ABSTRACT: Milk produced in Brazil has been seen as having poor quality and is associated with a large number of small farms. However, there are few studies demonstrating lower quality of milk of small properties. Thus, this study aimed to evaluate the relationship between production scale on dairy farms and milk quality, how it behaviors throughout the year and set goals to improve quality according to each strata. A total of 21,917 analysis of 409 farmers conducted from January 2005 to December 2014 were used. To study the database, the properties were divided according to monthly average daily milk yield: 10 to 100; 100 to 200; 200 to 500; 500 to 1,000; and 1,000 to 5,000L of milk day-1. The data showed that dairy farming is predominantly carried out on small-scale production properties; however, the highest volumes are produced by a small number of producers. Additional data reveals that milk quality can vary because of distinct factors as nutritional condition and feed supply. Quality of the milk produced should be a matter of concern for the entire milk-production chain, because it still has problems such as high total bacterial count, high somatic cell count and low solids.

This dataset collected for the organic core POWER project to assess resilience capacities of organic pig prodcuers in Austria, Danemark, Italy, Sweden and Switzerland. These datasets have been anonymized.

The resilience farm data are all data that where observed at farm level, and contain farm characterisitcs, namely

| variable | description | values | | farm id | unique identifier of the farm | characters, including country code based on ISO2 | | breeding type | type of pig entreprise on the found on the farm | breeding, finishing or both | | entrerprise_x | description of other entreprises found on the farm | feed production, cash crop, chicken, sheep, dairy, beef, direct marketing, tourism, on-farm processing, horse housing. | | number non-pig entreprise | number of entreprise describes | integer | | structure | type of pig housing structure | permanent, temporary, both | | outdoor area | type of oudoor access for pig | concrete, shifting arable land, permanent pasture | | LSU | livstock standard units computed following Eurostat standards | numeric | | pig/ha | intensity of production as LSU/UAApig | numeric | | self-sufficiency | percentage of pig feed produced on farm | numeric | | UAA pig | utilized agricultural area for the pig production | numeric | | UAA total | utilized agricultural area of the farm | numeric |

The resilience data is the result of the interpretation of farmers' resilience narratives, which were interpreted been interpreted using the Meuwissen et al, 2019 farming systems framework. The data is in long fromat and represents a particular resilience capacity related to a specific shock. More particularly, the data contains the follwing information

| variables name | description | values | | farm id | unique identifier of the farm | characters, including country code based on ISO2 | | country | country code | based on ISO2 | | question related to shocks | shocks to which the resilience narrative related to | input cost, price, outbreak, climate, legislation, labour, general | | narratives (a= first, b=second) | identifier of the narrative within a question | a, b | | capacity | resilience capacity following the Meuwissen et al (2019) framework | robustness, adaptability, transformability, non-resilience | | resilience attribute type | resilience attribute based on an expanded interpretation the Meuwissen et al (2019) framework (see paper) | functional diversity, response diversity, modularity, tighness of feedback, social capital, attitude, system reserve (physical captial -inherent), system reserve (physical capital -use), system reserve (natural capital -inherent) system reserve (human capital - use) | | resilience attribute | description of the attribute that led to the resilience attribute type classification | ability to convert to cash crop, ability to offer good working conditions, ability to switch brand, access to financial services, access to technical solutions, adapted crops, adding finishing section, adjust feed production, adjust volume of pig production, adjusting paddock size to enable double fencing, advisory and veterinary services, believe in organic, brand building with social media, build temporary shelter, build up savings, by-product through partnership, capacity to access more land, change external feed, change feed ratio, conservable end product, create microclimates, create new brand, created a young farmer network, customer relation, decrease pig, decrease pig production, direct marketing, diverse farm, diverse sale channels, do something else, double fencing, efficiency, entrepreneurship, excess cereal production, exploring governance model as no successor, family labour, farmer owned value chain, fencing, financial lock-in, flexible infrastructure (enabling), flexible pig keeping system, forest system, good indoor infrastructure, good infrastructure, good relation to customers, governmental support, habit, has margin, home feed production, inadequate salary, increase cash crop, increase own work, increase own working time, independent feed ratio, indoor keeping, indoor production, innovator, innovator (one welfare) , insurance, margins, mechanisation, mobile mode of production, neighbor network, neighborhood early warning, neighborhood network, new cooling infrastructure, niche production, no competition, no fencing option, no own farm, land or infrastructure, no qualified staff required, offering jobs to young people, other livestock, part time worker, partnership with other farmers, producing more home grown feed, profit, reduced pig production, rely on sectoral organization, resistant breed, robust animals, robust breed, sectoral power, sectoral response, short term feed contracts, social media, soil health, split production on other farms, staffing agency (through advisory services), sufficient outdoor space, sufficient pasture, sufficient space, sufficient space (enabling), switch to indoor production, switch to other livestock, tiredness in the sector, Too big to fail, training, unique pig keeping system, up-to-date infrastructure, volunteer networks, wallow, work with nature |

To compute the resilience capacity score (Cscore)

assign 0 to lack of resilience, 1 to robustness, 2 to adpababilty and 3 to transformability. If there is more than one narrative with a different capacity, the average score between mentionned capacities was taken.

Use following R code in dplyr

mydata<- ResilienceDataPreProcessed %>%

mutate(code = ifelse(capacity=="robustness", 1,ifelse(capacity=="adaptability",10,ifelse(capacity=="transformability",100,ifelse(capacity "no resilience capacity",1000,ifelse(capacity"no long term resilience capacity",10000,ifelse(capacity=="no short term resilience",1000,NA)))))))%>%

group_by(farm, question)%>%

summarise(Ccode=sum(code))%>%

mutate(Cscore=ifelse(Ccode==1|Ccode==2| Ccode==3, 1,ifelse(Ccode==20|Ccode==10|Ccode==111,2, ifelse(Ccode==100|Ccode==200,3, ifelse(Ccode==11|Ccode==21, 1.5,ifelse(Ccode==110|Ccode==120, 2.5,ifelse(Ccode==101,3,ifelse(Ccode==1000,0,ifelse(Ccode==10001|Ccode==1001,0.5,NA)) ))) ))))

Resilience questionnaire

Farm number:

Farm name or ID:

Country:

System descriptors

| Breeding or finishing (or both) | | | Indoor or outdoor (or a mix) | | | Organic or conventional | | | Number of years organic | |

1) Has your farm experienced significant challenges in the last 5 years?

| Yes or no? | Yes / No | | If "no", what factars (farm/external) created this resilience? |

| | If "yes", please describe the 1st challenge |

| | What was the impact on the farm (production, animal health/welfare, work load, work life quality etc)? |

| | Did this change your management or farm structure subsequently (and how)? |

| | If "yes", please describe a 2nd challenge |

| | What was the impact on the farm (production, animal health/welfare, work load, work life quality etc)? |

| | Did this change your management or farm structure subsequently (and how)? |

|

2) In the future, how do you feel your pig system would cope with these challenges:

a) Decreasing or negative margins due to increased feed or other input costs?

| Very severely (e.g. bankruptcy) | | | Severely (e.g. closure of pig enterprise) | | | Strong impact (e.g. large reduction in production) | | | Short term impact (e.g. reduced production) | | | Little impact (e.g. change ration) | | | | | | Why? |

| | How are you prepared for this potential challenge? (what are the characteristics of your farm or management that make you more or less prepared for this challenge) |

|

b). Decreasing or negative margins due to reduced pig prices?

| Very severely (e.g. bankruptcy) | | | Severely (e.g. closure of pig enterprise) | | | Strong impact (e.g. large reduction in production) | | | Short term impact (e.g. reduced production) | | | Little impact (e.g. change ration) | | | | | | Why? |

| | How are you prepared for this potential challenge? (what are the characteristics of your farm or management that make you more or less prepared for this challenge) |

|

c) Wide spread disease outbreak such as African Swine Fever

| Very severely (e.g. bankruptcy) | | | Severely (e.g. closure of pig enterprise) | | | Strong impact (e.g. large reduction in production) | | | Short term impact (e.g. reduced production) | | | Little impact (e.g. change ration) | | | | | | Why? |

| | How are you prepared for this potential challenge? (what are the characteristics of your farm or management that make you more or less prepared for this challenge) |

|

d) Climate change impact, e.g. severe storms, flooding, drought, hot seasons

| Very severely (e.g. bankruptcy) | | | Severely (e.g. closure of pig enterprise) | | | Strong impact (e.g. large reduction in production) | | | Short term impact (e.g. reduced production) | | | Little impact (e.g. change ration) | | | | | | Why? |

| | How are you prepared for this potential challenge? (what are the characteristics of your farm or management that make you more or less prepared for this challenge) |

|

e) Changing legislation impact, e.g. increased floor space allowance indoors, mandatory access to pasture, more land required (lower stocking densities to reduce nutrient loads from pasture systems or in general for the whole farm)

| Very severely (e.g. bankruptcy) | | | Severely (e.g. closure of pig enterprise) | | | Strong impact (e.g. large reduction in production) | | | Short term impact (e.g. reduced production) | | | Little impact (e.g. change ration) | | | | | | Why? |

| | How are you prepared for this potential

"The U.S. Department of Agriculture's (USDA) Farm Service Agency (FSA) provides emergency loans to help farmers and ranchers who own or operate a farm/ranch located in a county declared by the President or designated by the Secretary of Agriculture as a primary disaster area or quarantine area.

Emergency loan funds may be used to: Restore or replace essential property Pay all or part of production costs associated with the disaster year Pay essential family living expenses Reorganize the farming operation Refinance certain debts, excluding real estate

Loan applicants may borrow up to 100 percent of their total actual production and/or physical losses. The maximum loan amount is $500,000.

Loans for crops, livestock, and non-real estate losses have a repayment term usually between 1 to 7 years depending upon the loan purpose, collateral, and repayment ability. Loans for physical losses to real estate normally have a 30-year repayment term, not to exceed 40 years."This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: Farm Emergency Loans For complete information, please visit https://data.gov.

Agricultural landscapes in sub-Saharan Africa are dynamic and are shaped by farmers' land use decisions and livelihood strategies over time. Farmers’ decisions are influenced by the opportunities and constraints emanating from different socio-economic, biophysical, and political drivers. Ethiopia is now the second most populated country in Africa with more than 100 million people and an annual population growth rate of 3%. Here, we assess how the on-going expansion of arable land and urban areas is affecting the availability of common resources, such as forest and grazing land, and the availability of biomass for food, feed, and energy. Taking the Hawassa area of Sidama zone, Ethiopia as a study case, this study aims at analysing the drivers of change of farming systems, assessing farmers’ responses to these drivers and appreciating the consequences for the agricultural landscapes’ composition. We found that (i) national level policies, climate and soil fertility changes, population increase, and urban expansion were major drivers of farming systems change in the Hawassa area, (ii) forests and grasslands have been progressively replaced by cropland and urban areas, and (iii) these changes resulted in fragmentation and diversification of local agricultural landscapes with potential consequences for ecosystem service provision. Farmers responded with the following three main livelihood strategies: consolidation (maintaining food crops and livestock), diversification (combining agricultural and off-farm activities) and specialisation (increase in cash crop production). This research contributes to the ongoing debate about the viability of small farms.

Study area We conducted our study in the woreda (district) of Arsi-Negele, located in the Oromia region of Ethiopia. The study area covers about 100 km2 between 38°42.14' - 38°49.92' E and 7°15.05' - 7°22.57' N. It borders the state forest of Munesa, and encompasses parts of the three kebeles (sub-districts) of Ashooka, Bombaso Regi and Gambelto, in which a total of six villages were studied. Altitudes here range between 1,970 and 2,200 m above sea level. The climate is sub-humid, characterized by a mean annual rainfall of 1,075 mm per year (18-year average) and a mean annual temperature of 15°C (16-year average). The study area is characterized by bimodal rainfalls, with a short rainy season from March to May, and a long rainy season from July to September. The natural vegetation is classified as dry afromontane forest (Tesfaye, 2007). Wheat (Triticum sp. L.), maize (Zea mays L.), potato (Solanum tuberosum L.) and enset (Ensete ventricosum (Welw.) Cheesman) are the primary crops under cultivation. Most farmers keep livestock in the form of cattle, sheep, goats, and donkeys. The Sida Malkatuka village and Dikitu Shirke village (in Ashooka kebele) border the state forest of Munesa and form a zone referred to as the ‘high tree cover’ zone in the rest of the paper (Fig. 1). Households in the high tree cover zone use the Munesa forest for fuel and livestock feed (Baudron et al. 2017). A second zone of medium tree cover encompasses Gogorri Lako Toko village (in Ashooka kebele) and Kararu Lakobsa Lama village (in Bombaso Regi kebele) and is located about 5.5 km away from Munesa forest (Fig. 1). Households from the medium tree cover zone make extensive use of a large communal grazing area for fuel and feed (Baudron et al. 2017). A third zone of low tree cover encompasses the villages of Shodna and Belamu (in Gambelto kebele) and is located about 11 km away from Munesa forest (Fig. 1). Households in the low tree cover zone lack access to common grazing or forest areas (Baudron et al. 2017). Land use classification and agricultural productivity Contemporary land cover was determined using RapidEye imagery (5-meter resolution) from January 2015 and land was classified into five basic classes: cropland/bare soil, grassland, natural forest, plantations/woodlots, and enset homegardens, following the method described in Baudron et al. (2017). We defined a class ‘tree cover’ by merging the classes ‘natural forest’ and ‘plantations/woodlots’. To relate our findings to different proxies of productivity, we interviewed the head of each household in the study area between December 2014 and February 2015. A total of 266 households were interviewed (88 in the high tree cover zone, 97 in the medium tree cover zone, and 81 in the low tree cover zone) using a standardized questionnaire addressing crop, livestock, and household fuel management. A farm typology was delineated based on self-categorization exercises conducted in each zone, and a stratified subsample of nine farms was selected in each zone (27 farms in total) for which resource flow maps (i.e., maps of each farm showing the flows of resources between components in the farm and to and from the farm) and resource use calendars were produced (Geifus 2008; Giller et al. 2011). In addition, the area of each field was measured using a hand-held global positioning system (GPS) Garmin Etrek 10. Empirical measurements of daily fuel consumption were conducted in nine of these 27 farms (one farm per type and per zone, selected randomly) once in March 2015 and once in August 2015). Crop productivity per zone was calculated by dividing the total quantity of grain, tuber and fresh product harvested in the zone (from interview data) by the area of the zone, and multiplying this by the USDA’s specific standard value of dry matter content (https://ndb.nal.usda.gov/ndb/search). Feed productivity per zone was calculated by estimating the total biomass consumed by livestock in the zone and dividing it by the area. For each zone, the total biomass consumed by livestock was estimated by converting livestock numbers into Tropical Livestock Units (TLU), using a value of 250 kg live weight for one TLU (Houérou and Hoste 1977), and by assuming a daily feed intake of 5 kg DM TLU-1 (i.e., 2% of live weight). Oxen and bulls were assumed to be equivalent to 1.1 TLU, cows to 0.8 TLU, steers and heifers to 0.5 TLU, calves to 0.2 TLU, sheep and goats to 0.09 TLU, and donkeys to 0.36 TLU (Gryseels 1988). The total biomass consumed by livestock in a particular zone was then allocated between the zone itself (biomass consumed within the village), Munesa forest, and purchased feed, using resource use calendars. Fuel productivity per zone was calculated by estimating the total biomass used as household fuel in the zone and dividing it by the area. For eac... Visit https://dataone.org/datasets/sha256%3A4dd977fa44123a50320e0abff1abcbc7709a3d8f1d1a48539540fb16a5e3172d for complete metadata about this dataset.

Farm input price index (FIPI). Quarterly data are available from from the first quarter of 2002. The table presents data for the most recent reference period and the last four periods. The base period for the index is (2012=100).