengthen the training and technical support for herdsmen to enhance their scale operation and resource management ability, so as to make them more aware of and capable of scaling their operation. It is also possible to encourage herders to transform into family farms or to form new management bodies such as professional cooperatives, so as to realize the integration of production factors and complementary advantages. In conclusion, through a moderate scale of operation, herdsmen can refine the management of forage, labor, and machinery inputs, realize the optimal allocation of production factors, and implement more efficient production methods, which will help the sustainable development of grassland animal husbandry.
- (4)
- The results of the study show that an increase in the degree of market risk reduces variable factor input bias to a large extent, while the effect on constant factor input bias is not significant. This finding poses the following question: How can we reduce the factor input bias while coping with market risk? Enhancing information symmetry between herders and factor markets is the key. First, a production factor information-sharing platform can be created so that herders can keep abreast of market prices, supply and demand trends regarding forage and labor, and so on; these could be in the form of online platforms, regular information meetings, or local cooperatives. Second, herders should be encouraged to conduct regular market research, which will help them better grasp changes in factors of production and market conditions, avoid blindly expanding or reducing means of production, and be able to better adjust their production plans and input levels. Finally, herding households can be provided with relevant training on market information and production factor allocation, including how to obtain, interpret, and utilize market information and how to optimize production factor allocation. Such training can help them better understand market dynamics and allocate production factors in a scientific and reasonable manner. In summary, it is crucial to enhance the awareness of market information and improve the production decision-making ability of herdsmen, which can effectively reduce the uncertainty in the production process and promote the rational allocation of production factors, thus promoting the sustainable development of grassland animal husbandry.
5.3. Prospects
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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County | Sample Size |
---|---|
Aluqorqin Banner | 84 |
Bahrain Left Banner | 39 |
Right Banner of Bahrain | 79 |
Wengniute Banner | 47 |
Keshketeng Banner | 33 |
Variables | Definitions | Mean | Std. Dev. | Min | Max |
---|---|---|---|---|---|
Annual hay input costs (CNY) | 56,344.52 | 89,828.63 | 130.00 | 725,000.00 | |
Annual corn input costs (CNY) | 139,728.30 | 203,319.70 | 1040.00 | 1,562,400.00 | |
Annual other feed inputs (CNY) | 11,723.69 | 23,325.81 | 0.00 | 256,747.40 | |
The direct labor and hired workers of the family (CNY) | 59,987.65 | 33,266.03 | 15,500.00 | 326,000.00 | |
Depreciation of fixed assets (CNY) | 12,742.22 | 13,280.53 | 800.00 | 89,381.00 | |
Actual costs incurred for breeding through natural mating, artificial insemination, and embryo transfer, etc. (CNY) | 3827.81 | 6518.11 | 0.00 | 43,000.00 | |
Medical and epidemic prevention fees (CNY) | 6154.73 | 8237.24 | 0.00 | 70,000.00 | |
Expenditure on coal, fuel, electricity, lubricants, and other power consumed (CNY) | 13,458.98 | 38,137.40 | 300.00 | 353,000.00 | |
Insurance expenses (CNY) | 1575.08 | 3161.46 | 0.00 | 30,000.00 | |
Total costs of grassland animal husbandry (CNY) | 338,422.20 | 321,320.00 | 57,697.25 | 2,391,281.00 | |
Output value of all livestock products sold and retained (CNY) | 758,086.60 | 637,816.90 | 62,700.00 | 4,042,500.00 |
Variables | Definitions | Mean | Std. Dev. | Min | Max |
---|---|---|---|---|---|
Hay input bias | Calculated from Equations (4)–(6), and taken to absolute values (tonne) | 12.58 | 14.60 | 0.02 | 175.84 |
Corn input bias | Calculated from Equations (4)–(6), and taken to absolute values (tonne) | 10.21 | 14.48 | 0.01 | 116.74 |
Labor input bias | Calculated from Equations (4)–(6), and taken to absolute values (days) | 141.30 | 100.70 | 0.31 | 526.85 |
Mechanical inputs bias | Calculated from Equations (4)–(6), and taken to absolute values (CNY) | 2164.77 | 2591.18 | 0.15 | 23,656.65 |
Livestock size | Number of stocked livestock at the end of the year before survey (sheep units) 1 | 337.54 | 232.48 | 50.00 | 1550.00 |
Pasture size | Pasture size = Own pasture size + Transferred − in pasture size − Transferred − out pasture size (mu) 2 | 1042.71 | 1161.82 | 0.00 | 8157.00 |
Gender | Male = 1; female = 2 | 1.03 | 0.17 | 1.00 | 2.00 |
Age | Age of household head (years) | 48.43 | 9.04 | 22.00 | 70.00 |
Educational level | Schooling years of household head (years) | 9.26 | 3.00 | 0.00 | 16.00 |
Family size | Number of family members | 3.77 | 1.33 | 1.00 | 9.00 |
Political status | Whether the head of the household is a village cadre or not (Yes = 1; Otherwise = 0) | 0.23 | 0.42 | 0.00 | 1.00 |
Transportation conditions | Distance from county government (kilometer) | 59.66 | 25.75 | 1.10 | 126.00 |
Animal husbandry income | Income from sale of live animals and livestock products (CNY 10,000) 3 | 1.99 | 2.99 | 200.00 | 31.05 |
Proportion of hay | The proportion of hay cost to total costs (%) | 10.08 | 8.63 | 0.01 | 40.53 |
Proportion of corn | The proportion of corn cost to total costs (%) | 22.04 | 10.85 | 0.69 | 46.36 |
Proportion of other feeds | The proportion of other feeds costs to total costs (%) | 9.81 | 11.26 | 0.00 | 77.49 |
Participation in cooperatives | Whether they intend to join the cooperative or not (Yes = 1; Otherwise = 0) | 0.13 | 0.33 | 0.00 | 1.00 |
Technological training | Whether they participated in technical training (never participated = 0; occasional participation = 1; Regular participation = 2) | 0.93 | 0.77 | 0.00 | 2.00 |
Pasture quality | Quality deterioration = 0; quality unchanged = 1; better quality = 2 | 0.41 | 0.70 | 0.00 | 2.00 |
Variables | Coefficients | Std. Dev. |
---|---|---|
0.6640 *** | 0.0118 | |
0.0028 | 0.0022 | |
0.0062 * | 0.0032 | |
0.0118 *** | 0.0026 | |
0.0517 *** | 0.0112 | |
0.0017 | 0.0022 | |
0.2580 *** | 0.0190 | |
Constant | 1.0930 *** | 0.2010 |
R-squared | 0.963 | |
F-test | 1041.174 |
Factors | Contribution |
---|---|
41.2645 | |
0.0034 | |
0.0129 | |
0.0478 | |
0.2161 | |
0.0008 | |
6.4107 |
Variables | Coefficients | Std. Dev. |
---|---|---|
0.0905 *** | 0.0254 | |
0.1150 *** | 0.0326 | |
0.0160 * | 0.0084 | |
0.2110 *** | 0.0793 | |
0.2970 *** | 0.0458 | |
0.0802 ** | 0.0357 | |
−0.0011 | 0.0039 | |
0.0522 *** | 0.0121 | |
0.0001 *** | 3.15 × 10−5 | |
Constant | 4.6020 *** | 0.8750 |
R-squared | 0.530 | |
F-test | 34.430 |
Types of Production Factor | Factor Input Bias | Degree of Factor Input Bias 1 | Percentage of Bias in Factor Inputs for Herders (%) | ||||
---|---|---|---|---|---|---|---|
Small (<0.25) | Smaller (0.25–0.50) | Comparatively Large (0.50–0.75) | Large (0.75–1.00) | Big (>1.00) | |||
Hay input | −3.11 (tonne) | 0.82 | 14.89 | 19.15 | 29.43 | 24.47 | 12.06 |
Corn input | 5.67 (tonne) | 1.39 | 16.31 | 18.79 | 19.86 | 14.18 | 30.85 |
Labor input | −109.65 (days) | 0.59 | 15.25 | 19.15 | 38.65 | 23.05 | 3.90 |
Mechanical input | 486.70 (CNY) | 0.69 | 31.56 | 24.11 | 18.44 | 9.93 | 15.96 |
Variables | Hay Input Bias | Corn Input Bias | Labor Input Bias | Mechanical Input Bias |
---|---|---|---|---|
−1.5428 (3.2996) | −5.6045 * (3.3501) | −104.4684 *** (22.7794) | −1103.1360 * (612.0238) | |
−4.0643 (3.5977) | −10.1026 *** (3.6675) | −88.9249 *** (25.1258) | −998.2799 (672.1068) | |
−7.6267 * (4.5214) | −14.3610 *** (4.1781) | −86.4160 *** (28.7748) | −914.1912 (765.7775) | |
1.3869 (5.0497) | 4.3712 (4.5678) | −13.0690 (31.5602) | 1076.4790 (840.4974) | |
0.1216 (0.1000) | 0.0920 (0.0912) | 0.5259 (0.6265) | 3.5045 (16.7866) | |
0.6642 ** (0.3050) | −0.4106 (0.2770) | 1.7117 (1.9008) | −11.7560 (50.9572) | |
−0.7406 (0.6493) | 0.4252 (0.5910) | 0.7667 (4.0589) | 45.0315 (108.7228) | |
−1.8084 (2.0498) | −2.5675 (1.8747) | 0.7375 (12.8439) | −582.9056 * (346.8293) | |
−0.0158 (0.0340) | 0.0110 (0.0311) | −0.0600 (0.2070) | 9.1519* (5.5170) | |
2.0876 ** (0.8875) | 2.2932 *** (0.8203) | 25.8903 *** (5.7060) | 97.7681 (150.8521) | |
58.6947 *** (10.0320) | 65.2936 *** (7.2922) | −132.8810 *** (30.0200) | 15709.9430 *** (1831.3950) | |
5.0618 ** (2.5306) | 0.8848 (2.2990) | 43.1544 *** (15.7260) | 718.2626 * (423.0583) | |
1.3065 (1.1420) | 0.2224 (1.0431) | 12.8988 * (7.1267) | 223.3172 (190.8137) | |
−1.4965 (1.2228) | 0.1802 (1.1179) | 6.7683 (7.6502) | 15.1886 (205.6527) | |
Constant | −24.9933 * (12.7060) | −29.8188 ** (11.7147) | −63.4700 (84.6908) | −1561.8390 (2143.2530) |
N | 282 | 282 | 282 | 282 |
R-squared | 0.163 | 0.293 | 0.309 | 0.253 |
F-test | 3.700 | 7.892 | 8.532 | 6.451 |
Variables | Hay Input Bias | Corn Input Bias | Labor Input Bias | Mechanical Input Bias |
---|---|---|---|---|
−9.1427 *** (2.3035) | 1.6472 (2.1799) | −20.4669 (14.7470) | −439.1651 (394.8024) | |
−9.8401 *** (2.7829) | −5.3867 ** (2.6348) | −34.0066 * (18.5248) | −1246.5790 *** (476.3681) | |
−9.4308 *** (2.7032) | −1.1282 (2.5531) | −17.2847 (18.0267) | −376.0524 (458.0566) | |
0.7085 (4.9126) | 4.1197 (4.6444) | −13.7316 (32.7394) | 935.1094 (839.9993) | |
0.1721 * (0.0970) | 0.1120 (0.0919) | 0.5495 (0.6461) | 3.9856 (16.6153) | |
0.7033 ** (0.2971) | −0.4184 (0.2806) | 1.3419 (1.9708) | −11.3133 (50.7079) | |
−0.7799 (0.6293) | 0.4538 (0.5959) | −0.4878 (4.1874) | 40.8911 (107.7220) | |
−2.0145 (1.9928) | −4.0023 ** (1.8893) | −2.7105 (13.2443) | −661.6979 * (343.4713) | |
0.0004 (0.0340) | 0.0307 (0.0322) | −0.1221 (0.2223) | 8.7723 (5.7213) | |
1.4582 ** (0.7115) | 0.4712 (0.6756) | 23.6150 *** (4.9816) | 85.3129 (121.9534) | |
57.3402 *** (9.9025) | 63.6836 *** (7.4087) | −134.3908 *** (31.6791) | 15393.4900 *** (1814.4190) | |
3.7729 (2.4438) | −0.1618 (2.3168) | 42.0696 ** (16.2472) | 678.9699 * (419.0419) | |
1.3134 (1.0982) | −0.1470 (1.0419) | 16.8823 ** (7.3035) | 267.5041 (187.5166) | |
−1.4478 (1.2068) | 0.9371 (1.1453) | 8.5891 (8.0226) | 91.76384 (206.9793) | |
Constant | −15.6702 (11.7371) | −17.5086 (11.1971) | −101.4175 (84.5396) | −1760.8070 (2015.7770) |
N | 282 | 282 | 282 | 282 |
R-squared | 0.206 | 0.278 | 0.261 | 0.263 |
F-test | 4.962 | 7.348 | 6.752 | 6.822 |
Variables | Model (7) | Model (8) | Model (9) | Model (7) | Model (8) | Model (9) |
---|---|---|---|---|---|---|
Hay Input Bias | Market Risk | Hay Input Bias | Corn Input Bias | Market Risk | Corn Input Bias | |
−0.0009 * (0.0005) | −0.0005 *** (0.0002) | |||||
−1.5428 (3.2996) | −0.4353 (3.3544) | −5.6045 * (3.3501) | −3.9520 (3.4468) | |||
−4.0643 (3.5977) | −2.6355 (3.6858) | −10.1026 *** (3.6675) | −8.1325 ** (3.7960) | |||
−7.6267 * (4.5214) | −6.0582 * (4.6027) | −14.3610 *** (4.1781) | −11.7417 *** (4.3830) | |||
0.9766 * (0.5835) | 2.7105 * (1.4344) | |||||
Whether to control variables | Yes | Yes | Yes | Yes | Yes | Yes |
Constant | −24.9933 * (12.7060) | −5.0939 *** (1.3321) | −20.2112 (12.9817) | −29.8188 ** (11.7147) | −2.2030 *** (0.5052) | −23.6797 * (12.1029) |
N | 282 | 282 | 282 | 282 | 282 | 282 |
Variables | Model (7) | Model (8) | Model (9) | Model (7) | Model (8) | Model (9) |
Labor Input Bias | Market Risk | Labor Input Bias | Mechanical Input Bias | Market Risk | Mechanical Input Bias | |
−0.0081 *** (0.0028) | −0.0894 *** (0.0299) | |||||
−104.4684 *** (22.7794) | −80.3726 *** (22.8083) | −1103.1360 * (612.0238) | −1266.7620 ** (628.1) | |||
−88.9249 *** (25.1258) | −59.9043 ** (25.3074) | −998.2799 (672.1068) | −1194.7623 * (693.2303) | |||
−86.4160 *** (28.7748) | −49.3434 * (29.2407) | −914.1912 (765.7775) | −1175.5871 (798.5683) | |||
2.6752 *** (0.6311) | −1.8445 (1.6089) | |||||
Whether to control variables | Yes | Yes | Yes | Yes | Yes | Yes |
Constant | −63.4700 (84.6908) | −29.2697 *** (8.0633) | 13.9678 (84.1296) | −1561.8390 (2143.2530) | −313.1594 *** (81.7493) | −2137.0913 (2199.9780) |
N | 282 | 282 | 282 | 282 | 282 | 282 |
Variables | Model (7) | Model (8) | Model (9) | Model (7) | Model (8) | Model (9) |
---|---|---|---|---|---|---|
Hay Input Bias | Market Risk | Hay Input Bias | Corn Input Bias | Market Risk | Corn input Bias | |
−0.0002 ** (0.0001) | −0.0001 * (0.00003) | |||||
−9.1427 *** (2.3035) | −9.1316 *** (2.2945) | 1.6472 (2.1799) | 1.2907 (2.162) | |||
−9.8401 *** (2.7829) | −9.4877 *** (2.7792) | −5.3867 ** (2.6348) | −5.1452 ** (2.6094) | |||
−9.4308 *** (2.7032) | −8.9715 *** (2.7052) | −1.1282 (2.5531) | −0.7730 (2.5306) | |||
0.9813 * (0.5566) | 3.5425 ** (1.3816) | |||||
Whether to control variables | Yes | Yes | Yes | Yes | Yes | Yes |
Constant | −15.6702 (11.7371) | −4.6307 *** (1.2797) | −11.1871 (11.9643) | −17.5086 (11.1971) | −1.8612 *** (0.4912) | −10.9072 (11.3772) |
N | 282 | 282 | 282 | 282 | 282 | 282 |
Variables | Model (7) | Model (8) | Model (9) | Model (7) | Model (8) | Model (9) |
Labor Input Bias | Market Risk | Labor Input Bias | Mechanical Input Bias | Market Risk | Mechanical Input Bias | |
−0.0008 * (0.0005) | −0.0088 * (0.0050) | |||||
−20.4669 (14.7470) | −17.1540 (15.3425) | −439.1651 (394.8024) | −427.1717 (395.1142) | |||
−34.0066 * (18.5248) | −29.2058 * (17.8141) | −1246.5790 *** (476.3681) | −1273.9150 *** (477.3973) | |||
−17.2847 (18.0267) | −11.4536 (17.3500) | −376.0524 (458.0566) | −409.6087 (459.5948) | |||
3.0269 *** (0.6228) | −1.4385 (1.5481) | |||||
Whether to control variables | Yes | Yes | Yes | Yes | Yes | Yes |
Constant | −101.4175 (84.5396) | −25.0709 *** (7.9864) | −26.4338 (82.6246) | −1760.8070 (2015.7770) | −257.9842 *** (79.6469) | −2134.0330 (2055.9110) |
N | 282 | 282 | 282 | 282 | 282 | 282 |
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Xue, C.; Du, F.; Yong, M. Impact of Operating Scale on Factor Inputs in Grassland Animal Husbandry—Intermediary Effects Based on Market Risk. Sustainability 2024, 16, 7540. https://doi.org/10.3390/su16177540
Xue C, Du F, Yong M. Impact of Operating Scale on Factor Inputs in Grassland Animal Husbandry—Intermediary Effects Based on Market Risk. Sustainability. 2024; 16(17):7540. https://doi.org/10.3390/su16177540
Chicago/Turabian StyleXue, Chen, Fulin Du, and Mei Yong. 2024. "Impact of Operating Scale on Factor Inputs in Grassland Animal Husbandry—Intermediary Effects Based on Market Risk" Sustainability 16, no. 17: 7540. https://doi.org/10.3390/su16177540