Calculation of diversification indicators and other covariates

Summary

Indicators

Indicator	Data	Format
perimeter and area of the field	RPG*	vectoriel
mean field size within buffer	RPG*	vectoriel
crop rotation (N-4:N)	RPG*	vectoriel
hedgerows around field	RPG* Liu 2023	vectoriel raster 30m
% land cover within buffer	RPG* + CLCplus	raster 10m
edge density	Not available	NA

Datasets

Registre Parcellaire Graphique (RPG): annual field crop data for the period 2015-2024 available for France on IGN website: https://cartes.gouv.fr/rechercher-une-donnee/dataset/IGNF_RPG. The dataset differentiates 366 crop categories
Geodienste - nutzungsflaechen (Nutzung): Swiss equivalent of the RPG, not entirely open yet but raw data provided by Selma Cadot: https://geodienste.ch/services/lwb_nutzungsflaechen. The dataset differentiates 146 crop categories. It also contains hedgerows and semi-natural.
Corine Land Cover plus (CLCplus): land cover at European scale with 10m resolution and 11 basic land cover classes, available on Corine Land Monitoring Service https://land.copernicus.eu/en/products/clc-backbone.
Liu et al. (2023): European tree cover and biomass map at 30m resolution for Europe https://zenodo.org/records/8154445 derived from 3m resolution PlanetScope imagery of 2019.
BD Haies v2 (6.8Gb): hedgerows dataset for France available on the IGN website: https://geoservices.ign.fr/bdhaie. BD Haie v2 was produced from satellite images of 2020-2022.
Land cover class harmonization: list all classes from RPG, Nutzung and CLCplus and how to categorize them. This file has been completed during WS3 and can be further customized for the project objectives.
Orchards and vineyards from individual projects to further complete the agricultural land use.

Field observations

Figure 1: Number of observations per year and per project

Table 1: Number of observations per year and per project

	2014	2015	2016	2017	2018	2019	2020	2021	2022	2023	2024	2025	TOTAL
Agrim2019	0	0	0	0	0	40	0	0	0	0	0	0	40
BACCHUS_OPERA	0	0	0	0	40	38	40	40	38	38	38	0	272
BIOMHE	0	0	0	0	0	0	40	0	0	0	0	0	40
BISCO	0	0	0	27	0	0	0	0	0	0	0	0	27
DIVAG	0	0	0	0	0	40	0	0	0	0	0	0	40
DURUM_MIX_GM	0	0	0	0	226	0	0	0	0	0	0	0	226
EXCLU_BVD	0	0	0	0	0	0	0	7	12	16	0	0	35
FRAMEwork_BVD	0	0	0	0	0	0	0	36	0	0	0	0	36
Herrera2026	0	0	0	0	0	0	30	35	40	0	0	0	105
lepibats	0	0	0	0	0	0	0	33	0	0	0	0	33
muesli	0	0	29	0	0	0	0	0	0	0	0	0	29
OSCAR	0	0	0	0	21	44	50	80	100	102	115	0	512
PestiRed	0	0	0	0	0	0	62	60	54	60	58	58	352
Pigot2023	0	0	0	0	0	0	0	156	144	0	0	0	300
SEBIOPAG_BVD	0	0	0	0	0	0	20	21	20	0	0	0	61
SEBIOPAG_Plaine de Dijon	20	20	20	20	20	20	20	20	20	20	20	0	220
SEBIOPAG_VcG	20	19	17	17	17	17	17	17	17	17	0	0	175
SEBIOPAG_ZAAr	0	0	0	0	20	0	0	17	0	16	0	0	53
Seree2022	0	0	0	0	0	14	19	0	0	0	0	0	33
SERIPAGE	0	0	9	0	0	0	0	0	0	0	0	0	9
TOTAL	40	39	75	64	344	213	298	522	445	269	231	58	2598

Figure 2: Map of field observations

More detailled overview of the field observations can be found here

Identification of the crop field

Because of data availability, we will only focus on the observations made in the period 2015-2024 with field coordinates. The project DURUM_MIX_GM has only one coordinates leading to the entrance of the Institut Agro-Montpellier, without registered crop field within 1500m from the coordinates so it was also excluded.

We identified the crop field from RPG or Nutzung dataset corresponding to the observations based on the coordinates and the year of the sampling.

Table 2: Number of observations in fields from RPG

	Nobs	in_RPG	in_VineOrchard	Perc
Agrim2019	40	40	0	100.00
BACCHUS_OPERA	272	1	268	98.90
BIOMHE	40	39	0	97.50
BISCO	27	26	0	96.30
DIVAG	40	40	0	100.00
EXCLU_BVD	35	0	35	100.00
FRAMEwork_BVD	36	0	36	100.00
Herrera2026	105	94	0	89.52
lepibats	33	30	0	90.91
muesli	29	29	0	100.00
OSCAR	512	448	11	89.65
PestiRed	294	267	0	90.82
Pigot2023	300	299	0	99.67
SEBIOPAG_BVD	61	0	61	100.00
SEBIOPAG_Plaine de Dijon	200	200	0	100.00
SEBIOPAG_VcG	155	152	0	98.06
SEBIOPAG_ZAAr	53	53	0	100.00
Seree2022	33	30	0	90.91
SERIPAGE	9	9	0	100.00

Remarks:

In total, 95 % of the fields observations are covered with information on crop field.
It is important to know and acknowledge that RPG is incomplete but the vineyards and orchards provided by data providers helps characterizing projects with permanent crops.

Field size

We calculated the area and the perimeter of the crop fields corresponding to the samplings.

Figure 3: Field area in ha per project. The dashed line show the median area.

Table 3: Summary statistics per project of the area (in ha) of crop fields

	Min.	1st Qu.	Median	Mean	3rd Qu.	Max.
Agrim2019	0.97	2.21	3.02	3.80	4.99	10.21
BACCHUS_OPERA	0.23	0.58	0.78	1.07	1.40	3.15
BIOMHE	0.68	1.59	3.69	4.48	6.29	13.99
BISCO	0.50	1.10	1.71	3.22	3.15	23.69
DIVAG	0.97	2.21	2.98	3.17	4.02	6.01
EXCLU_BVD	0.45	0.58	0.72	0.94	1.29	2.20
FRAMEwork_BVD	0.29	0.58	0.84	1.25	1.77	3.28
Herrera2026	0.87	4.58	7.88	8.76	11.91	37.52
lepibats	1.40	2.77	6.18	11.89	13.96	55.47
muesli	0.41	1.90	3.84	5.46	7.61	34.05
OSCAR	0.22	0.48	1.00	1.71	1.70	21.35
PestiRed	0.10	0.97	1.06	1.33	1.40	5.19
Pigot2023	2.40	7.93	10.46	12.87	17.05	44.15
SEBIOPAG_BVD	0.29	0.60	0.87	1.37	1.77	5.68
SEBIOPAG_Plaine de Dijon	0.53	5.11	6.83	7.46	8.92	17.82
SEBIOPAG_VcG	0.85	2.90	4.27	5.56	7.00	24.06
SEBIOPAG_ZAAr	1.21	3.16	4.73	4.96	6.94	16.22
Seree2022	0.90	2.60	4.81	9.17	12.85	37.52
SERIPAGE	1.55	2.19	3.51	4.04	5.19	7.60

Figure 4: Field perimeter in m per project. The dashed line show the median perimeter.

Table 4: Summary statistics per project of the perimeter (in m) of crop fields

	Min.	1st Qu.	Median	Mean	3rd Qu.	Max.
Agrim2019	485.39	691.18	797.06	889.45	1053.13	1674.45
BACCHUS_OPERA	214.18	330.40	398.22	449.45	552.24	1157.85
BIOMHE	354.03	592.12	835.73	907.71	1072.08	1807.17
BISCO	307.54	503.97	593.59	743.08	846.90	2550.45
DIVAG	485.39	654.90	799.78	816.22	937.89	1275.08
EXCLU_BVD	269.15	324.13	459.31	452.72	550.25	669.28
FRAMEwork_BVD	247.58	445.90	509.95	523.05	602.94	938.87
Herrera2026	527.24	1056.08	1423.60	1450.42	1727.66	5991.28
lepibats	488.44	824.10	1260.50	1789.15	2670.28	6828.99
muesli	257.24	608.61	939.47	1060.52	1338.17	4021.15
OSCAR	200.02	358.30	493.46	583.72	749.38	2728.54
PestiRed	299.90	454.28	513.51	550.32	607.61	1126.50
Pigot2023	707.30	1381.42	1616.08	1735.90	2089.44	4574.97
SEBIOPAG_BVD	247.58	366.24	500.45	525.22	643.19	984.37
SEBIOPAG_Plaine de Dijon	393.53	1075.19	1240.09	1297.87	1558.72	2220.91
SEBIOPAG_VcG	377.85	815.28	1171.63	1202.13	1505.30	4249.39
SEBIOPAG_ZAAr	451.97	793.52	949.02	1061.93	1207.41	3466.03
Seree2022	378.99	778.39	1272.30	1482.71	2076.89	5991.28
SERIPAGE	494.30	750.53	776.57	928.22	1227.43	1466.38

Figure 5: Relation between area and perimeter (in log scale)

There is a strong relation between area and perimeter (Figure 5). In median, field size is 2.2 ha and field perimeter is 710m.

Outliers

Figure 6: Field with small area and large perimeter

Figure 7: Field with large area

Remarks:

Some fields are defines as a strip (large perimeter, small area), e.g. Figure 6
Some fields include many sub-plots, e.g. Figure 7.

Field size within buffer

Using the coordinates of the sampling sites, we calculated the average area of all crop fields within a buffer (500m, and 1000m).

Table 5: Summary statistics of the field area (in ha) within different buffer size

	B_500m	B_1000m
Min.	0.14	0.16
1st Qu.	0.74	0.72
Median	1.32	1.22
Mean	2.42	1.99
3rd Qu.	2.95	2.40
Max.	21.98	16.35
NA’s	2.00	1.00

We see that some observations don’t have crop field within 500m (N=2). These observations (listed in Table 6) would need to be checked and ensure that they are close to agricultural lands.

Table 6: Observations with no fields within a 500m buffer.

	Study_ID	Plot_ID	Year
181	OSCAR	33_2011_00002	2018
481	OSCAR	11_2023_00004	2023

Figure 8: Correlation among field areas per buffer size

Figure 9: Average field size with buffer of 500m

Figure 10: Average field size with buffer of 1000m

Outliers

Figure 11: Highest average field size within 1000m buffer

Figure 12: Lowest average field size within 1000m buffer

Figure 13: Highest difference between 500 and 1000m buffer

Figure 14: Lowest difference between 500 and 1000m buffer

Crop rotation (N-4:N)

Figure 15: Length of the land cover time series per project.

Table 7: Number of observations per length of crop rotation data (in years) available

	0	1	2	3	4	5
Agrim2019	0	0	0	0	1	39
BACCHUS_OPERA	1	0	0	1	2	268
BIOMHE	1	0	1	0	0	38
BISCO	1	0	0	26	0	0
DIVAG	0	0	0	0	0	40
EXCLU_BVD	0	0	0	0	0	35
FRAMEwork_BVD	0	0	0	0	0	36
Herrera2026	9	0	0	0	2	94
lepibats	2	1	4	0	3	23
muesli	0	0	29	0	0	0
OSCAR	34	1	26	36	79	336
PestiRed	1	2	3	29	126	133
Pigot2023	1	0	0	0	0	299
SEBIOPAG_BVD	0	0	0	0	0	61
SEBIOPAG_Plaine de Dijon	0	20	20	20	20	120
SEBIOPAG_VcG	1	18	17	17	19	83
SEBIOPAG_ZAAr	0	0	0	0	20	33
Seree2022	3	0	0	0	0	30
SERIPAGE	0	0	9	0	0	0

The number of observations in the period N-4:N in Table 7 is a results of the number of observations per year (Table 1). Most observations have 5 years of data on crop rotation (73%, N=1668).

Let’s have a look at the crop rotations over the 5-year period (N:N-4).

Figure 16: Number of different crop cultivated in the period N:N-4. Only observations with complete information on land cover for the 5 years are included.

Figure 17: Number of different crop groups cultivated in the period N:N-4. Only observations with complete information on land cover for the 5 years are included.

Figure 18: Number of different crops cultivated in the past 5 years

Figure 19: Number of different crop groups cultivated in the past 5 years

From 1668 observations with complete crop rotation information in [N-4:N], 535 have the same crop for the whole time period, while 393 fields have four different crop groups in the past 5 years (Figure 16).

Table 8: Most populat crop group rotation cultivated in the 5 years before observations

Var1	Freq
vignes	488
vergers	132
ble_tendre_hiver,colza_hiver,orge_hiver	75
divers,vignes	26
gel_sans_prod,vignes	24
ble_tendre_hiver,prairies_temporaires	17
ble_tendre_hiver,colza_hiver,proteagineux	16
ble_tendre_hiver,mais_ensilage	14
ble_tendre_hiver,colza_hiver,orge_hiver,proteagineux	13
ble_tendre_hiver,colza_hiver,lin_non_textile,proteagineux	12
ble_tendre_hiver,colza_hiver,orge_printemps,proteagineux	12
autres_cult_indus,ble_tendre_hiver,orge_hiver	11
ble_tendre_hiver,mais_grain_semence	10
mais_grain_semence,vignes	10
ble_dur_hiver,ble_tendre_hiver,colza_hiver,orge_printemps	8
ble_tendre_hiver,colza_hiver,mais_ensilage,orge_hiver	8
autres_cult_indus,ble_tendre_hiver,mais_grain_semence,orge_hiver	7
ble_dur_hiver,ble_tendre_hiver,tournesol	7
ble_tendre_hiver,colza_hiver,fourrage_fabacees	7
ble_tendre_hiver,colza_hiver,mais_grain_semence,proteagineux	7

Remarks:

Crop rotations depends a lot on the project targeting either annual vs perrenial cropping systems.

Hedgerows length

We overlaid the field as defined in RPG* (+ a 10m buffer) with the tree cover density from Liu et al. 2023 and with other national information on hedgerows (BDHaie and Nutzung).

Figure 20: Correlation among hedgerow indicators

Figure 21: Hedgerow cover (%) per project from Liu 2023. The dashed line show the median biomass.

Figure 22: Hedgerow cover (%) per project from national database. The dashed line show the median biomass.

Table 9: Percentage of fields with some hedgerow detected per data source

	Liu	National
Agrim2019	100.00	100.00
BACCHUS_OPERA	40.07	26.10
BIOMHE	92.50	97.50
BISCO	96.30	96.30
DIVAG	92.50	95.00
EXCLU_BVD	100.00	100.00
FRAMEwork_BVD	100.00	100.00
Herrera2026	51.43	49.52
lepibats	54.55	48.48
muesli	100.00	96.55
OSCAR	58.01	47.07
PestiRed	48.64	8.16
Pigot2023	29.00	38.00
SEBIOPAG_BVD	100.00	100.00
SEBIOPAG_Plaine de Dijon	54.50	39.50
SEBIOPAG_VcG	89.03	98.06
SEBIOPAG_ZAAr	94.34	100.00
Seree2022	24.24	45.45
SERIPAGE	100.00	100.00

Outliers

Figure 23: Field with high hedgerows cover in BD Haie but not in Liu 2023

Figure 24: Field with low hedgerows cover in BD Haie but high in Liu 2023

Land cover within buffer

Table 10

	n_classes	av_perc_rpg
Agrim2019	36	86.97
BACCHUS_OPERA	32	80.12
BIOMHE	40	83.17
BISCO	30	84.17
DIVAG	38	85.93
EXCLU_BVD	39	78.54
FRAMEwork_BVD	38	76.71
Herrera2026	51	84.21
lepibats	36	71.63
muesli	36	82.44
OSCAR	51	76.44
PestiRed	40	67.67
Pigot2023	47	85.27
SEBIOPAG_BVD	42	80.80
SEBIOPAG_Plaine de Dijon	44	75.77
SEBIOPAG_VcG	44	83.94
SEBIOPAG_ZAAr	45	85.57
Seree2022	42	83.82
SERIPAGE	28	87.14

After grouping, there are 54 different categories covered by the 1000m buffers (Table 10).

In average, roughly 70% of the buffer areas are filled with land cover classes from RPG (and the other half are CLC classes). The proportion of RPG classes vary greatly by project.

Table 11: Top 10 most frequent land cover classes

                  frac1000_forest                   frac1000_vignes 
                            15.36                             15.25 
        frac1000_ble_tendre_hiver     frac1000_permanent.herbaceous 
                            10.63                              9.45 
    frac1000_prairies_permanentes                   frac1000_sealed 
                             5.51                              5.36 
             frac1000_colza_hiver     frac1000_prairies_temporaires 
                             3.45                              3.40 
frac1000_low.growing.woody.plants               frac1000_orge_hiver 
                             3.33                              3.12

Figure 25: Average land cover per project with buffer of 1000m

Figure 26: Average forest cover per project with buffer of 1000m

Figure 27: Average sealed cover per project with buffer of 1000m

Figure 28: Average vineyard cover per project with buffer of 1000m

Figure 29: Average wheat cover per project with buffer of 1000m

Figure 30: Shannon diversity in land cover buffer of 1000m. The dashed line show the median area.

Table 12: Summary statistics per project of the Shannon diversity in land cover buffer of 1000m

	Min.	1st Qu.	Median	Mean	3rd Qu.	Max.
Agrim2019	2.02	2.27	2.33	2.34	2.41	2.63
BACCHUS_OPERA	0.56	1.07	1.16	1.15	1.32	1.73
BIOMHE	2.07	2.28	2.35	2.35	2.44	2.61
BISCO	1.99	2.12	2.21	2.19	2.27	2.35
DIVAG	2.22	2.30	2.36	2.39	2.48	2.63
EXCLU_BVD	1.14	1.50	1.66	1.69	1.84	2.24
FRAMEwork_BVD	1.27	1.70	1.87	1.84	1.99	2.16
Herrera2026	1.25	2.09	2.32	2.28	2.49	2.83
lepibats	1.10	1.54	1.88	1.78	2.05	2.35
muesli	1.76	2.22	2.30	2.27	2.39	2.62
OSCAR	0.96	1.42	1.74	1.75	2.02	2.84
PestiRed	1.14	2.05	2.29	2.23	2.51	2.84
Pigot2023	1.29	1.98	2.09	2.14	2.30	2.85
SEBIOPAG_BVD	1.23	1.74	1.87	1.87	2.00	2.25
SEBIOPAG_Plaine de Dijon	1.02	1.99	2.21	2.13	2.34	2.78
SEBIOPAG_VcG	1.86	2.25	2.37	2.35	2.45	2.66
SEBIOPAG_ZAAr	1.97	2.24	2.34	2.34	2.45	2.70
Seree2022	1.73	1.96	2.14	2.17	2.33	2.71
SERIPAGE	1.88	2.16	2.18	2.13	2.19	2.28

Remarks:

All buffers include some amount of forest (5-20%) and sealed area (5-10%), while the crop composition is highly project specific.

Summary and questions about metrics

Figure 31: Correlation among field diversity metrics

Remarks:

Most observations have a corresponding crop field in RPG dataset or in complementary dataset (Table 2).
The sampling location within the field might influence the results (different impact of hedgerows, or of agricultural practices). We might want to add an indicator reflecting the distance to the center of the field and/or the distance to the closest field boundary?