Current status and future prospect of local food production in Hungary: a spatial analysis

Figures & data

Table 1. Examples of food-related indices.

Index/measure	Source	Description of policy relevance
Index for Food Relocalization	Ricketts et al. (2006)	Ranking of counties in England and Wales to reveal mismatches between production and marketing possibilities related to local food (later adapted to Scotland and Ireland)
Locavore Index	Strolling of the Heifers (NGO)	Ranking of marketing possibilities in US states (number of famers’ markets, CSAs, food hubs, etc.)
SAFA Indicator set	Jawtrusch, Schader, Stolze, Baumgart, and Niggli (2013) (FAO and FiBL)	Sustainability assessment tool on the food sector
Sustainable food system indicators	Yakovleva (2007) (DEFRA)	DEFRA developed a set of sustainable food system indicators to measure the implementation of the Food 2030 strategy, UK. Scope is UK-wide; it also seeks to set the scene within the global context—to measure the global impact of UK food production and consumption
Metrics of sustainable diets and food systems	Allen and Prosperi (2014) (Biodiversity International)	Setting out dimensions of sustainable diets
Indicators for sustainable food systems	Feenstra, Jaramillo, McGrath, and Grunnell (2005)	Measuring 63 indicators for circa 20 sustainable food system goals.
Environmentally Sensitive Shopper Index	Panzone, Wossink, and Southerton (2013)	Measuring the environmental sustainability of food consumption at the household level
Ecological footprint of food consumption	Wackernagel and Rees (1996), Deumling, Wackernagel, and Monfreda (2003), Collins and Fairchild (2007), Vetőné Mózner and Csutora (2013)	Quantifying the (food-related) environmental impacts of countries, cities, socio-economic groups or individuals
Sustainable Food Index	Societe Generale Index	Tracking the stock market performance of companies that are active in the sustainable food business
Global Food Security Index	The Economist Intelligence Unit (consultancy)	Ranking of countries based on food security.
Global Food Index	Oxfam (NGO)	Ranking of countries based on the challenges people face getting enough of the right food (overconsumption included)
Healthy Eating Index	Kennedy et al. (1995); Center for Nutrition Policy and Promotion (US)	Measuring the diet quality of the US population based on national surveys
Nutrient-Rich Food Index	Drewnowski and Fulgoni (2008)	Nutrient-profiling of different food items

Note: DEFRA, Department for Environment, Food and Rural Affairs; FAO, food and agriculture organization of the United Nations; FiBL, research institute of organic agriculture; NGO, non-governmental organization; SAFA, sustainability assessment of food and agriculture systems.

Figure 1. Conceptual framework. The FRI production sub-index of Ricketts Hein et al. (2006) is used and applied to Hungary to map the IFR_nj in the Hungarian counties and Budapest. The boxes show measures that can be interpreted in both the British and Hungarian contexts. A methodological refinement is suggested to account for the potential for local food production development (IFR_rj). The ratio of agricultural areas expresses the biophysical limits; for convenience, in the model the ratio of non-agricultural areas in county j is used. The newly introduced PIFRI shows where rural development programme measures are expected to be the most successful in the support local food production.

Figure 2. IFR_n in the Hungarian counties and the capital (Model 1). Darker colours show better performance. Source: Own compilation, based on the calculations. Prepared with Esri ArcGIS, Adobe Illustrator. No permission was needed to publish the image.

Table 2. The values of the IFR_n and the composing indicators for the Hungarian counties and Budapest (Model 1). Lower IFR_n indicates better performance.

County	Number of organic farmers^a		Number of local food producers advertising in the online local food directory^b		Number of small-scale producers^c		Number of certification schemes^d		Number of farms producing food for sale^e		IFRn
	Score	Rank	Score	Rank	Score	Rank	Score	Rank	Score	Rank
Szabolcs-Szatmár-Bereg	146	1	934	5	864	5	2	7	5082	9	27.0
Bács-Kiskun	119	2	122	20	1847	2	4	2	13 442	2	28.0
Hajdú-Bihar	112	3	542	12	1080	3	1	9	9444	3	30.0
Pest	81	6	822	7	598	9	4	2	5464	7	31.0
Jász-Nagykun-Szolnok	72	9	654	10	772	6	2	7	19 269	1	33.0
Csongrád	49	10	374	15	947	4	5	1	7651	5	35.0
Győr-Moson-Sopron	89	4	486	13	564	10	1	9	4175	11	47.0
Borsod-Abaúj-Zemplén	87	5	290	16	355	14	3	4	5375	8	47.0
Békés	80	8	234	17	2130	1	0	17	9235	4	47.0
Tolna	29	16	990	4	732	7	1	9	3079	12	48.0
Zala	25	19	1158	1	332	15	3	4	2644	13	52.0
Heves	32	13	598	11	386	13	1	9	5937	6	52.0
Veszprém	35	12	1102	2	267	17	1	9	2103	17	57.0
Baranya	40	11	178	18	536	11	3	4	2346	15	59.0
Somogy	31	15	878	6	518	12	0	17	4972	10	60.0
Vas	26	18	1046	3	274	16	1	9	2148	16	62.0
Fejér	32	13	430	14	626	8	0	17	2608	14	66.0
Budapest	81	6	166	19	227	18	1	9	345	20	72.0
Nógrád	28	17	766	8	71	20	1	9	1132	18	72.0
Komárom-Esztergom	24	20	710	9	212	19	0	17	1005	19	84.0
Sum	1218	–	12,480	–	13,338	–	34	–	10,7456	–	–

Note: Bold values indicates outcomes of the calculations (IFR_n), and sum of the composing indicators' scores.

^aSource: HU-ÖKO-01 és HU-ÖKO-02 (2013).

^bSource: ‘Termelőtől.hu' Ltd. (2013).

^cSource: Land Information System (2011).

^dSource: Hungarian Intellectual Property Office (2013).

^eSource: General Agricultural Census (2010).

Figure 3. The ratio of agricultural areas in the Hungarian counties and Budapest. Darker colours show better biophysical conditions for agricultural production. Country average: 57%. Source: own compilation, based on the data of the Central Statistical Office. Data refer to 2010. Prepared with Esri ArcGIS, Adobe Illustrator.

Table 3. The results of the tests on normal distributions

	IFRn	IFRn	Ratio of agricultural areas
Shapiro–Wilk p-value	0.49025	0.96195	0.97000
Shapiro–Francia p-value	0.63687	0.95176	0.99117
Kurtosis test p-value	0.7027	0.7016	0.9178

Figure 4. IFR_r in the capital and counties of Hungary (Model 2). The index reflects the expected speed of local food production development. Darker colours show better performance (faster development). Source: Own compilation, based on the calculations. Prepared with Esri ArcGIS, Adobe Illustrator.

Table 4. The values of the IFR_r and the composing indicators for the Hungarian counties and Budapest (Model 2). Lower IFR_r indicates better performance.

County	Ratio of organic farmers^a		Ratio of local food producers advertising in the online local food directory^b		Ratio of small-scale producers^c		Number of certification schemes^d		Ratio of farms producing food for sale^e		IFRr
	Ratio	Rank	Ratio	Rank	Ratio	Rank	Score	Rank	Ratio	Rank
Budapest	5.19	1	10.64	1	14.55	1	1	9	22.12	7	19.0
Szabolcs-Szatmár-Bereg	0.55	2	3.54	8	3.27	5	2	7	19.24	9	31.0
Győr-Moson-Sopron	0.50	3	2.73	10	3.17	6	1	9	23.46	6	34.0
Bács-Kiskun	0.22	7	0.22	20	3.39	4	4	2	24.66	4	37.0
Heves	0.18	12	3.36	9	2.17	11	1	9	33.34	1	42.0
Csongrád	0.15	14	1.16	15	2.94	7	5	1	23.76	5	42.0
Tolna	0.14	16	4.88	6	3.61	3	1	9	15.16	10	44.0
Hajdú-Bihar	0.24	5	1.18	14	2.36	10	1	9	20.64	8	46.0
Békés	0.21	8	0.63	19	5.71	2	0	17	24.77	3	49.0
Veszprém	0.20	9	6.36	4	1.54	15	1	9	12.14	13	50.0
Nógrád	0.26	4	7.11	2	0.66	20	1	9	10.50	18	53.0
Vas	0.15	15	6.07	5	1.59	13	1	9	12.46	12	54.0
Baranya	0.19	11	0.84	17	2.53	9	3	4	11.08	16	57.0
Komárom-Esztergom	0.22	6	6.57	3	1.96	12	0	17	9.31	20	58.0
Pest	0.17	13	1.77	13	1.29	16	4	2	11.79	15	59.0
Jász-Nagykun-Szolnok	0.10	18	0.92	16	1.08	18	2	7	26.96	2	61.0
Borsod-Abaúj-Zemplén	0.20	10	0.65	18	0.80	19	3	4	12.12	14	65.0
Zala	0.10	19	4.45	7	1.28	17	3	4	10.17	19	66.0
Fejér	0.13	17	1.80	12	2.63	8	0	17	10.95	17	71.0
Somogy	0.09	20	2.65	11	1.56	14	0	17	15.00	11	73.0
Sum	–	–	–	–	–	–	34	–	–	–	–

Note: Bold values indicates outcomes of the calculations (IFR_r), and sum of the composing indicators' scores.

^aSource: HU-ÖKO-01 és HU-ÖKO-02 (2013).

^bSource: ‘Termelőtől.hu' Ltd. (2013).

^cSource: Land Information System (2011).

^dSource: Hungarian Intellectual Property Office (2013).

^eSource: General Agricultural Census (2010). Data were compared to the number of agricultural businesses (General Agricultural Census, 2010).

Table 5. Comparison of Model 1 and Model 2 and the values of PIFRI. PIFRI shows where an intervening policy measure is expected to be the most efficient, when food production is considered.

County	IFRn		IFRr		RNAA		PIFRI
	Score	Rank	Score	Rank	Score	Rank	Score	Rank
Szabolcs-Szatmár-Bereg	27.0	1	31.0	2	35.5	5	93.5	1
Hajdú-Bihar	30.0	3	46.0	8	27.7	2	103.7	2
Bács-Kiskun	28.0	2	37.0	4	40.7	8	105.7	3
Csongrád	35.0	6	42.0	5	34.2	4	111.2	4
Békés	47.0	9	49.0	9	23.1	1	119.1	5
Győr-Moson-Sopron	47.0	7	34.0	3	38.7	7	119.7	6
Tolna	48.0	10	44.0	7	30.8	3	122.8	7
Jász-Nagykun-Szolnok	33.0	5	61.0	16	41.5	10	135.5	8
Pest	31.0	4	59.0	15	46.9	12	136.9	9
Heves	52.0	12	42.0	6	47.1	13	141.1	10
Budapest	72.0	18	19.0	1	62.5	19	153.5	11
Baranya	59.0	14	57.0	13	41.1	9	157.1	12
Veszprém	57.0	13	50.0	10	56.2	17	163.2	13
Vas	62.0	16	54.0	12	48.5	14	164.5	14
Borsod-Abaúj-Zemplén	47.0	8	65.0	17	53.9	16	165.9	15
Fejér	66.0	17	71.0	19	38.2	6	175.2	16
Zala	52.0	11	66.0	18	57.6	18	175.6	17
Somogy	60.0	15	73.0	20	48.8	15	181.8	18
Komárom-Esztergom	84.0	20	58.0	14	45.3	11	187.3	19
Nógrád	72.0	19	53.0	11	64.1	20	189.1	20
Pearson correlation p-value	0.3660				–		–

Note: Calculation is based on the ratio of agricultural areas (Central Statistical Office, 2010).

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