consuming. Statistical modelling of population density and distribution has only begun to be applied to farm animal populations, although it is commonly used in wildlife ecology. We developed zero-inflated Poisson regression models in a Bayesian framework using environmental and socioeconomic variables to predict the counts of livestock units (LSUs) and of cattle on spatially referenced farm polygons in a commercially available New Zealand farm database, Agribase. Farm-level counts of cattle and of LSUs varied considerably by region, because of the heterogeneous farming landscape in New Zealand. The amount of high quality pasture per farm was significantly associated with the presence of both cattle and LSUs. Internal model validation (predictive performance) showed that the models were able to predict the count of the animal population on groups of farms that were located in randomly selected 3km zones with a high level of accuracy. Predicting cattle or LSU counts on individual farms was less accurate. Predicted counts were statistically significantly more variable for farms that were contract grazing dry stock, such as replacement dairy heifers and dairy cattle not currently producing milk, compared with other farm types. This analysis presents a way to predict numbers of LSUs and cattle for farms using environmental and socio-economic data. The technique has the potential to be extrapolated to predicting other pastoral based livestock species.
The final chapter deals with conclusions and perspectives on the future of organic farming at the farmlevel. For the dairy farms, there needs to be a better balance be- tween production and demand. This will probably lead to a reduction in the amount of milk which is given the price premium by 30-40%. In the case where these farms stop as organic farms they will reduce the organic area by 30,000 ha. The organic area could therefore be reduced to 130,000 ha. With the lower organic area it is not likely that the organic milk production will exceed 10% of the total Danish milk production. However, it is also likely that farms which stop organic production will continue with an environmentally friendly production not using pesticides and with a limit on the nitrogen application. Many organic farmers have, over the years, come to appreciate this type of production. So although some might change back to conventional farm- ing, they will still use less pesticides than conventional farmers and use the crop rota- tion more actively in order to reduce N-leaching. A smaller organic dairy sector will make the 100% organic manure scenario more costly as the amount of organic ma- nure is lower.
ward selection methods. Thus, in the Icelandic broiler industry, an all-in-all-out policy on the farm does not appear to be associated with Campylobacter colonization during the summer season. One possible explanation for this finding may be related to the changes in the broiler industry that took place following the epidemic in 1999 and the implicated role of fresh broiler chicken products. Broiler producers came under much pressure to reduce flock prevalence. A major emphasis was placed on height- ened strict biosecurity rules on broiler farms, thorough cleaning and disinfection of houses between flocks, and pest control. Rigorous multi-step cleaning and disinfec- tion of the live haul crates and trucks was also initiated. These initiatives began early in 2000. Freezing of products from all flock lots found positive on pre-slaughter sam- pling, and the price penalty to the producer for positive flock lots, ensured continued producer motivation to maintain high standards. This may have reduced the oth- erwise expected importance of an all-in-all-out system. Fifteen percent of the farms in our study were excluded from the analysis due to missing data for one or more var- iables. In order to assess what effect this might have had on our results, we re-analysed the data using all 33 farms, excluding the four variables with missing data (an all-in- all-out policy, manure spreading in the summer season, manure spreading in the winter season, and manure stor- ing). We found that, whether we used 33 farms or 28 farms in our models, our estimates for other domestic livestock on the farm, farm water source, and median flock size were consistent. However, when we used 33 farms, the presence of other commercial poultry and the number of houses did not remain in any of the backward elimination models. It was evident that there was con- founding between the number of houses, the presence of other poultry, and manure spreading & storing practices on the farm. Therefore, by including manure management practices (and hence analysing data from fewer farms), we likely have better estimates for these potentially important risk factors for flock colonization at the farmlevel, and the impact of other variables appears stable.
The aim of the SMART-Farm Tool is to provide a globally applicable tool, which is comprehensive in terms of what is measured and efficient in data requirements. The SMART-Farm Tool models the performance of a farm with respect to the 58 SAFA sub-themes (Figure 1). For each of these the SAFA Guidelines define an absolute globally applicable objective for operators in food and agriculture supply chains . For instance, for the sub-theme Water Quality the objective is “The release of water pollutants is prevented and water quality is restored”. For application in SMART, some of the objectives had to be further delineated to fit into the farming context for an assessment to be completed at farm-level (Supplementary Material A Table S1). For each objective, there is a number of indicators that in combination allow for an assessment of the level of goal achievement, which is expressed on a scale from 0 to 100%. 0% represents a state where all applicable farm activities are counteracting the goal achievement, while 100% represent a state where the respective sustainability goal have been fully achieved by implementing all relevant beneficial activities on a farm and avoiding all relevant detrimental activities to the greatest extent possible. In total, the SMART-Farm Tool (Version 2.1) is based on 327 indicators for the 58 sub-themes. The following sub-sections describe how these indicators have been derived and how they are used for farm assessments.
Pulses are complementary to cereals both in production as well as in consumption. During the production process, pulses help in improving sustainability by (i) fixing the atmospheric nitrogen into the soil (ii) consuming less water and (iii) controlling diseases and pests. On consumption front, pulses reduce malnutrition and improve human health being a rich and most viable source of protein for vegetarians and poor people. Realizing the importance of pulses, the government of India announces various schemes and programs from time to time to promote the cultivation of pulses in the country. However, pulse production in India has not achieved the targeted level. The paper analyses the production trend of pulses over the last decade and identifies the gap between the targets and achievements. Plot level data from cost of cultivation scheme across major pulses growing states has been used to estimate technical efficiencies of the pulses using data envelopment analysis. The paper also highlights the yield gap of the pulses across major pulse growing states and suggests suitable measures for improving farmlevel profitability and sustainability by increasing technical efficiency. The study postulated the hypothesis that technical efficiency of the pulses is low and the yield potential of the pulses are not fully harnessed. The results revealed that increase of technical efficiency by 1% will reduce the yield gap by 9 kg per ha and increase total pulse production of the country by 225 thousand tons.
Seed is an important critical input for increasing production and, the reliability of seeds is very important issue, but there is abundance of spurious seeds in Bihar. Farmers were asked to indicate “whether they ever purchased modern seeds of principal crops” from public sources. About 37.50 percent interviewed farmers reported that they purchased wheat seeds from government sources however access to government sources for wheat seeds was higher for large and medium farmers (<50%) but only 25 percent of marginal farmers purchased wheat seeds from government sources in interviewed villages, indicating less priority to marginal farmers in seed distribution programme of Government. It is worth pointing out that only 15 percent farmers purchased maize seeds from government sources and mainly due to fact that two interviewed villages were adjacent to headquarters of Rajendra Agricultural University, Pusa. None of the farmers of Banka and East Champaran districts purchased maize seeds from government sources because private seed companies dominate the maize seed market in Bihar (Table 3). About one-fourth interviewed farmers of all the four categories purchased paddy seeds from govt. sources. It could be possibly be due to revival of government farms for seed production in Bihar. But almost all farm households used local variety seeds of pulses and only 12.5 percent farmers purchased modern varieties of pulses/ oil seeds from government sources including National seeds Corporation (NSC) and State Farm Corporation (SFC), but none of these were not released within the period of five years. In Bihar, vegetable and fruit crops are grown on only 10 percent of cropped area but they generate 50 percent of income, still governmental efforts have not made any dent in production and sale of vegetable seeds/ fruit saplings in the state. About 97.50 percent of respondents reported use of vegetable seeds either home grown or purchased from market. Despite implementation of NHM, farmers’ access to modern varieties of vegetable seeds has not been improved. State government programmes indicated in Road Map of Agriculture and Allied Sectors for production and distribution of vegetable seeds has not yet been implemented. Thus an urgent need to launch a massive vegetable seed multiplication and technology transfer programme for increasing quality vegetable production in Bihar.
This project has attempted to introduce an efficient smart farm system. It has incorporated automation into various aspects of the farm. A new design for animal enclosures is put forward to improve the living conditions of livestock, as well as reduce manual labor. It includes an automated light, temperature, humidity and sprinkler system. The humidity and moisture control mechanisms make sure the animals are comfortable in the enclosures they are kept in, by adjusting the settings as per requirement. The system is made secure through a password protected digital lock which ensures the safety of animals in their enclosures. The auto lock and release doors can be used to facilitate the incoming and outgoing livestock. Smoke detectors are included to prevent fire hazards which if not detected on time could lead to loss of livestock and valuable resources. The feeder control system times the meals of the animals and reduces the human labor in the process. The system is energy efficient as it helps conserve resources like energy, water and reduces manual labor to a great extent. A GSM module is interfaced to connect all aspects of the modern automated farm. The farm owner has easy access to the system and can control it remotely through his mobile phone. This paper demonstrates that with the integration of information technology to the farm environment, systems and appliances will be able to communicate in an integrated manner. This will result in convenience, energy efficiency, and quality and safety benefits.
In the proposed method, the measurements of farm characteristics are converted into dimensionless values that represent an easy-to-read score of the raw data according to the desirability of the measured performance. For example, when measuring the indica- tor “ S_1 Quality of the products ” higher scores rationally correspond to higher mea- sures. Furthermore, each indicator can range from a minimum or a maximum score (single scores are reported in Appendix); while the minimum score is always zero, the maximum scores vary depending on the social relevance attributed to the indicator, and therefore, more relevant indicators have higher maximum scores. This weighting procedure derived from a subjective evaluation typical of these types of studies that assigned the scores in accordance to the relevance attributed by the literature (when available) and the characteristics of the case study and its objectives (von Wirén_Lehr 2001). This process, which is convenient for adaptation to the local
Seed is the an important critical input for increasing production and, the reliability of seeds is very important issue, but there is abundance of spurious seeds in Bihar. Farmers were asked to indicate “whether they ever purchased modern seeds of principal crops” from public sources. About 37.50% interviewed farmers reported that they purchased wheat seeds from government sources however access to government sources for wheat seeds was higher for large and medium farmers (<50%) but only 25% of marginal farmers purchased wheat seeds from government sources in interviewed villages, indicating less priority to marginal farmers in seed distribution programme of Government. It is worth pointing out that only 15% farmers purchased maize seeds from government sources and mainly due to fact that two interviewed villages were adjacent to headquarters of Rajendra Agricultural University, Pusa. None of the farmers of Banka and East Champaran districts purchased maize seeds from government sources because private seed companies dominate the maize seed market in Bihar (Table 3). About one-fourth interviewed farmers of all the four categories purchased paddy seeds from govt. sources. It could be possibly be due to revival of government farms for seed production in Bihar. But almost all farm households used local variety seeds of pulses and only 12.5% farmers purchased modern varieties of pulses/ oil seeds from government sources including National seeds Corporation (NSC) and State Farm Corporation (SFC), but none of these were not released within the period of five years. In Bihar, vegetable and fruit crops are grown on only 10% of cropped area but they generate 50% of income, still governmental efforts have not made any dent in production and sale of vegetable seeds/ fruit saplings in the state. About 97.50% of respondents reported use of vegetable seeds either home grown or purchased from market. Despite implementation of NHM, farmers’ access to modern varieties of vegetable seeds has not been improved. State government programmes indicated in Road Map of Agriculture and Allied Sectors for production and distribution of vegetable seeds has not yet been implemented. Thus an urgent need to launch a massive vegetable seed multiplication and technology transfer programme for increasing quality vegetable production in Bihar.
The data on the farm capital structures, financial positions and loan rates suggest that the European farming sector is a combination of quite different farm business strategies, capabilities to generate capital revenues, and segmented agricultural loan market regimes. In some countries, such as Denmark, farmers have adopted quite aggressive farm expansion strategies, while in other countries such as Italy the farmer expansion strategies have been more modest. The different business strategies have substantial, and perhaps more substantial than expected, implications for the financial leverage of farms. Italian farms appear to operate fully on their own capital, with average equity ratios being between 97– 98% depending on the production line. Their economic resilience does not, therefore, directly depend on the performance of the financial market, with possibly a few exceptions on farms with a high leverage. These high equity ratios may, however, also signal that the access of farmers to fair credit and/or farming assets may have been more constrained in Italy than in other countries. This issue is left here to be addressed in more quantitative credit market analyses.
level data set. Most of the studies in the literature lack comprehensive examination of farmers' decisions on the use of different marketing arrangements for different commodities. To achieve the goal, I first estimate a behavioral model explaining farmers’ joint decisions on which commodities to produce and which marketing channels to use when selling their outputs. In the first part, I use a discrete choice model. Modern discrete choice models had been widely developed and applied by economists. Hundreds of papers use discrete choice models to estimate both individual and aggregate demand (Mcfadden 1974; Dubin and Mcfadden, 1984; Berry, Levinsohn and Pakes, 1995; Einav, 2007). It is also a common methodology used to estimate recreation demand in environmental economics (Bockstael et al., 1989; Train, 1998; Murdock, 2006 and Timmins and Murdock, 2007). Basically, I adapt the discrete choice random utility maximization model to examine farmers’ choices on combinations of commodities and marketing channels, following the framework in Murdock (2006) and Timmins and Murdock (2007). In my model, a choice is defined as a unique combination of commodities produced and marketing channels used. The farmer is assumed to compare the utilities he gets from each of the possible production regimes and then selects the production regime that yields the highest utility to him.
The specific objectives of this study were therefore to evaluate variations of soil nutrient stocks at farmlevel, measure and estimate the major nutrient flows at farmlevel as a means to describe current farm nutrient manag- ement, to identify the key factors influencing land mana- gement in perennial crop-based farms in the humid fore- sts of South West Cameroon in a bid to discover some of the underlying causes of soil fertility depletion. This was particularly supposed to be done using the Nutrient Mo- nitoring Programme (NUTMON) or software, a tool that was developed to assess nutrient balances (stocks and fl- ows of some macro-nutrients - N, P and K), biomass fl- ow and economic performance at farmlevel .
Fragmentation of agricultural land is widespread in the world and is the result of various institutional, political, historical and sociological factors, such as inheritance laws, collectivisation and consolidation processes, transaction costs in land markets, urban development policies, and personal valuation of land ownership (King and Burton, 1982; Blarel et al., 1992). Farm land fragmentation (LF) is a complex concept that encompasses five dimensions covering: i) number of plots farmed; ii) plot size; iii) the shape of plots; iv) distance of the plots from farm buildings; v) distances between plots (or plot scattering). From the public economics perspective, LF may generate both positive and negative externalities: it may increase biodiversity and society’s economic value of landscape but, conversely, it may induce additional trips by farmers that result in extra roadworks, road safety issues, greenhouse gas emissions, etc. First and foremost, however, LF may affect farmers’ production decisions and thus impact farm performance. This impact may be negative or positive. The impact may be negative, for several reasons. First, LF may exacerbate conflicts regarding labour allocation on the farm: it takes time to travel from one plot to another while the labour force could be undertaking more productive tasks. Second, production costs may be increased as LF may require additional equipment, secondary farm buildings and/or external service expenses. Third, LF may restrict the choice of production and constrain management practices, especially in terms of herd management. This could be true for regions where dairy production prevails, such as Brittany, a region in the west of France. Fourth, investments for soil quality improvement, such as drainage, may be reduced on remote plots, potentially reducing yields. However, the impact of LF on farm performance may be positive. This is the case if LF leads to an increased diversity in land quality so that the allocation of crops across plots may be optimised, potentially resulting in higher overall yields. In addition, LF may give greater opportunities for risk diversification, thereby reducing production risks at the farmlevel. For example, a fragmented farm would be less affected by a pest outbreak that spreads on contiguous plots only.
( Olayemi , 1998). The reality is that Nigeria has not been able to attain self-suﬃciency in food production, despite increasing land area put into food production annually. The constraint to the rapid growth of food production seems to mainly be that of low crop yields and resource productivity. This is revealed by the actual yields of major food crops, compared with their potential yields ( Federal Ministry of Agriculture , 1993). The low yield of crops may also be attributed to a relative decline in rainfall in recent years. Studies by Jagtap (1995) showed that annual rainfall in Nigeria during 1981-90 declined from that in 1961-70. The greatest change occurred in the onset of the rainy season and the extent of early rainfall, which resulted in a reduction by nearly one month in the growing season. There were fewer wet days and higher rainfall intensities in most of the country. The rainfall series showed prolonged dry periods, especially since 1970. The rainfall decline is unprecedented in duration; spatial, temporal character and seasonal expression ( Kamara et al., 2006) Thus, drought is one of the major causes of yield loss in the guinea savannas. This has aggravated the food supply situation in the area resulting in low food security index ( Federal Republic of Nigeria , 2002). This paper examines the determinants of food crop production and technical eﬃciency in the Guinea savannas of Nigeria. A pre-requisite for enhanced eﬃciency is the iden- tiﬁcation of those factors which prevail at the farm-level and which aﬀect eﬃciency of production. Thus, it will help in providing information for the formulation of appropriate policies.
Population density has led to land scarcity in the rural farm households. This has adversely affected livelihood activities in agriculture leading to low income. Farm households in KTZ tend to rely on alternative income sources to improve their household income and increase agricultural production. However, the households level of livelihood diversification to different income sources beyond agriculture vary across land holding size. Thus, the aim of this article is to measure the level of diversification of farming household’s livelihood into non/off- farm activities in the study area. A huge amount of farmlevel primary data was collected from the study area individual farmers through personal interview using structured questionnaire. A total of 252 sample households who were selected through a combination of purposive and stratified random sampling techniques were retained for subsequent analysis. The finding of the survey result indicates that ninety seven percent of the respondents in the study area diversified in to non-farm activities. The study has also shown that non-farm income accounts for 53% total income of rural households in rural Ethiopia. The Composite Entropy Index has been used for measuring livelihood diversification. The livelihood diversification index of 0.260 (CV 94) showed that majority of the household heads undertook one form of livelihood diversification strategies or another. It has been argued that there is a significant difference (at 1% level of significance) among different farm size with respect to level of livelihood diversification. It is evident that livelihood diversification is the highest among small land holding groups and small holders derive a higher proportion of their income from non-farm sources than large farm holders. Therefore, the smallholder farm households’ participation in lucrative non -farm activities needs to be strengthened.
Cultivated and associated plant diversity, were compared (the last at 3 scales: alpha, beta and gamma), in 2 farms under conventional and organic management in La Plata. The relevé method was used for associated species recording in spring and summer. At farmlevel we assessed total spontaneous specific richness (γ diversity), genus and family richness. Richness – species total per crop- was used as index of α diversity. We also calculated the median richness per crop sample unit. A randomization method was used to assess the degree for which differences between the observed and expected median richness per crop in a management style are attributable to chance. β diversity values were compared to a random framework, separately for each farm. Results indicate that the organic farm had higher associated diversity at all levels; also, higher number of cultivated plots and species, and proportion of exclusive, perennials, native and utilitarian species. In the organic farm crops with higher or lower median richness than expected, and higher β, indicated higher spatial and seasonal heterogeneity. Conventional management limited spontaneous plants to a reduced, homogeneously distributed group. Other factors may influence associated plant diversity.
addition of crop produced by farmers. It is an enterprise where the required facilities for primary and secondary processing of agricultural produce e.g. cereals, pulses, oilseeds etc. are made available. In it, agro-based value added products are made at village level itself which may be sold to nearby markets. The post harvest losses may be reduced and value addition may be done at rural / farmlevel itself. So, it is a mean of providing income and employment to rural people / entrepreneurs through agro-based processing activities of various locally available agricultural produce.
The above distinction is also reflected in empirical works, with studies at the farm-household level largely based on microdata at the farmlevel, and studies on farm labour (re)allocation conducted at the aggregate (country or regional) level. Microdata allow us to address the individual adjustment behaviour in response to changes in factors affecting the household utility, such as alternative revenue sources. For example, Mishra & Goodwin (1997), using a Tobit model on farm households located in Kansas, found that policy changes reducing farm income support can increase the off-farm employment of farmers and their spouses. Similarly, El-Osta et al. (2004) investigated the effect of payments under the US Agricultural Market Transition Act on agricultural labour supply using 2001 data. Results indicate that government payments tend to increase the hours that operators work on-farm and vice versa. There are also important examples of microdata analysis using panel data (e.g. Pietola et al., 2003; Gullstrand & Tezic, 2008) and semi-parametric approaches (e.g. Pufahl & Weiss, 2009; Esposti, 2011a). 7
A farm-level transmission network was created for each of 100 simulated disease spread to calculate vari- ous summary statistics of their epidemic characteristics. Although we knew exactly who infected whom on an individual animal level, we chose to describe the disease transmission on a farmlevel because the latter is of inter- est in many epidemic situations. Two types of variables were computed; variables related to super-spreader and those related to other epidemic characteristics. Vari- ables related to super-spreader included (1) R calculated as the average number of secondary cases generated by each infected farm, (2) their standard deviations, (3) the maximum R divided by the total number of infected farms (hereafter, max R proportion), which indicates how dominant a super-spreader was in the epidemic, (4) max R proportion calculated after excluding index farm, (5) whether or not a super-spreader with different R cut-offs (10, 15, 20, 30, and 40) existed, and (6) same measure as (5) but after excluding index farm. Variables related to other epidemic characteristics included (7) whether the index farm was sampled (8) the average shortest path lengths (defined as the number of farm-level transmission events separating any two given infected farms) between all infected farms or (9) between all sampled farms, (10) the average shortest path lengths from the index farm to all infected farms or (11) to sampled farms, (12) epidemic duration in days, (13) number of infected farms, (14) the proportion of infected farms sampled, and (15) normal- ised Sackin index. The normalised Sackin index repre- sents an imbalance of tree such as phylogenetic tree  and transmission tree . We computed the normalised Sackin index I s using the following formula :