THE FUTURE SIZE OF FARMS: MODELLING THE EFFECT OF CHANGE IN LABOUR AND MACHINERY

Cow farming for milk production is one of the most important sectors in Greek animal production. Cows’ milk production combined with beef meat production is the second most important sector of animal production after sheep farming. Animal production in Greece has also suffered from the financial “crisis” (2008) with the number of milk producing cows declining by a significant amount. The most recent studies (Valergakis, 2000, Mitsopoulos, 2012) of the cows’ milk producing sector in Greece are centered in one geographical region of the country (Central Macedonia) and they are focused on identifying and describing the main characteristics of the structure and organisation of the farms. The basic criteria for this are mainly the size, the annual milk yield per cow, the animal housing system, the nutrition, reproduction, milking and waste management methods. These studies compare the farming methods used to the results achieved, both technical and economic.Moreover, Theodoridis (2008) using primary data from the same geographical region (i.e. Central Macedonia) evaluated the results of the policies concerning cows’ milk farming after 1992 using both parametric and non-parametric methods. Data Envelopment Analysis was used to examine the level of output oriented technical efficiency. Land (in ha), labour (in hours), variable and fixed capital (in EUR) were used as inputs and gross income (in EUR) as output. The results of this study showed that the technical efficiency of the farms increases as the size of the farms increases. These results indicate that the sector’s adjustment to the new policies favours the mid and large-sized farms while small farms are declining. Also, this study showed the inadequacies in the organisation of the sector and confirmed the positive correlation between size and technical efficiency.One of the sector’s main features that differentiate the farms is whether these produce their own feedstuffs (home grown feedstuffs) or not. The object of this Ph.D. thesis was to examine the cost of milk production by milk producing cows’ farms in relation to the production system used. “Pure” farms that are not producing feedstuffs versus mixed farms (home growing feedstuffs). The productivity and the efficiency of the sector were also evaluated. The main question was whether the multiple plant production activities of the cows’ milk producing farms lead to more efficient production systems even between farms with similar technical characteristics. This study was based on primary data that cover a wider area of the country than previous studies that focused on a single geographical region and a wide spectrum of farms that operate in different soil conditions.In evaluating the data, initially the structure and the economic situation of the average farm in the current state of the sector is described, in relation to the production or not of home grown feedstuffs by the farm. In this context “pure” and mixed farms are also compared. The next step of the analysis was to create a typology with the method of Hierarchical Cluster Analysis. Finally, the technical efficiency of the farms in the sample was evaluated by using the non-parametric Data Envelopment Analysis (DEA). This evaluation investigated weaknesses in organisation and management at the farm level. Questionnaires were used to collect the data of this study, covering 78 farms from the regions of Epirus, Thessaly, Western and Central Macedonia.The (average) year the farms were established was 1990 (±9.23) for the “pure” farms and 1989 (±9.37) for the mixed farms (p>0.05). The combined average age of the farmers was 44.7 (±9.18) years. The average age of the farmer in “pure” farms was 43.63 (±8.78) years while in mixed farms it was 45.56 (±9.49) years (p>0.05). This difference was not statistically significant (α=0.05).The educational background of the farmers in “pure” farms was 27% higher education, 33% secondary education, 40% primary education while in mixed farms it was 31% higher, 36% secondary, 33% primary. The average size of farm in relation to the educational background was 132.8 (±76.26) cows (higher education), 106.7 (±62.26) cows (secondary education) and 112.9 (±92.37) cows (primary education) (p>0.05). There was also no statistically significant difference between the average milk yield per cow in relation to education as this was 7,635.3 kg (±1,653.15), 6,933.25 kg (±1,387.45) and 6,644.7 kg (±1,646.68) for higher, secondary and primary education respectively. The knowledge of management is mostly acquired empirically by the farmers which is detrimental to the productivity of the farms.The size of the farms and plant production for feedstuffs by them were compared in relation to the geographical region. In respect to the size of the farms, the number of cows per farm in the four regions was as follows: Epirus 84.6 (±53.34), Western Macedonia 74.13 (±32.39), Central Macedonia 140.1 (±81.97), Thessaly 141.7 (±94.06). The average was 116.6 (±77.89) cows (p≤0.01).The average land area per farm was 41.354 ha (Epirus), 61.913 ha (Western Macedonia), 26.328 ha (Central Macedonia) and 12.900 ha (Thessaly) (p≤0.017). The average land area owned per farm was 19.904 ha (Epirus), 37.487 ha (Western Macedonia), 8.838 ha (Central Macedonia) and 7.000 ha (Thessaly) (p≤0.001). There is also a statistically significant difference in the irrigated land available to the farms in relation to the area they operate which is 28.454 ha (Epirus), 53.538 ha (Western Macedonia), 16.297 ha (Central Macedonia) and 10.606 ha (Thessaly) (p≤0.01).The farms according to their size (cows per farm) were classified in three categories: large, 172.4 (±93.30), medium, 125.3 (±80.15) and small 77.6 (±37.42) (p=0.000). The average farm size of the “pure” farms was 123.4 (± 85.33) cows while the average size of the mixed farms was 111.6 (±72.70) with an overall average of 116.6 (± 77.98) cows (p>0.05). The technically efficient farms were larger sized than the inefficient ones with an average of 220.6 (±131.06) cows per farm versus 130.1 (±57.00) for the inefficient (p≤0.001). Plant production activities were irrespective of the farm size but the increase in size results in less total land area per cow and this difference was statistically significant.The average annual milk yield per farm amounted to 981,818 kg (±765,119) in “pure” farms versus 810,111kg (±737,914) in mixed farms (p>0.05). In respect to size, average annual milk yield for large, medium and small sized farms was 1,596,875 (±946,230.9), 964,375 (±662,897.8) and 414,833 (±208,374.2) kg respectively (p≤0,001). Efficient farms had an average annual milk yield per farm of 1,880,000 kg (±1,332,891.59) while inefficient ones had an average of 752.681 kg (±527,513.3) (p≤0.001).The average annual milk yield per cow for the farms in the sample was 7,037 kg (±1,596). In “pure” farms this average was 7,662kg (±1,310) and in mixed ones it was 6,578kg (±1,644)(p=0.002). The same average in respect to farm size was 9,123.2kg (±666.91) (large), 7,561.6 kg (±596.97) (medium) and 5,363.9 kg (±744.84) (small) (p≤0.001). Efficient farms had an average annual milk yield per cow of 7,896.3 kg (±1,570.15) while inefficient ones had an average of 6,924.6 kg (±1,576.38) (p>0.05). The increase in size results in increased annual milk yield per cow, increasing the technical efficiency.Total annual work hours (farmer labour and paid labour) per farm were on average 11,096.3 (±111.58) and were increasing as home grown feedstuffs production increased. There was no statistically significant difference between “pure” farms (11,153.0 hours) and mixed farms (12,134.5 hours) (p>0.05). The additional work hours in mixed farms are spent in plant production by the farmer himself and are substituted with paid non-specialised labour, subtracting valuable time from the animals and resulting in superficial supervising. The available work hours of the farmers for the cows amounted to 4,973.9 in the case of “pure” farms versus 4,692.7 in the case of mixed ones (p>0.05). Increasing the farm size results in reducing the necessary work hours per cow per year and this difference was statistically very significant (p≤0.001).It was calculated by the analysis of production costs that the average cost capital (variable and fixed) necessary per cow amounted to 3,453.05€ which represents 90% of the annual total expenditure. The increase in farm size results in its statistically significant reduction (p≤0.001). There is also a statistically significant difference in respect to home growing feedstuffs or not. “Pure” farms need more capital per cow. In mixed farms the fixed capital necessary is more than in “pure” farms (p≤0.001). Less variable capital is necessary in “pure” farms than in mixed ones (p≤0.01). Moreover, the increase in farm size results in the increase of variable capital necessary per cow (p≤0.001) while there is no statistically significant difference in fixed capital per cow.Time between births for the average farm in the sample was calculated at 14.2 months (426 days). The best observed was 12.8 months (385 days) and the number of necessary inseminations per gestation was 2.89. Milking period was found to have an average duration of 366.27 days. The average age of heifers at the first insemination was 16.27 months. There was no statistically significant difference between “pure” and mixed farms with regard to the above indices. There was also no statistically significant difference in relation to farm size and milk yield per cow. The farmers often do not keep records and this results in reproductive problems going undetected preventing them from being addressed timely and effectively.The percentage of cows kept up to the 4th milking period (incl.) was 82.93% for the average farm. There was a statistically significant difference in relation to home growing feedstuffs. “Pure” farms achieved a percentage of 86.66

Israeli agriculture has experienced rapid structural changes in recent decades, including the massive exit of farmers, a resulting increase in average farm size, a higher farm specialization and a higher reliance on non-farm income sources. The higher farm heterogeneity makes it necessary to examine changes in the entire farm size distribution rather than the common practice of analyzing changes in the average farm size alone. This article proposes a nonparametric analysis in which the change in the distribution of farm sizes between two periods is decomposed into several components, and the contributions of subgroups of farms to this change are analyzed. Using data on Israeli family farms, we analyze the changes in the farm size distribution in two separate time periods that are characterized by very different economic environments, focusing on the different contributions of full-time farms and part-time farms to the overall distributional changes. We found that between 1971 and 1981, a period characterized by stability and prosperity, the farm size distribution has shifted to the right with relatively minor changes in higher moments of the distribution. On the other hand, between 1981 and 1995, a largely unfavorable period to Israeli farmers, the change in the distribution was much more complex. While the overall change in the size distribution of farms was smaller in magnitude than in the earlier period, higher moments of the distribution were not less important than the increase in the mean and led to higher dispersion of farm sizes. Between 1971 and 1981, the contributions of full- and part-time farms to the change in the size distribution were quite similar. Between 1981 and 1995, however, full-time farms contributed mostly to the growth in the average farm size, while the average farm size among part-time farms actually decreased, and their contribution to the higher dispersion of farm sizes was quantitatively larger. This highlights the need to analyze the changes in the entire farm size distribution rather than focusing on the mean alone, and to allow for differences between types of farms.

Visegrad Cooperation is the regional organization of four states known as the Visegrad Group or the Visegrad Four (the Czech Republic, Poland, Hungary, and Slovakia). The purpose of cooperation is joint representation of economic, diplomatic and political interests of these Central and Eastern European countries and coordination of their possible measures. In the framework of this study, the major trends regarding the average area and farm number of agricultural holdings in the Visegrad Group are examined and evaluated. We attempted to elucidate structural differences between the farm structures of these countries, their different and unusual development and its reasons. Despite these differences, we have uncovered a number of identical trends and joint points as well. The average farm size in Slovakia and the Czech Republic was 28 or 135 hectares in 2007, while in 2010, this figure changed to 75 or 152 hectares in the circle of the observed agricultural holdings. For both countries, therefore, a significant increase in farm size was observed in the period under survey. While in Hungary the average size of 6 hectares in 2007 increased to 8 hectares in three years, then in Poland, the average farm was 12 or 10 hectares in size in the years under survey. In the circle of these countries, therefore, only a slight shift, mainly stagnation was observed, albeit on different bases: while the number of holdings under survey increased in Poland, it decreased in Hungary (similarly to Slovakia and the Czech Republic). Investigating the details regarding farm structure in these countries, it can be stated that most holdings carried out their activities on an area of under 20 hectares, in respect of distribution of agricultural land in the countries of the Visegrad Four. However, the survey results also revealed that in the vast majority of cases, the number of farms decreased, but there was an increase in their average size, that is, the process of concentration that has been experienced in the Western part of the European Union for the last two decades can be observed in these four countries as well.

The ‘pure’ and structural contributions to the average farm size growth in the EU: The index decomposition approach

Resource degradation, technical change, and the productivity of energy use in U.S. agriculture

Farm size affects nitrogen fertilizer input and agricultural practices, which are key determinants of ammonia (NH3) emissions from croplands. However, the degree to which NH3 emissions are associated with changes in farm size is not well understood yet despite its crucial role in achieving agricultural sustainability in China, where agricultural production is still dominated by smallholder farms. Here we provide a first analysis of the relationship between farm size and NH3 emissions based on 863 000 surveys conducted in 2017 across China. Results show that NH3 emissions (kg ha-1) on average decrease by 0.07% for each 1% increase in average farm size. This change occurs mainly due to a reduction in nitrogen fertilizer use and the introduction of more efficient fertilization practices. The largest reduction in NH3 emissions is found in maize, with less pronounced changes in rice cultivation, and none for wheat production. Overall lower NH3 emissions factors can be observed in the north of China with increasing farm size, especially in the northeast, the opposite pattern was found in the south. National total NH3 emissions could be approximately halved (1.5 Tg) in a scenario favoring a conversion to large-scale farming systems. This substantial reduction potential highlights the potential of such a transition to reduce NH3 emissions, including benefits from a socioeconomic point of view as well as for improving air quality.

A “first-pass” test on a set of monthly prices index series from 2000 to 2015 was applied to detect market power exertion in the dairy value chain of 25 EU countries. Due to econometric and theoretical restrictions, the test yielded conclusive findings only in 11 over 25 EU Countries. Such results show that in Austria, Portugal, Slovakia, Hungary and Croatia, the downstream sector exerts market power. Other EU countries (Spain, UK, Denmark, Czech Republic, Bulgaria and Sweden) are characterised by perfectly competitive dairy chains. These results were consistent with the findings of previous studies based on structural and mark-up models. Results of the market power test in the subsample of 11 countries have been related to various structural characteristics of the dairy chains. Market power exertion is negatively related to the average farm size. Such variable may be seen as a proxy of the degree of supply concentration provided by Producers Organizations (POs) to increase the bargaining power of the farm sector along the food chain. To test such a hypothesis, comparable data on supply concentration by POs across EU Countries are necessary. On the other hand, the structural change, represented by the increase of average farm size over time and the concentration rate in higher classes (above 250,000 € of Standard Output) is almost unrelated to the perfectly competitive conduct along EU dairy chains.

The economic development of rural economies across the global south is often related to access to water and the development of water infrastructure. It has been argued that the construction of new dams would unleash the agricultural potential of African nations that are exposed to seasonal water scarcity, strong interannual rainfall variability, and associated uncertainties in water availability. While water security is often presented as the pathway to poverty alleviation and invoked to justify large dam projects for irrigation, it is still unclear to what extent small holders will benefit from them. Are large dams built to the benefit of subsistence farmers or of large-scale commercial agriculture? Here we use remote sensing imagery in conjunction with advanced machine learning algorithms to map the irrigated areas (or ‘command areas’) that have appeared in the surroundings of 18 major dams built across the African continent between 2000 and 2015. We quantify the expansion of irrigation afforded by those dams, the associated changes in population density, forest cover, and farm size. We find that, while in the case of nine dams in the year 2000 there were no detectable farming patterns, in 2015 a substantial fraction of the command area (ranging between 8.5% and 96.7%) was taken by large-scale farms (i.e., parcels >200 ha). Seven of the remaining 9 dams showed a significant increase in average farm size and number of farms between 2000 and 2015, with large-scale farming accounting for anywhere between 5.2% and 76.7% of the command area. Collectively, these results indicate that many recent dam projects in Africa are associated either with the establishment of large-scale farming or a transition from small-scale to mid-to-large scale agriculture.

Market proximity and irrigation infrastructure determine farmland rentals in Sichuan Province, China

Commercial farming of Atlantic salmon in Scotland started in 1969 and has since expanded to produce >179,000tyear−1. A government department has published annual statistics and information on the seawater and freshwater sub-sectors of the Scottish salmon farming industry since 1979, and this review collates and discusses metrics covering aspects of production, farm sites and systems, fish performance, socio-economics and environmental pressures. Trends illustrated in this case study of aquaculture development include: initial increases in numbers of farms and companies, followed by decreases due to industry consolidation; increases in average farm size, and productivity of systems and employees; increases in survival, size at age and productivity of fish (yield per smolt, ova per broodstock); reduced dependence on wild stocks for ova. This case study also illustrates the importance of disease management, control of biological processes to overcome natural seasonality (i.e. production of out-of-season smolt), and the international nature of aquaculture. Improvements in fish survival, growth and productivity are attributed to progress in vaccination and health management (including fallowing), husbandry, system design, feed formulation and provision, and introduction of technology and mechanisation. Salmon farming is discussed in relation to the challenging strategy of “sustainable intensification”. Improved growth and survival over a period of increasing rearing unit size, farm size and output and decreasing relative staff input counters the common assumption that intensification compromises animal welfare. The value of capturing time series data on industry wide metrics is illustrated as it enables identification of trends, underperformance and bench-marking, as well as assessment of resource use efficiency, environmental pressures, and ultimately sustainability. Statement of relevanceThis review is an original collation of a comprehensive set of time series of official statistics on an entire, discrete and regionally important sector of commercial aquaculture.

Japan's agricultural border protection is high for food, but low for feed, thus effectively protecting both rice and meat products. This border protection is complemented by other governmental support in the form of direct subsidies, capital subsidies, price support, together amounting to 37% of agricultural GDP in 1985 and not offset by the taxes paid by farmers or on farm products. Changing this protective structure requires resource adjustment, which can be brought about by changing agricultural terms of trade, concomitant with an increase in average farm size and a decline in part-time farming, through a more liberal land-use policy.

In the last few decades, dairy farms in Galicia (NW Spain) have gone through a restructuring process, characterized by a dramatic reduction in the number of farms and an increase in average farm size and production. Therefore, the farm area devoted to dairy housing must be enlarged, and the current layout parameters must be reconsidered. With a view to opening new possibilities, this study defines a multi-criteria function that considers functional elements of the production process and aspects related to investment costs. The multi-criteria function defined can be used to evaluate the most representative layouts currently in use in Galicia and possible alternatives that may respond more efficiently to the evolution of the farming sector.

This paper studies the validity of Gibrat’s law for the growth of Slovenian farms between 2007 and 2015 using Farm Accountancy Data Network datasets. Cross-sectional dependence test and four different groups of panel unit root tests are applied to study the relationship between farm size and the farm size growth. It revealed evidence of cross-sectional dependence in farm sizes. Both input (land and labour) and output (economic) sizes of variables as proxy for the measures of farm size are applied. The results suggest that Gibrat’s law is valid for Slovenian farms independently from the measures of farm size and types of panel unit root tests. Slovenian smaller farms are not growing faster than larger ones and thus all farm sizes tend to contribute to an increase in average farm size in generally relatively small- to medium-size farm structures.

Using Lenin's 50-year-old study of United States agriculture as a starting point, the author analyzes structural changes from the Marxist point of view, dealing with such elements as increases in average farm size, mechanization, hired labor, decreases of cultivated land, acreage limitations, and changes in crop specialization.

The ongoing economic pressure on farmers has resulted in lower gross margins, lower income, and a continuous decrease in the number of farmers in large parts of the world. Most remaining farmers upscale their activities by taking over the land of their former competitors, resulting in a decrease in agricultural employment and an increase in average farm size, accompanied by specialisation and new management techniques. Understanding these significant trends and their impact on the land use and environment requires a deeper knowledge of the mechanisms involved and the impacts of different policy measures. These processes are ideally represented through agent-based modelling. Currently, agent-based models are rarely for larger regions. This paper presents ADAM (Agricultural Dynamics through Agent-based Modelling), using it for the case study of Belgium. ADAM was created to obtain insights in past and current agricultural trends and to explore possible effects of policy measures. ADAM simulates the evolution of a farmer population and their farms at a fine scale on the country level. It produces yearly outputs on the number of farms, their size, and the type of farming activity on every parcel. Results show that ADAM is capable of adequately modelling a farmer population according to past trends and that it can be used to explore the results of a business-as-usual scenario, therefore showing the possibility of creating agent-based models for larger scale real-world applications.