| Livestock Research for Rural Development 32 (11) 2020 | LRRD Search | LRRD Misssion | Guide for preparation of papers | LRRD Newsletter | Citation of this paper |
Improving biosecurity on small commercial broiler farms in Indonesia will reduce the risk of disease and improve food safety. A study was undertaken over a 2-year period to assess the productivity and economic effects of improving biosecurity and management in small commercial broiler farming systems. Seven project farms and four control farms were monitored before and after recommended interventions were agreed to and implemented on project farms. The interventions included training in flock vaccination and water management, brooding and grower management, disease management, and structural and operational biosecurity.
The results indicate that by improving farmer capacity, farm management and biosecurity, broiler farmers can improve their productivity and, depending on the type of improvement and the type of production contract, receive financial benefits. The Performance Index (PI) on project farms increased from 301 to 310 (a 3% increase) while the PI on control farms during the same period declined by 2.6%. This indicates that the PI on project farms was 5.6% higher than it would have been without the biosecurity and management improvements. This improved efficiency translated into higher enterprise returns. Project farmers received an additional USD 45.00/1,000 birds, while in the same period the income on control farms was reduced by USD 33.00/1,000 birds. The total economic benefit, therefore, of implementing the interventions was USD 78.00/1,000 birds. This benefit was attained with farmer investments of only USD 2.00/1,000 birds.
The type of contract will determine the proportion of benefits that accrue to the farmer and the proportion that is appropriated by the integrator company. Small commercial broiler farmproductivity can be improved by integrator companies working in partnership with their farmers to improve biosecurity, farm management and vaccination programs. The results of these improving partnerships will be the supply of safe food products at a price that encourages investments in farm biosecurity and farmer capacity development.
Key words: productivity, farmer capacity, economic benefit
Small commercial broiler farms in Indonesia continue to be affected by institutional, political, technical and financial constraints (Mulder, 2019), while consumers are becoming increasingly concerned with food safety and animal welfare (Wahyono and Utami, 2018). Farmers are, therefore, looking at ways to improve their profitability under changing market conditions. Improving biosecurity not only offers the opportunity to increase productivity but also produce a safer and healthier product that the end user is increasingly demanding (Directorate of Veterinary Public Health, 2006). In poultry farming, biosecurity is defined as management activities that aim to keep diseases out of the flock and if diseases do gain entry, limit disease spread around and off the farm. Biosecurity is regarded as key to controlling poultry diseases and improving both poultry and human health and livelihoods (Beach et al 2007, FAO 2008, Patrick and Jubb 2010, Fasina et al, 2011).
If farmers are to be encouraged to improve their biosecurity they must be convinced that there are personal economic benefits in them doing so (Susilowati et al, 2013). If farmers are to invest in infrastructure and improve management they must be duly compensated, production increases and improvements in community welfare may not be enough to encourage individual farmers to invest.
This study aims to evaluate the production and hence the economic benefits of improving biosecurity on small commercial broiler farms. Detailed information was collected from seven farms over a two-year period and used to evaluate the changes in production before and after the adoption of improved biosecurity activities. These results were compared to four control farms where no improvements were made. Comparisons were made with regard to changes in the level of biosecurity as measured by the Farm Biosecurity Score (FBS) before and after adoption of agreed interventions, farm production and efficiency of production and, economic returns to farmers.
The study consisted of two phases; a pre-intervention and an intervention phase. Each phase was one year in duration. The pre-intervention phase included four or five production cycles. During this phase the partner farms were visited weekly by a Veterinary Services Officer (VSO) to assess farm management and biosecurity practices. Weekly data collection was necessary as broilers have a short production cycle which includes a range of management changes as the chickens grow. From these observations, a Farm Biosecurity Score (FBS) for each production cycle was calculated (Susilowati et al, 2013). The FBS includes the important components that make up good farming practice and allows comparison between farms before and after implementation of improved biosecurity practices. The farms were assessed using a check list of activities related to production management and biosecurity. Based on the characteristics of the farms, each farm was allocated a FBS. The higher the FBS, the higher the level of biosecurity.
The activities were divided into eight components; these originated from discussions between the FAO ECTAD team and VSOs in the project area, these were;
At the end of the production cycle, production and financial data were collected from the sales notes that farmers received from the buyer. Based on the ensuing assessments, a farm plan was devised and an action plan agreed to by each farmer. This was implemented during the intervention phase. During this second phase, production data were again collected from every farm, and in each production cycle to estimate production performance and monitor the impact of the changes on farm management and performance. The production indicators were:
As part of the analysis, control farms were identified, and production data collected from the contract companies that they worked with. Initially, nine control farms were selected for this study, however, during the study period some of the farms signed with other contract companies and hence consistent data could not be collected. Data from only four control farms are used in the final analysis. During the study period, financial data were also collected to calculate the impact of the changes to farm’s profitability using partial budget analysis.
At the beginning of the study, there were 11 farmers who wished to participate in the study, but during the pre-intervention phase two farms decided they could not continue. One farm had its shed destroyed by storms and the other suffered financial difficulties. At the beginning of the intervention phase, a further two farms were removed from the analysis as they did not provide adequate production data. One farmer regularly switched between companies as companies refused him contracts due to poor production performance. This farm also did not wish to implement recommendations provided by the FAO project team or from the contract company. Another farmer did not provide production data as records were not kept. This farm was an independent farm so there was no obligation to report production and financial results. Therefore, seven commercial broiler farms remained in the study.
There are certain structural and geographic characteristics of the farm that are outside the control of the farmer and yet may influence productivity. Table 1 provides some baseline information concerning some of these non-management factors that may influence productivity and a farmer’s ability to control disease.
|
Table 1. General information from the participating farms |
||||||||
|
Farm |
Total |
Farming |
No. of |
Shed size |
Density |
No. of |
Owner |
|
|
1 |
13,000 |
Contract |
2 |
1,350 |
1: 9.6 |
3 |
Yes |
|
|
2 |
9,000 |
Independent |
2 |
1,100 |
1:10 |
2 |
No |
|
|
3 |
7,500 |
Contract |
1 |
720 |
1: 10.4 |
2 |
No |
|
|
4 |
12,000 |
Contract |
4 |
1,365 |
1: 8.8 |
4 |
No |
|
|
5 |
3,000 |
Independent |
1 |
300 |
1: 10 |
1 |
Yes |
|
|
6 |
4,500 |
Contract |
1 |
450 |
1:10 |
1 |
Yes |
|
|
7 |
10,000 |
Contract |
2 |
920 |
1:11 |
2 |
No |
|
These farms were spread over six local government sub-districts with bird numbers ranging from 3,000 to 13,000 per farm. Five of the participating farms were contract farms and two were independent farms. On four of the participating farms, non-family members were employed as farm managers. Direct owner involvement may lead to improved management and greater incentive to maximise production and income. A paid employee may not have the same incentive.
Of the five contract farms only two were with the same company, the other three (1, 4 and 7) all had contracts with different companies. Differing contracts may mean different monitoring procedures and several types and quality of feed, DOC, drugs and vaccines. Feed for both contract and independent farms are provided by, or bought from, the large integrator companies. The contract farms, in particular are not able to select the feed that they provide to the chickens but it is in the best interest of the company to provide high quality and clean feed and DOC in order to maximise productivity. All feed and DOC are sourced in Indonesia. There may also be different bonus structures within the contract that provide different incentives to improve efficiency and productivity (Komaladara et al 2016). These differences have the potential to affect individual farm productivity.
None of the farms had elevated housing. Elevated housing allows greater ventilation which is essential in reducing the possibility of respiratory problems and disease. When the ventilation is poor, it may be necessary to reduce the population density which may reduce profitability. The average density of the cages was nine chickens per square meter with an average live weight of chicken at harvest between 1.6 and 1.8 kg per chicken.
The eight biosecurity components are divided into two score groups; biosecurity and management (Table 2). Each farm was allocated scores for each component, which were then amalgamated into the two group scores. Farms 1 and 4 had the highest scores. These farms had good DOC management during the first 48 hours, good flock density ratios, effective cleaning and disinfection of feeders and drinkers, clean litter and implemented the correct vaccination programs. Based on this information, specific improvement plans were developed for each farm and implemented in the intervention phase.
|
Table 2. Farm Biosecurity Scores for broiler farms pre-intervention (max of 100) |
||||||||
|
Farm No. |
1 |
2 |
3 |
4 |
5 |
6 |
7 |
AVG |
|
Biosecurity |
40 |
23 |
17 |
27 |
32 |
8 |
28 |
25 |
|
Operational |
42 |
21 |
21 |
42 |
26 |
16 |
32 |
29 |
|
Structural |
38 |
25 |
13 |
13 |
38 |
0 |
25 |
20 |
|
Management |
69 |
58 |
62 |
69 |
46 |
63 |
51 |
60 |
|
Water |
50 |
50 |
50 |
75 |
25 |
50 |
25 |
46 |
|
Brooding |
62 |
62 |
76 |
76 |
62 |
71 |
62 |
67 |
|
Growing |
60 |
70 |
70 |
50 |
30 |
50 |
60 |
54 |
|
Farm |
82 |
45 |
45 |
91 |
45 |
82 |
55 |
53 |
|
Feed |
90 |
60 |
60 |
70 |
50 |
80 |
70 |
69 |
|
Disease |
73 |
64 |
73 |
55 |
64 |
45 |
36 |
59 |
With regard to operational biosecurity, Farm 6 was the poorest with a score of 16. This low score was due to minimal precautions or procedures limiting the movement of people in and around the shed. There were also significant levels of dirt, manure and used litter lying around the shed and environs, and a lack of vermin control. Effective litter management involves ensuring litter thickness of 8 cm and kept dry. Manure management involves composting and use on farm as a natural crop fertilizer On-farm disposal and use minimises the potential risk of disease movement to other farms, farming systems and the general community. Effective control of waste products has implications not only for the farm but also for the local community, Indonesia and the global community. Farms 1 and 4 had the highest scores (42) for this component, as their sheds had a wall and locked-gate that could only be accessed by the farmer; the chicken houses are made of concrete, hence, significantly easier to clean at the end of a production cycle.
Most farms had poor structural biosecurity. Farm 6 had virtually no structural means of minimising disease movement in or around the farm. It is located on a main road and near a village. It is also near other broiler farms (< 50 meter distance), has no fences or farm gate. In terms of ensuring a clean and consistent supply of water, some farms are better than others. Farm 5 had an insufficient number of drinkers. At Farm 7, there was no water treatment, no regular cleaning of the water tanks and the drinkers were not cleaned twice a day. These two farms had the lowest score (25) compared to the others.
Four farms (1, 2, 5 and 7) had a similar low score (62) for the management of the brooder phase, while Farms 3 and 4 had a similar high score (76). This was because these better-managed farms have the correct ratio of heaters to chickens, provide an appropriate depth of litter, and use double curtains in the shed.
For management activities during the growing period, Farm 5 had the lowest score for this component (30). This farm had poor ventilation and litter management. Farm 6 (91) maintained good production and financial records for each cycle and used these data to plan future activities and investments.
Farm 1 had the highest score for feed management (90). It had a dedicated feed shed with good ventilation located away from the chicken sheds. Feed was stored on pallets and vermin were controlled. Other farms often stored feed in the chicken shed, where it was more open to the weather and rats. With regard to animal health and disease management, Farm 7 had the lowest score (36). At this farm dead and sick chickens were not removed appropriately, and medicines, particularly antibiotics were used routinely rather than in response to disease.
The highest combined management scores were recorded by Farms 1 and 3 (69). These two farms implement basic biosecurity such as restricting people and vehicle movement, use effective disinfectants, feet dipping, have (medium or high) fences and a gate.
The project farms were small and medium size broiler farms, ranging from 3,500 to 13,000 birds per cycle (Table 3). Average liveability on the project farms was 95 percent, while the average FCR was 1.64. The performance of project farms was measured using the performance index (PI) which combines liveability, FCR, body weight (BW) and age at sale into a single estimate. The average PI of the project farms was 300, which is poor by Indonesian standards (Santoso and Sudaryani, 2011).
|
Table 3. Initial production indicators for project farms |
||||||||
|
Farm |
Type of |
Population |
% |
FCR |
BW |
Age |
PI |
|
|
1 |
Contract |
13,000 |
96 |
1.71 |
1.74 |
35 |
290 |
|
|
2 |
Independent |
5,500 |
95 |
1.68 |
2.07 |
41 |
286 |
|
|
3 |
Contract |
7,500 |
96 |
1.63 |
1.55 |
31 |
296 |
|
|
4 |
Contract |
10,000 |
97 |
1.66 |
1.85 |
34 |
313 |
|
|
5 |
Independent |
3,500 |
92 |
1.60 |
2.03 |
38 |
310 |
|
|
6 |
Contract |
4,000 |
94 |
1.56 |
1.71 |
33 |
312 |
|
|
7 |
Contract |
9,000 |
96 |
1.64 |
1.71 |
35 |
293 |
|
|
Average |
7,500 |
95 |
1.64 |
1.81 |
35 |
300 |
||
During the pre-intervention period, production data from four control farms was also collected to represent farms that had no contact with the project. All control farms are contract farms and the production data were elicited directly from the contract company.
|
Table 4. Initial production indicators of control farms |
||||||||
|
Farm |
Type of |
Population |
% |
FCR |
BW |
Age |
IP |
|
|
C1 |
Contract |
4,000 |
95 |
1.60 |
2.26 |
34 |
391 |
|
|
C2 |
Contract |
8,000 |
93 |
1.74 |
2.05 |
36 |
304 |
|
|
C3 |
Contract |
3,500 |
96 |
1.59 |
2.14 |
35 |
372 |
|
|
C4 |
Contract |
7,000 |
96 |
1.61 |
1.80 |
33 |
324 |
|
|
Average |
5,625 |
95 |
1.63 |
2.08 |
35 |
352 |
||
On average, control farms performed better than the participating farms. They had the same liveability rates but with slightly better FCR. What stands out is that the average weight at sale is larger than in the participating farms at the same average age. This implies that the control farms had better growth rate/daily weight gain, this leads to a better PI. What will be important to compare is the change in these indicators after the interventions on participating farms.
During the pre-intervention and intervention phases, financial data were collected to estimate the gross margins (GM) of the project farms. The economic analysis was undertaken using fixed prices for all farm inputs and outputs. This minimizes the impact of external market and seasonal factors that are external to the farm. Other operational costs are fixed using the average cost from all project farms (Table 5).
|
Table 5. Generic costs used in calculating the Gross Margins (USD/1,000 birds) |
|
|
DOC Cost |
342 |
|
Feed cost |
1,412 |
|
Litter Cost |
13 |
|
Heating Cost |
37 |
|
Medicine, Vaccine and Chemical Cost |
36 |
|
Labour Cost |
36 |
|
Other Cost |
5 |
The variations in GM (Table 6) are, therefore, due to productivity and efficiency variations rather than input and output price variations. This study used a partial budget approach to estimate the economic impact of the interventions, where changes in FCR and liveability influence total feed cost and total income.
|
Table 6. Gross margin estimates for project farms (USD per 1,000 chicken per cycle) |
||||||||
|
Farm No. |
1 |
2 |
3 |
4 |
5 |
6 |
7 |
AVG |
|
Total income |
1,999 |
2,377 |
1,777 |
2,120 |
2,327 |
1,967 |
1,963 |
2,076 |
|
Total cost |
1,571 |
2,233 |
1,745 |
2,018 |
2,111 |
1,817 |
1,886 |
1,912 |
|
feed cost |
1,503 |
1,765 |
1,277 |
1,550 |
1,643 |
1,349 |
1,418 |
1,500 |
|
total other costs |
468 |
468 |
468 |
468 |
468 |
468 |
468 |
468 |
|
Gross Margin |
28 |
144 |
32 |
102 |
216 |
150 |
77 |
107 |
|
Return (%) |
1.3 |
6.1 |
1.8 |
4.8 |
9.3 |
7.6 |
3.9 |
5.0 |
Live bird sales are the main revenue source. Farmers receive higher revenue by selling larger chickens. Four farms (2, 4, 5 and 6) had higher GMs as they have higher average body weight at sale and below average FCR. On average, the percentage return is around 5% per cycle, which is relatively small considering this business is high risk. In most cases contract companies are prepared to share the risk if the farmers manage their chickens in accordance with the agreed standards and rules.
Recommended interventions for each farm were based on observations and assessment of each farm during the pre-intervention phase. They were also influenced by the willingness of each farmer to implement the set of interventions. Table 7 lists the range of interventions considered by each farm and indicates how many of the seven farmers agreed to implement these interventions. Table 7 also identifies those farms that did implement the agreed changes. For example, of the seven farmers who agreed to improve the cleaning and disinfection of the sheds only 71% of them carried through with their intention.
|
Table 7. Group of agreed interventions by participating farms level of implementation |
||
|
List of interventions |
No. of farms |
No. of farms |
|
Biosecurity |
||
|
Cleaning the shed and equipment after harvest with clean water, detergent, and disinfection. Cleaning netting and automatic drinkers, dedicated equipment for collecting/removing manure |
7 |
5 |
|
Vermin control (rats, flies, other poultry). Prevent entry of other animals into shed such (wild birds, village chickens, nomadic ducks). Using rat bait and keeping farm area clear of refuse |
3 |
2 |
|
Hygiene, sanitation, tidiness of farm environment (grass, bush, stagnant water). Cleaning and clearing area surrounding sheds, ensuring no feed is lying around, good drainage to minimise puddles of overflowing rainwater. |
5 |
3 |
|
Availability of infrastructure, equipment and procedures for people entering farm/shed. Provision of hand-washers, foot washers/foot-dipping, dedicated shed footwear, all-in/all-out procedures, restricted traffic of people, goods/vehicles into and around the farm |
7 |
4 |
|
Biosecurity signs and warnings. Install warning and agreed procedure signs at the farm gate and shed door. |
6 |
3 |
|
Position of the warehouse, parking area etc. Establishment of dirty, clean and transition zones and movement procedures. |
6 |
2 |
|
Installation of fences, gates and locks to restrict the movement of people and animals onto and within the farm. |
4 |
1 |
|
Management |
||
|
Ensure a stable temperature. Use of curtains and placement of heaters, especially for DOC |
7 |
6 |
|
Manure management. Frequent removal of manure, minimize risk of water pooling and add lime (if necessary) to help manure drying. |
2 |
2 |
|
Water testing/treatment/handling. Chlorine to neutralize contamination. Aerator in the water tank and water filtered at the chicken shed. Routine water testing (at least annually). |
6 |
4 |
|
Adequacy of feeders and drinkers. Ensure feeders and drinkers are at correct height (type and height should correspond to age of the chicken). Ensure sufficient feeders and drinkers for all chickens. |
3 |
2 |
|
Density and grading/grouping. Ensure appropriate chicken density in the shed. The use of barriers may ensure smaller chickens are not being dominated by larger birds when attempting to access feed and water |
4 |
3 |
|
Record keeping (production, financial, harvest and treatment). File harvest notes, all costs, and monitor productivity |
4 |
3 |
|
Lighting evenly distributed so that chickens don’t cluster and compete for feed and water |
3 |
2 |
|
Litter management. Ensure litter is 5 cm deep, of excellent quality and regularly replaced |
7 |
5 |
|
Monitoring of performance. Ensure the first 24-hour and 7-day targets are achieved. Monitor feed and water intake particularly in the first 6, 8 and 24 hours. Weigh regularly. |
7 |
4 |
|
Ensure appropriate vaccination programs |
2 |
1 |
|
Handling of the chicken. Remove dead birds to minimise risk of disease. Disinfect dead chickens before removal or composting |
1 |
0 |
As well as the information regarding the adoption of the agreed interventions, farmers and farms were also re-evaluated to assess the changes made on their farms. Table 8 provides the Farm Biosecurity Scores of the participating farms after the interventions were implemented. These can be compared to the baseline profiles as shown in Table 2.
|
Table 8. Farm Biosecurity Scores for broiler farms post-intervention (max of 100) |
||||||||
|
Farm No. |
1 |
2 |
3 |
4 |
5 |
6 |
7 |
AVG |
|
Biosecurity |
61 |
43 |
50 |
82 |
82 |
35 |
67 |
60 |
|
Operational |
84 |
37 |
63 |
89 |
63 |
58 |
84 |
68 |
|
Structural |
38 |
50 |
38 |
75 |
100 |
13 |
50 |
52 |
|
Management |
90 |
66 |
78 |
86 |
80 |
74 |
87 |
80 |
|
Water |
88 |
63 |
63 |
88 |
63 |
63 |
88 |
74 |
|
Brooding |
100 |
76 |
100 |
95 |
95 |
95 |
100 |
94 |
|
Growing |
90 |
70 |
100 |
80 |
70 |
70 |
80 |
80 |
|
Farm |
100 |
82 |
73 |
91 |
82 |
82 |
91 |
86 |
|
Feed |
70 |
70 |
90 |
70 |
100 |
80 |
70 |
79 |
|
Disease |
91 |
36 |
45 |
91 |
73 |
55 |
91 |
69 |
Table 9 shows the change in the FBS of the participating broiler farms between the pre-intervention and intervention phases. Farms 4 and 5 are two farms with significant improvements. Farm 4 improved both structural and operational biosecurity by building fences, gates and a parking lot, changing clothes and footwear before entering the farm, and using soap for cleaning. Farm 5 focussed on improving structural biosecurity. Farm 7 also improved operational biosecurity on the farm. On most farms, there were more improvements in operational rather than structural biosecurity as operational biosecurity required lower capital investment.
|
Table 9. Changes in technical profile of broiler farms pre- and post-intervention (%) |
||||||||
|
Farm No. |
1 |
2 |
3 |
4 |
5 |
6 |
7 |
AVG |
|
Biosecurity |
21 |
20 |
34 |
55 |
50 |
27 |
39 |
35 |
|
Operational |
42 |
16 |
42 |
47 |
37 |
42 |
53 |
40 |
|
Structural |
0 |
25 |
25 |
63 |
63 |
13 |
25 |
31 |
|
Management |
20 |
8 |
16 |
16 |
34 |
11 |
35 |
20 |
|
Water |
38 |
13 |
13 |
13 |
38 |
13 |
63 |
27 |
|
Brooding |
38 |
14 |
24 |
19 |
33 |
24 |
38 |
27 |
|
Grower |
30 |
0 |
30 |
30 |
40 |
20 |
20 |
24 |
|
Farm |
18 |
36 |
27 |
0 |
36 |
0 |
36 |
22 |
|
Feed |
-20 |
10 |
30 |
0 |
50 |
0 |
0 |
10 |
|
Disease |
18 |
-27 |
-27 |
36 |
9 |
9 |
55 |
10 |
Farms 5 and 7 were also farms that showed significant improvements in management. Farm 5 improved grower management, general farm management and feed and feeding management, while Farm 7 focussed on improving brooding management, general farm management and disease management. On average, feed and feeding management and disease management did not change significantly and even declined on some farms. Most farms still need to improve their feed management by investing in a warehouse to store their feed. With regard to disease management, there still needs to be improvements in medicine and antibiotics use.
Table 10 presents the production performance results for the project farms after implementation and monitoring of interventions. On average, the performance of the project farms was good with average PI for the contract farms above 300.
|
Table 10. Production indicators on project farms in the intervention phase |
||||||||
|
Farm No. |
Type of |
Population |
% |
FCR |
BW |
Age |
IP |
|
|
1 |
contract |
13,000 |
97 |
1.61 |
1.93 |
37 |
314 |
|
|
2 |
contract |
7,500 |
97 |
1.55 |
1.52 |
31 |
303 |
|
|
3 |
contract |
10,000 |
94 |
1.62 |
1.66 |
33 |
296 |
|
|
4 |
contract |
4,000 |
94 |
1.59 |
1.57 |
33 |
289 |
|
|
5 |
contract |
9,000 |
97 |
1.52 |
1.78 |
32 |
351 |
|
|
6 |
Independent |
3,500 |
89 |
1.77 |
1.60 |
36 |
226 |
|
|
7 |
Independent |
5,500 |
94 |
1.58 |
2.01 |
37 |
320 |
|
|
Avg Contract |
8,700 |
96 |
1.58 |
1.69 |
33 |
310 |
||
|
Avg Independent |
4,500 |
91 |
1.68 |
1.81 |
37 |
273 |
||
|
Average |
7,500 |
94 |
1.61 |
1.72 |
34 |
300 |
||
The average performance of the two independent farms are quite different and potentially reflects the ability of independent farmers to make their own decisions. Farm 6 has inferior performance due to a lower liveability rate and a higher FCR. While the control farms have better production performance with generally higher PIs than the participating farms (Table 11). However, when compared to the production performance changes in pre-intervention and intervention phases, there was a significant increase in performance for project farms, and there was a decrease in performance for control farms.
|
Table 11. Production indicators for control farms in the intervention phase |
||||||||
|
Farm |
Type of |
Population |
% |
FCR |
BW |
Age |
IP |
|
|
C1 |
Contract |
4,000 |
95 |
1.64 |
2.22 |
35 |
372 |
|
|
C2 |
Contract |
8,000 |
94 |
1.78 |
2.17 |
37 |
309 |
|
|
C3 |
Contract |
3,500 |
96 |
1.63 |
2.22 |
36 |
367 |
|
|
C4 |
Contract |
7,000 |
93 |
1.67 |
1.75 |
33 |
299 |
|
|
Average |
5,625 |
95 |
1.68 |
2.13 |
35 |
343 |
||
Table 12 presents a summary of the production differences between the project farms and the control farms in the pre-intervention and intervention phases. Contract farms are only included in this analysis in order to minimize potential bias due to possible differences in the quality of inputs such as DOC and feed supplied to contract and independent farms. On average, the project farms have improved in the intervention phase as shown by an increased PI and a lower FCR. Feed efficiency of project farms has improved by 3.6 percent which means there will be savings from decreased feed costs per unit output. In the same period, the control farms show on average a lower PI and FCR. The FCR only slightly increased by 0.04. Difference in difference (DID) analysis indicated that project farms had significantly better FCRs than the control farms post intervention.
|
Table 12. Summary of production performance changes in pre-intervention and intervention phases |
||||||||
|
Indicator |
Project Farm |
Control Farm |
Difference in Difference |
|||||
|
Pre-intervention |
Intervention |
Pre-intervention |
Intervention |
|||||
|
%Live |
95.9 |
95.9 |
95.0 |
94.8 |
0.2 |
|||
|
FCR |
1.65 |
1.57 |
1.63 |
1.68 |
-0.12 ** |
|||
|
BW (kg) |
1.71 |
1.71 |
2.08 |
2.13 |
-0.05 |
|||
|
Age(days) |
33.7 |
33.2 |
34.7 |
35.3 |
-1.1 |
|||
|
IP |
299 |
317 |
352 |
343 |
26 |
|||
| ** significant at 5% level | ||||||||
Table 13 presents the GMs for project farms after intervention and monitoring. This shows the production performance changes and their influence on farm income. The calculations are based on the same input and output prices for each farm to eliminate the effect of market price changes. On average, the project farms improved their incomes. The GMs range from USD -68 to USD 227 per thousand chickens with an average of USD 122 per thousand birds. The lowest and highest GMs are from the independent farms. The returns on investment increased from the pre-intervention period with a return on investment of 6.1% post-intervention compared to 5% at the start of the project.
|
Table 13. GM estimates for project farms post-intervention (USD/’000 chickens) |
||||||||
|
Farm No. |
1 |
2 |
3 |
4 |
5 |
6 |
7 |
AVG |
|
Total Sales |
2,221 |
2,304 |
1,750 |
1,900 |
1,843 |
1,800 |
2,044 |
2,001 |
|
Feed Cost |
1,579 |
1,609 |
1,199 |
1,354 |
1,438 |
1,264 |
1,371 |
1,412 |
|
Gross Margin |
175 |
227 |
83 |
79 |
-64 |
68 |
204 |
122 |
|
Return (%) |
7.9% |
9.8% |
4.8% |
4.1% |
-3.5% |
3.8% |
10.0% |
6.1% |
Table 14 presents the impact of production performance changes on farm income. On average, the project farms have better returns in the post-intervention period compared to the pre-intervention period, while the control farms experienced decreasing GMs. The sales revenue of the project farms are the same in the two periods due to a stable average weight of chickens at harvest. However, improved feed efficiency resulted in a lower feed cost. While for the control farms, even though the total sales increased by almost USD 0.06/chicken, the feed cost also increased by USD 0.09/chicken due to a higher FCR.
|
Table 14. Differences between pre-intervention and intervention economic returns |
||||
|
Project Farms |
Control Farms |
|||
|
Pre |
Post |
Pre |
Post |
|
|
% Live |
95.9 |
95.9 |
95.0 |
94.8 |
|
FCR |
1.65 |
1.57 |
1.63 |
1.68 |
|
Avg BW (Kg) |
1.71 |
1.71 |
2.08 |
2.13 |
|
Avg Age (Days) |
33.7 |
33.2 |
34.7 |
35.3 |
|
PI |
299 |
317 |
352 |
343 |
|
Live Bird Price (USD/chicken) |
1.15 |
1.15 |
1.15 |
1.15 |
|
Feed Price (USD/chicken) |
0.51 |
0.51 |
0.51 |
0.51 |
|
Total Sales (USD/chicken) |
1.96 |
1.96 |
2.39 |
2.45 |
|
DOC Cost (USD/chicken) |
0.34 |
0.34 |
0.34 |
0.34 |
|
Feed Cost (USD/chicken) |
1.42 |
1.36 |
1.72 |
1.81 |
|
Overhead (USD/chicken) |
0.13 |
0.13 |
0.13 |
0.13 |
|
Gross Margin (USD/1,000 chicken) |
78 |
122 |
204 |
171 |
To accurately measure the benefits of the interventions it is necessary to consider what would have happened without the project. This is the role of the control farms. While income has improved on the project farms, it is necessary to consider if this is simply the result of changing feed or chicken prices in the whole sector or is there the potential for it to be a result of better management and biosecurity. The benefits of the interventions are therefore, measured as the changes in project farm income less the change in income that would have occurred anyway. This is represented by the change in income on the control farms. The total impact of the intervention is:
Benefits of interventions = Δ project farms - Δ control farms
These changes are taken from Table 13 and summarised in Table 15. During the intervention period, the control farms experienced decreased income while in the same period, the project farms received increased income due to lower feed costs. Therefore, the benefits are:
Benefits of interventions = USD 44 – USD (33)
= USD 77
|
Table 15. Economic impact (USD/1000 chicken) |
||
|
Project |
Control |
|
|
Change in sales |
1 |
57 |
|
Change in feed cost |
(64) |
90 |
|
Change in GMs |
44 |
(33) |
|
Project – Control |
77 |
|
However, project farms also have the additional cost of the interventions, these were estimated on each project farm each cycle. Table 16 presents the average cost of interventions per farm per cycle. The interventions focused on small, low cost activities. On average, they cost approximately USD 2.00 per thousand chickens per cycle. So, the total benefit due to production management and biosecurity improvement in the project farms is approximately USD 75. On average the interventions adopted by broiler farmers improved the profitability of their farms.
|
Table 16. Cost of interventions per cycle (USD) |
||||||||
|
Farm No. |
1 |
3 |
4 |
6 |
7 |
AVG |
||
|
Total cost per cycle |
48 |
6 |
23 |
12 |
9 |
20 |
||
|
Cost/1,000chicken/cycle |
3.64 |
0.86 |
1.93 |
2.64 |
0.93 |
2.00 |
||
Improving farm biosecurity is one important way that the poultry industry can reduce disease incidence and improve productivity. It is important to understand, however, whether these improvements can actually improve farm profitability. While most biosecurity interventions are relatively cheap there is always the need for farmers to understand the management changes required and be prepared to implement them.
A detailed survey of small-scale commercial broiler farmers in Indonesia measured the economic benefits of implementing biosecurity improvements. The study included 7 farms who were monitored for 12 months and then adopted agreed biosecurity activities. They were then monitored for a further 12 months to measure the changes in productivity. These farms were also compared to 4 control farms, farms that were monitored for the 2-year period but were not encouraged to adopt any innovations.
By implementing these new biosecurity activities, the project farms were able to improve their FCR both within their own farms and when compared over time with the control farms. Even after investing time and money in biosecurity, the project farms increased their profitability by USD 75 per thousand birds per cycle. On an average farm of 10,000 birds with 6 cycles per year this equates to a benefit of USD 4,000 per year.
These are the on-farm benefits of improving biosecurity, there may also be unmeasured external benefits to consumers and society in general through the reduction in sick birds entering the market, improved food safety and a more efficient, or reduced, use of antibiotics. Government, integrated contract companies and local retailers need to continue to provide evidence of the internal (on-farm) and external (society) benefits of improvements in small commercial broiler farm biosecurity in order to reduce the disease risk, improve all farm profitability and provide safe food to consumers in Indonesia.
Anonymous 2018 Poultry Diseases Projection in 2019. PT Medion Indonesia
Carvalho et al 2015 Main Factors that Affect the Economic Efficiency of Broiler Breeder Production. Brazilian Journal of Poultry Science Jan-Mar 2015 edition. Retrieved January 10, 2020, from https://www.scielo.br/pdf/rbca/v17n1/1516-635X-rbca-17-01-00011.pdf
Directorate of Veterinary Public Health 2006 Guidelines of Veterinary Control Number for Animal Origin Food Business Unit. Directorate General of Livestock and Animal Health Services, Jakarta-Indonesia.
FAO 2010 Biosecurity training module for commercial poultry farms. FAO Indonesia.
FAO 2014 Biosecurity Cost-Effectiveness Study on Commercial Layer Chicken Farms in Indonesia. FAO Indonesia.
Komaladara A A S P, I Patrick and N Hoang 2016 Contract bonus systems to encourage biosecurity adoption on small-scale broiler farms in Indonesia. Animal Production Science. Retrieved January 18, 2020 from https://www.publish.csiro.au/an/AN15845
Martindah E, Nyak Ilham, Edi Basuno 2014 Biosecurity Level of Poultry Production Cluster (PPC) in West Java, Indonesia. International Journal of Poultry Science 13 (7): 408-415.
Mulder 2019 Poultry Quarterly Q4 2019. Global Animal Protein Sector Team Rabobank, the Netherland.
Poultry Indonesia 2019 Indonesia Poultry Business Projection in 2019. PT. Kharisma Satwa Media, Indonesia
Susilowati, S H, I Patrick, M Iqbal and T Jubb 2013 The characteristics of the farm and the farmer that affect the adoption of biosecurity on smallholder poultry farms in Indonesia. Livestock Research for Rural Development, volume 25, Article#5 Retrieved November 1, 2019, from http://www.lrrd.org/lrrd25/5/susi25088.htm
Wahyono, N D and Utami M M D 2018 A Review of the Poultry Meat Production Industry for Food Safety in Indonesia. Journal of Physics Conference Series 953 012125 Retrieved December 8, 2019, from https://iopscience.iop.org/article/10.1088/1742-6596/953/1/012125/pdf