Assessing biodiversity potential of arable farms – A conceptual approach

Abstract

Biodiversity loss is a global problem, with agriculture being a major driver. Every agricultural operation, including management, has an impact on biodiversity because it interferes with nature. It is challenging to assess these impacts. Correspondingly, it can be difficult to support farmers to work in a more biodiversity-friendly way. This paper presents a conceptual framework for farmers to predictively assess their biodiversity potential and compare it over several years. On the one hand, parameters at field level (“on-crop”) are taken into account and, on the other hand, the landscape level (“off-crop”) with corresponding parameters is also included. The simple application and the easy integration in field record systems through data already collected by the farmer allows widespread use.In conclusion, the framework is a recommendation for biodiversity assessment. It should be further developed and validated so that new scientific findings can be incorporated into the assessment of biodiversity in order to be able to calculate and predict it even more accurately.

Keywords:

• Biodiversity assessment
• agricultural management indicator
• landscape indicator
• sustainable agriculture
• expert interviews
• logistic growth

PUBLIC INTEREST STATEMENT

The decline of biodiversity is becoming a global focus and agriculture is seen as a major driver. To counteract this, measures need to be implemented in agriculture for which assessment models suitable for mass usage are required. In the presented approach, an indicator-based and site-specific assessment method for biodiversity potential in arable farms is presented. This methodology could be used for controlling in public funding, labelling, legal restrictions or marketing purposes.

1. Introduction

The loss of biodiversity is progressing worldwide and, next to climate change, is the greatest ecological threat caused by humans (Mantyka-Pringle et al., 2015; Rounsevell et al., 2020). Numerous studies indicate the loss of biodiversity (Gascon et al., 2015; Maier et al., 2019; Tscharntke et al., 2021). In recent years there has been increased writing and reporting on this topic as the importance of biodiversity has become clearer and policy makers have recognized the need for action (IPBES, 2020; Moonen & Bàrberi, 2008). In this context, the importance and loss of biodiversity has been repeatedly emphasized, as it is now known that the extinction of species has serious consequences for humans and the environment (Halliday et al., 2020; Hough, 2014; Johnson et al., 2017), such as lower agricultural productivity (Roe, 2019; Wright et al., 2017) and lower resilience to climate variability (Mijatović et al., 2013; Sidibé et al., 2018; Tasser et al., 2019). The extinction of species has a dramatic impact on the environment, as ecosystems are thrown out of balance and habitats are affected, increasing the likelihood of zoonoses, for example (Allen et al., 2017; Cardinale et al., 2012; Genung et al., 2020; Hough, 2014; Keesing & Ostfeld, 2021; Schmeller et al., 2020).

There is scientific consensus on determinants of biodiversity in agriculture (Grass et al., 2019, Hochkirch et al., 2020; Targetti et al., 2012; Tscharntke et al., 2021). Agriculture is the main driver of biodiversity decline, as it affects habitats through its land use (Erisman et al., 2016; Schmeller & Bridgewater, 2016). Agricultural food production is increasingly responsible for these species declines due to intensification to enhance yields (Batáry et al., 2017; Tscharntke et al., 2021; Vidaller & Dutoit, 2022). The use of fertilizers and pesticides, the cultivation of monocultures and land consolidation, which has further reduced heterogeneity in agricultural farming, all have a negative impact on species diversity (Bennett et al., 2021; Erisman et al., 2016). Farm specialization also emphasizes this trend. Crop rotations are shortening, for example, due to the use of energy crops (Steinmann & Dobers, 2013), which affects heterogeneity and thus biodiversity (Degani et al., 2019). For farmers, economic aspects are in the foreground, especially through intensification to increase crop yields in order to be able to survive on the market in the long term and to be able to supply the growing number of people (Henle et al., 2008; Hirsch et al., 2022; Scotti et al., 2015). For biodiversity services, there is no remuneration, so the focus in agriculture is mainly on yields (Nijkamp et al., 2008).

Socially and politically, the issue of biodiversity loss and modern agriculture’s role is gaining momentum (X. Li, 2020). It can be assumed that farmers will have to apply evidence of particularly biodiversity-friendly management practices in the foreseeable future because of yield declines in the long term or whether through legal requirements, public subsidies or marketing practices such as labelling (Clough et al., 2020; Dudley & Alexander, 2017; Tscharntke et al., 2014). For this reason, assessment systems for the biodiversity potential of agriculture have to be developed that are simple to use and prospectively show the biodiversity potential.

This paper presents a framework helping farmers to assess their biodiversity potential based on the field activities carried out throughout the year and their surrounding conditions. The additional effort for the farmers should be as low as possible and easy usability is necessary for this. As an exact observation and recording of biodiversity for every farm is practically impossible at large scale, the main focus of this framework is to capture the main parameters influencing biodiversity and how the farmer´s activities affect those. Put simply the results rate the farmer`s effort to promote biodiversity potential and cannot show a measurement of biodiversity itself. This framework with its literature-based parameters and weightings has been validated by expert interviews.

Most of the essential parameters of this concept can be recorded using field records, saving farmers additional effort. Thus, the assessment for biodiversity is done in the background without additional effort. Only for the off-crop area data entries are required once. After that, the biodiversity potential can be calculated individually for each field.

The aim of this work and the proposed framework is to enable farmers to assess their biodiversity potential.

In the second chapter, method description, the conceptual approach for the assessment of the biodiversity potential is presented and the exact procedure is explained. It describes how the logistic growth function is used to evaluate the parameters and how the final result is composed.

Chapter three describes a practical example showing how the biodiversity potential is calculated. The exact procedure is explained systematically and step by step. First, all parameters have to be evaluated with points, then weighted and added up to be able to calculate the biodiversity potential at the end.

In the discussion, the framework presented in the paper is put into context with other approaches to biodiversity assessment. The innovations from this work are described and it is shown how the framework can be used in practice.

The conclusion emphasizes the necessity of taking biodiversity into account in arable farming in the future and describes the need for further research.

2. Method description

It is known from literature that both crop management practices as well as the environment of the field, such as landscape elements that contribute to greater heterogeneity, have a major impact on biodiversity in agriculture (Martin et al., 2020; Tscharntke et al., 2021; Tuanmu & Jetz, 2015). Therefore, in order to assess biodiversity holistically, this framework captures the agricultural management indicator (on-crop) and the landscape indicator (off-crop). Both indicators are based on different parameters which are assessed every cropping year. The on-crop indicator takes into account as parameters all field activities that the farmer carries out and actively manages throughout the year on a certain field. The off-crop indicator, on the other hand, takes into account parameters that affect the surroundings of the fields, which the farmer cannot influence through normal field work. As all parameters differ in their contribution to biodiversity promotion, this framework uses a weighting multiplier for each parameter. With this weighting the contribution to biodiversity can be assessed. The higher the contribution of a parameter, the higher the score and vice versa.

The on-crop and off-crop parameters were selected from literature (see Tables 1 and 3) and subsequently, expert interviews were conducted in order to supplement additional parameters on the one hand and to weight them regarding their contribution to biodiversity promotion on the other hand. In nine interviews with experts from the fields of agriculture, agricultural and landscape ecology, biology and field ornithology each parameter was discussed and those with a higher effect on biodiversity were weighted twice, while the other parameters were given single weighting.

Table 1. Categorization of the biodiversity potential score

very low biodiversity potential	low biodiversity potential	medium biodiversity potential	high biodiversity potential	very high biodiversity potential
0.0–0.2	0.2–0.4	0.4–0.6	0.6–0.8	0.8–1.0

Table 2. On-crop parameters for biodiversity potential. Parameters with * are weighted twice by the experts

On-crop (indicator for agricultural management) composed of the following parameters	Abbreviation	Literature	Selection options	Points
1. Soil cultivation before sowing	onM1	Honermeier (2006)	no-till not turning (ploughless) turning (with plough)	1.0 0.5 0
2. Chemical and mechanical crop protection (a) Fungicide* (b) Herbicide* (c) Insecticide* (d) Plant growth regulator (e) Hoe for weed control/Harrow or curry comb	onM2 onM3 onM4 onM5 onM6	Yoon et al. (2013) Böcker et al. (2019) Mahmood et al. (2016) Aktar et al. (2009) Machleb et al. (2020); Alba et al. (2020)	number of applications number of applications yes/no yes/no no crossings 1–4 crossings 5 or more crossings	no applications = 1 less than prescribed applications = 0.5 prescribed applications = 0 no = 1 yes = 0 1 0.5 0
3. Chemical seed treatment	onM7	Sekulic and Rempel (2016)	yes/no	no = 1 yes = 0
4. FertilizationChemical-synthetic or organic* Chemical-synthetic or organic*	onM8	Mozumder and Berrens (2007); Török et al. (2021)	only organic organic and chemical-synthetic combined chemical-synthetic	1.0 0.5 0
5. Intercropping*	onM9	Mala et al. (2020)	yes/no	yes =1 no = 0
6. Undersowing	onM10	Boetzl et al. (2022)	yes/no	yes = 1 no = 0
7. Elements of crop rotation*	onM11	Venter et al. (2016)	7 or more 5–6 4 or less	1.0 0.5 0
8. Use of precision farming	onM12	Garske et al. (2021); Zanin et al. (2022); Faupel et al. (2023)	yes/no	yes = 1 no = 0

Table 3. Off-crop parameters for biodiversity potential. Parameters with * are weighted twice by the experts

Off-crop (landscape indicator) composed of the following parameters	Abbreviation	Literature	Selection options	Points
1. Mean field size*	offM1	Landis (2017)	<2 ha between 2 ha and 5 ha >5 ha	1.0 0.5 0
2. Cultures of neighbouring fields	offM2	Tscharntke et al. (2021)	4 or more 2 to 3 less than 2	1.0 0.5 0
3. Longitudinal elements*	offM3	Marja et al. (2018)	>2/3 field and field margins >1/3 to <2/3 field and field margins <1/3 field and field margins	1.0 0.5 0
4. Hedges/flower strips in or at the edge of the field	offM4	von Königslöw et al. (2021); Tschumi, Albrecht, Collatz, et al. (2016)	hedges/flower strips in or around the field hedges/flower strips sporadically in or around the field no hedges/flower strips	1.0 0.5 0
5. Surface watercourse	offM5	Dyson and Yocom (2015)	yes/no	yes = 1 no = 0
6. Isolated trees in the field	offM6	Prevedello et al. (2018)	3 or more trees in the field per ha 1 to 2 trees in the field per ha no trees in the field	1.0 0.5 0
7. Sealed area (street, residential area)	offM7	Tobias et al. (2018)	<1/3 sealing around the field between 1/3 and 2/3 sealing around the field >2/3 sealing around the field	1.0 0.5 0

In the appendix, a detailed description for the on-crop and off-crop parameter assessment is given. First, the general procedure of awarding points is described there. After that, the selection of each parameter is explained in a short paragraph, which is substantiated by literature. Thus, the appendix is a comprehensive supplement for Tables 2 and 3.

This semi-quantitative framework does not aim to measure individual species, as is the case for example with Sandvik et al. (2013), Cardoso et al. (2008), Butler et al. (2009) or Plaisance et al. (2009), but rather assesses in general terms the contribution that farmers are doing to biodiversity in their daily work.

As biodiversity promotion cannot grow into infinity, this framework is based on logistic growth. Figure 1 shows how the individual parameters are evaluated in terms of their effectiveness on biodiversity potential and how the logistic growth curve runs. A similar approach was taken by Wood et al. (2015), who used comparable curves to assess the potential of ecosystem services and agrobiodiversity of agro-ecosystems. However, this approach was in a much more theoretical manner. Multiple parameters benefit from marginal benefit and receive more points until a certain saturation point is reached. After this point is reached, the effect on biodiversity growth decreases again. Double-weighted parameters are allowed to go two steps on the respective curve, as the effect of the parameter on biodiversity is higher. When all parameters have been evaluated as in Tables 2 and 3, the weighting multiplier is applied on each parameter for on-crop and off-crop. Then the points achieved for both indicators are summed up, resulting in a score that is the x-value in the logistic growth curve. According to this value, the final score is charted on the y-axis.

Figure 1. Logistic growth logic for assessing biodiversity potential.

Notes: Parameters = Cumulative parameter effectiveness weighting for on-crop and off-crop

p = Biodiversity potential score

For the calculation logistic growth differential equation $f\left(x\right)=m\ast \frac{a\ast S}{a+\left(S-a\right)\ast e^{-S\ast k\ast x}}$ is used, with “a” being the starting value at the value x = 0, “S” being the saturation limit, “k” being the growth constant and “m” being the result multiplier. To assess biodiversity potential for arable farming, the formula is parametrized as following:

$a=0.1$$S=30$$k=0.014$$m=0.033$

To categorize biodiversity potential, a rating is carried out at the end of the assessment to provide farmers with an overview. The assessment ends with the result of this categorization. A total of five categories are identified. Tzoulas and James (2010), Chase et al. (2018) and May et al. (2018) use a similar categorization (See Table 1).

Erisman et al. (2016) identified a growing biodiversity potential with an increasing amount of implemented parameters. In addition, this study illustrates that more biodiversity in a habitat results in a higher stability of the entire ecosystem. This fact applies to both on-crop and off-crop parameters (Geertsema et al., 2016; Hass et al., 2018; Rosa-Schleich et al., 2019). If an ecosystem counts more species, this has an overall more positive impact on biodiversity, as more species can migrate to other systems (Roe, 2019). In general, and in simplified terms, the more parameters are taken for biodiversity, the higher the effect, because there is more heterogeneity in the ecosystem. It follows that the marginal benefit increases with each additional biodiversity-friendly parameter. However, this additional, disproportionate effect of a further parameter is not unlimited, otherwise it would end in exponential growth, which would not be true as biodiversity cannot grow into infinity. After a certain number of parameters, the marginal benefit decreases again. And again, the decline is weaker at the beginning. Or put differently and more generally: Marginal benefit increases with each additional biodiversity enhancing parameter until the turning point and then decreases again. The evaluation of biodiversity on the basis of logistic growth was also part of the expert interviews. The majority were in favor of this framework rather than the linear curve where each parameter has the same gradient. As this framework is based on the biodiversity promoting effects of the applied parameters and not on a count of species itself, there has to be done no observation of biodiversity itself as a starting point beforehand. The framework mainly aims at the aspect of visualizing the effects of each applied parameter by the farmer and his potential to increase his site- and field-specific biodiversity.

2.1. Parameters that are included in the calculation

Table 2 shows a selection of individual parameters for the on-crop area while Table 3 shows a selection of parameters for the off-crop area. All selected parameters are backed up with literature. For each parameter, a score between zero and one point is awarded. A full point is assigned when the parameter is fulfilled in the most biodiversity potential promoting way. Half a point is awarded for moderately biodiversity enhancing influences on parameters. No point is given when the influences of the parameters are harmful to biodiversity. Since not every parameter has the same impact on biodiversity, individual parameters with high relevance for biodiversity were weighted twice by the experts. More detailed information on the individual parameters, their rating categories and weightings can be found in the appendix. Based on literature, it is explained why the parameters shown there were selected and why they contribute decisively to biodiversity. It should be noted that the selection of parameters and their evaluations serve as a reference point for arable farms in North-West Europe. These can be adapted and evaluated regionally and sector-specifically to achieve more accuracy.

The parameters consist of two-thirds on-crop parameters and one-third off-crop parameters. Since this framework is intended to assess biodiversity potential, all parameters end up being considered together. Biodiversity is to be considered holistically and should therefore not be subdivided into an on-crop indicator and an off-crop indicator. This approach was also discussed with the experts. As farmers can influence the on-crop area in contrast to the off-crop parameters, the higher weighting is justified.

3. Procedure of the framework shown on a practical example on a single field for one year

In the previous section, the theoretical basis of this approach was presented. In order to illustrate the practicability of this framework and its application, an example wheat field from southern Germany was chosen to show how biodiversity assessment is carried out by applying the method described. Table 4 shows the assessments of the parameters for the on-crop area, Table 5 shows the assessments for the off-crop area. For the on-crop parameter, the farmers can use data of their field records. Thus, no additional effort by the farmer is necessary. For the off-crop parameter additional information is required, which is not part of the field record data, but which can be easily provided by the farmer.

Table 4. Assessment of the parameters on-crop. Parameters with * are weighted twice by the experts

On-crop agricultural management indicator	Evaluation of the parameter for the award of points	Parameters assessed	Weighted points
1. Soil cultivation before sowing (onM1)	no-till	1	1
2. Chemical and mechanical crop protection
(a)Fungicide (onM2)*	1 application	0.5*	1
(b)Herbicide (onM3)*	3 applications	0*	0
(c)Insecticide (onM4)*	no application	1*	2
(d)Plant growth regulator (onM5)	yes	0	0
(e)Hoe for weed control/Harrow or curry comb (onM6)	no crossings	1	1
3. Chemical seed treatment (onM7)	yes	0	0
4. FertilizationChemical-synthetic or organic (onM8)* Chemical-synthetic or organic (onM8)*	chemical-synthetic	0*	0
5. Intercropping (onM9)*	yes	1*	2
6. Undersowing (onM10)	no	0	0
7. Elements of crop rotation (onM11)*	7	1*	2
8. Use of precision farming (onM12)	yes	1	1
Parameters and points in summary		5.5/12	10/18

Table 5. Assessment of the parameters off-crop. Parameters with * are weighted twice by the experts

Off-crop landscape indicator	Evaluation of the parameter for the award of points	Parameters assessed	Weighted points
1. Mean field size (offM1)*	1.5 ha	1*	2
2. Cultures of neighbouring fields (offM2)	less than 2	0	0
3. Longitudinal elements (offM3)*	>1/3 and < 2/3 field and field margins	0.5*	1
4. Hedges/flower strips in or at the edge of the field (offM4)	no hedges/flower strips	0	0
5. Surface watercourse (offM5)	yes	1	1
6. Isolated trees in the field (offM6)	no	0	0
7. Sealed area (street, residential area) (offM7)	<1/3 sealing around the field	1	1
Parameters and points in summary		3.5/7	5/9

The following chapter shows in detail how the parameters in the example field are composed. The approach aims at an automated calculation, which will be done in the future by a FMIS (Farm Management Information System). Using data from the farmers’ field documentation, the program calculates the biodiversity potential in parallel and prospectively in the background with the information provided. Farmers can then estimate how they can influence biodiversity potential even before the growing season.

To understand the procedure, an example is used below to show how this calculation looks in detail. This very detailed example is intended to illustrate what the program will calculate in the background in the future and how the biodiversity potential of each individual field will ultimately be computed.

Biodiversity potential for the agricultural management indicator (on-crop)

Biodiversity potential for the landscape indicator (off-crop)

The following calculations have to be carried out during the scoring process. All the points achieved, both on-crop and off-crop, are added up so that the total number of points is reached at the end. After adding up the points achieved and determining the final number of points, the logistic growth curve is read off, which in the end gives the total result in one value.

Table 6 gives an overview of the points achieved in the example. Figure 2 compares the results with the best-case scenario for biodiversity potential and shows what potential for biodiversity was achieved in the example field.

Figure 2. Results of the biodiversity application example for on-crop and off-crop.

Table 6. Result overview of the practical example

	Number of possible points = highest score for biodiversity potential	Actual points achieved	y-axis logistic growth function value
On-crop	18	10
Off-crop	9	5
Total	27	15	0.65

In the practical example, the farmer achieves a value of 0.65 on his wheat field. This value consists of 18 on-crop and 9 off-crop parameters. Out of a total of 27 parameters, the farmer has achieved 15. According to the calculation formula used for x = 15, the result corresponds to the biodiversity potential value of 0.65. Thus, based on Table 1, the farmer achieves a high biodiversity potential. In addition, easy availability of data and practicability of the framework was confirmed by an interviewed farmer.

$f\left(15\right)=\frac{1}{30}\ast \frac{3}{0.1+29.9\ast e^{-0.42\ast 15}}=0.65$

4. Discussion

Biodiversity loss is progressing worldwide (Maier et al., 2019), with intensive agricultural land use considered the most important factor in biodiversity decline (Gabel et al., 2018). Although biodiversity assessment methods have already been developed, these approaches often focus on the whole farm rather than the individual field (Berbeć et al., 2018; Birrer et al., 2014; Gottwald & Stein-Bachinger, 2018; Świtek et al., 2019). In addition, monitoring of different species or expert knowledge is often required, as in the approach of Tasser et al. (2019), the approach of Chaplin et al. (2021) or even that of Elmiger et al. (2023). Furthermore, the increased effect on biodiversity due to the combination of parameters is not taken into account.

In comparison to other approaches to biodiversity assessment, the framework presented here was different. Firstly, a distinction is made between on-crop and off-crop and biodiversity is assessed for each field individually. In addition, the parameters derived from the literature have been weighted by experts, so that the influence of parameters with a particular impact on biodiversity are weighted twice. Furthermore, the evaluation of the parameters is based on the logistic growth curve. This possibility of evaluation was also discussed in the expert interviews. The majority agreed with this framework, as more parameters that promote biodiversity should also be given greater consideration up to a certain point. This new framework rewards through the marginal benefit effect when multiple points are achieved that are beneficial for biodiversity and shows the farmer in advance what his potential will be in the season.

Extensive and organic farming are more beneficial for biodiversity (Chamorro et al., 2016; Stein-Bachinger et al., 2021; Tsvetkov et al., 2018). The positive effect of these types of farming on biodiversity is also illustrated by the framework presented here. Organic farming avoids both chemical-synthetic pesticides and chemical-synthetic fertilizers and thus achieves full marks in four parameters. In addition, these parameters are weighted twice. Although yields are significantly lower, which in turn represents a conflict of objectives in general (Henle et al., 2008), the framework presented here is only intended to take biodiversity into account while a supplement of economic parameters could be conceivable in future projects.

Innovations of this conceptual framework need to be validated in a further step. Moreover, currently only whole and half points are awarded for parameters which limit granularity. Furthermore, there is a turning point in the logistic growth curve. The gradient is highest here, which means that parameters around the inflection point have the greatest effect on biodiversity. Currently the curve parameters are arbitrary and need to be validated in terms of evaluation results. What has not yet been researched are the mutual interactions of parameters on biodiversity. In this framework, points are awarded for parameters. However, it is currently not clear how parameters interact with each other. When parameters are implemented, ecosystems are affected to different degrees. This interaction also exists in reverse. More research is needed to better understand the interactions.

The framework presented provides the possibility to integrate its algorithms into a FMIS, so that the biodiversity assessment runs in the background without farmers having to do additional work with the input. This would be possible for all on-crop parameters. For off-crop parameters, the workload for the farmer would be slightly higher, but many of these parameters can be collected once and then used again each year.

5. Conclusion

This paper proposes a framework that enables the assessment of impacts on biodiversity due to farm based land management. This is relevant as agriculture is the main cause of biodiversity loss. Politically and socially, the issue of biodiversity is becoming increasingly important, and it is to be expected that farmers will be financially compensated in the near future if certain parameters for more biodiversity are implemented. The framework presented in this paper is intended to be a viable utilization tool for practicing farmers. After evaluating this framework, it could also be implemented in an automated software that has access to the farmer’s field records, which would further reduce the effort required from the farmer. Parameters not available in field records can be provided by the farmer without involving external experts and observations to reach wide practicability. When all parameters are available, a biodiversity potential metric is provided. The results of the individual fields can be compared over the years, so conclusions could be drawn about how increased biodiversity potential affects yields and profitability of the individual fields in the future. The advantage of this field-specific framework is that the assessment of biodiversity is site- and parameter-dependent, which allows comparability of the individual fields and field-specific elaboration of the parameters. Through the field-specific evaluation, it may also be conceivable to market products of the farmer with a high contribution to biodiversity at a higher price. Furthermore, it is the authors’ intention to further develop and adapt this framework in the future in order to be able to assess biodiversity more accurately. In the future, more research needs to be done in this area to further improve the operationalization of biodiversity in agri-food production. It is known which parameters have a particularly positive or negative impact on biodiversity. This paper is intended to be the starting point for further research to further increase the granularity in scoring for this framework. One main innovation of the framework presented is the non-linear assessment of biodiversity which can be evaluated without monitoring of certain species groups.

Correction

This article has been corrected with minor changes. These changes do not impact the academic content of the article.

Acknowledgments

The project DiWenkLa (Digital Value Chains for a Sustainable Small-Scale Agriculture) is supported by funds of the Federal Ministry of Food and Agriculture (BMEL) based on a decision of the Parliament of the Federal Republic of Germany via the Federal Office for Agriculture and Food (BLE) under the innovation support programme (grant reference 28DE106B18). DiWenkLa is also supported by the Ministry for Food, Rural Areas and Consumer Protection Baden-Württemberg.

Disclosure statements

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the Federal Ministry of Food and Agriculture (BMEL).

Notes on contributors

Rolf Weber

Rolf Weber is currently a PhD student in the DiWenkLa project (Digital Value Chains for a Sustainable Small-Scale Agriculture). As part of his dissertation, he is working on the sustainability assessment of digital technologies in small-scale farming systems. In June 2022, he published the paper “How does the Adoption of Digital Technologies Affect the Social Sustainability of Small-scale Agriculture in South-West Germany?”. He is also researching a methodology for biodiversity assessment in agriculture. Finally, his dissertation deals with site-specific fertilization. It is about site-specific N-application in small-scale agriculture in Germany - trade-offs and synergies of ecological and economic parameters. He studied Energy and Resource Management in his Bachelor’s degree and Sustainable Agriculture and Food Management in his Master’s degree. After graduating, he worked for ifeu - Institute for Energy and Environmental Research Heidelberg for 1.5 years before starting his dissertation in June 2020.

Marius Kuhlmann

Marius Kuhlmann is a research assistant at Nuertingen-Geislingen University.

Jan Lask

Jan Lask was a PhD student at the University of Hohenheim at the time of the project.

Jürgen Braun

Jürgen Braun is a professor at Nuertingen-Geislingen University of Applied Sciences.

Markus Frank

Markus Frank is a professor at Nuertingen-Geislingen University of Applied Sciences.

Appendix

Table

1 point	0.5 point	0 points
very biodiversity promoting	moderately biodiversity promoting	not biodiversity promoting

Points are awarded on a scale of 0–1. One full point is awarded if the parameter is carried out in a way that promotes biodiversity. Half a point is awarded to farmers for parameters that moderately promote biodiversity. No point is awarded for parameters that do not promote biodiversity. The allocation of points is based on literature research on the one hand and on expert interviews on the other. The allocation was discussed with the experts and agreed upon, but still acts as an example and has to be verified.

Agricultural management indicator on-crop

The on-crop parameters can be actively controlled by the farmer. Although there are various field operations and applications that the farmer has to perform in the calendar year in order to be able to harvest a corresponding crop, the decision is directly in the farmer’s area of responsibility. Below is a list of all the different on-crop parameters, which are explained in more detail below. All these on-crop parameters have been taken from the literature. In addition the farmer can quickly and easily find out the information needed for them in the field records.

Parameter 1) Soil cultivation before sowing: onM1

Table

no-till	not turning (ploughless)	turning (with plough)
1 point	0.5 point	0 points

For seeding, there are three options: Turning, not turning and no-till (Honermeier, 2006). Generally, the more soil that is left untouched, the better the impact on biodiversity (Adl et al., 2006; Li, He, et al., 2021; Sheibani & Ahangar, 2013; van Capelle et al., 2012). For this reason, no-till gives the most biodiversity points. Half the points are given for not turning, whereas turning with a plow gives no points. Since a lot of organisms live in the soil, it is mandatory to include this area in the assessment via the seeding parameter (Karayel & Sarauskis, 2019; Souza et al., 2013).

Parameter 2) Chemical and mechanical crop protection: a) Fungicide, b) Herbicide, c) Insecticide), d) Growth regulator, e) Mechanical hoe/Harrow or curry comb

a) Fungicide onM2, b) Herbicide onM3, c) Insecticide onM4 (all double weighted)

Crop protection products (CPPs) (fungicides, herbicides and insecticides) have an enormous impact on biodiversity (Beketov et al., 2013; Brühl & Zaller, 2019; Mahmood et al., 2016; Ratnadass & Deguine, 2021). For this reason, the weighting of this parameter is increased by a factor of two for all CPPs (all experts agreed on this). The decisive factor is the quantity and number of applications of the respective crop protection product used. In the approach in this paper, the allocation of points is linked to the applications specified for the crop. No application of CPPs is best for biodiversity and is therefore rewarded with a whole biodiversity point. If the specified applications are sprayed, no points are awarded. If, on the other hand, less than the recommended applications are applied, half a point is awarded, as toxicity decreases here.

In this example, the number of applications of herbicides and fungicides are based on the cultivation guidelines for winter wheat in southern Germany (Kulturenratgeber, 2019).

As the recommended number of herbicide applications ranges from four to five and the recommended number of fungicide applications ranges from one to three, the average value was used for the rating system.

As insecticides are used as required, there is only a full point on avoidance of insecticides and no point allocation for insecticide usage.

Table

	1 point allocation	0.5 Point allocation	0 point allocation
a) Fungicides	no application	1 application	2 or more applications
b) Herbicides	no application	1–4 applications	5 or more applications
a) Insecticides	no application		1 or more applications

d) Plant growth regulator: onM5

Table

plant growth regulator no	plant growth regulator yes
1 point	0 points

Growth regulator belongs to the pesticides and have to be included due to its toxic properties (Aktar et al., 2009; Luo et al., 2019). Through their use, substances enter the environment that affect animals and plants there and have therefore to be taken into account (Fishel, 2006). If they are not used, one point is awarded for this. If, on the other hand, growth regulators are used, farmers do not receive a point in this regard.

e) Hoe for weed control/Harrow or curry comb: onM6

Table

no crossings	1–4 crossings	5 or more crossings
1 point	0.5 point	0 points

In addition to chemical pesticides, mechanical plant protection also plays a decisive role in pest control, especially in organic farming (Alba et al., 2020; Machleb et al., 2020). Here, hoes and harrows/currycombs are used. These weed control options primarily have no negative impact on biodiversity because no toxin is used. However, multiple passes on the field are often necessary to keep weeds in control. Each pass with a hoe or harrow/curry comb affects and disturbs soil life, which in turn has a negative impact on biodiversity. Therefore, the fewer passes with hoes and harrows/currycombs, the better for biodiversity. The awarding of points is regulated accordingly.

Parameter 3) Chemical seed treatment: onM7

Table

chemical seed treatment no	chemical seed treatment yes
1 point	0 point

Seed treatment has a negative impact on biodiversity and does not score biodiversity points in the calculation program when used. In this context, neonicotinoids are used which have a negative impact on insects in particular (Sekulic & Rempel, 2016). If, on the other hand, seed treatment is not used, the full number of points is awarded.

Parameter 4) Fertilization: onM8: Chemical-synthetic or organic according to Fertilizer Regulation: (double weighting)

Table

only organic	organic and chemical-synthetic combined	chemical-synthetic
1 point	0.5 point	0 points

Chemical-synthetic fertilizers (nitrogen, phosphorus, potash (NPP)) have an impact on ecosystems (Mozumder & Berrens, 2007). The application rate is crucial. The more of them that are applied, the higher the likelihood that they will leach out, polluting surface waters and groundwater (Dicks et al., 2014; Goucher et al., 2017; Hasler et al., 2015; Tayefeh et al., 2018).

Organic fertilizer closes natural cycles and has always been applied to land in agriculture to keep yields at a high level. The application of organic material improves soil life, which in turn has a positive effect on biodiversity (Török et al., 2021). If farmers adhere to the fertilizer ordinance and only apply as much as is permitted, there is a full point here.

Parameter 5) Intercropping: onM9 (double weighting)

Table

intercropping yes	intercropping no
1 point	0 points

The cultivation of an intercrop has a positive effect on biodiversity (Engbersen et al., 2021; Gentsch et al., 2020; Mala et al., 2020). The roots of the intercrop promote soil life, which in turn has a positive effect on biodiversity (Nemecek et al., 2015). In addition, this can reduce leaching because the catch crops absorb excess fertilizers. Cultivation of an intercrop gives the full number of biodiversity points. No cultivation, on the other hand, is not taken into account.

Parameter 6) Undersowing: onM10

Table

undersowing yes	undersowing no
1 point	0 points

To cover the soil in the field completely, undersowing can be used. Boetzl et al. (2022) showed that undersowing suppresses weeds and thus creates new habitats for pollinators without causing yield losses. Undersowing is taken into account in the biodiversity calculator with the full number of points. If, on the other hand, undersowing is not used, no points are awarded.

Parameter 7) Elements of crop rotation: onM11 (double weighting)

Table

7 or more	5–6	4 or less
1 point	0.5 point	0 points

A higher number of crop rotations has positive effects on soil life (Li, Guo, et al., 2021; Venter et al., 2016). Each crop has different soil requirements. Expanded crop rotation increases long-term yields and copes better with extreme weather events (Bowles et al., 2020; Jalli et al., 2021). If other crops are grown over time, this has a particularly positive effect on microbial diversity. For this reason, biodiversity points are awarded when a farmer applies seven or more elements in the crop rotation. If, on the other hand, a five or six-member crop rotation is used, half the number of points is awarded. If the crop rotation is less than four, there is no consideration in the calculation tool.

Parameter 8) Use of precision farming: onM12

The application of digital technologies has a positive impact on the environment and biodiversity. In particular, the precise application of chemical pesticides substantially reduces applied amounts, as only affected areas are treated, thereby increasing efficiency (Faupel et al., 2023; Garske et al., 2021; Zanin et al., 2022). For this reason, the use of precision farming is rewarded with one point, while no point is awarded if this technic is not used.

Table

use of precision farming yes	use of precision farming no
1 point	0 points

Landscape indicator off-crop

Parameters in the off-crop area are primarily characterized by the fact that they cannot be directly influenced by the farmer. Nevertheless, in a holistic assessment of biodiversity, these off-crop parameters have to be included in the calculations. Many of the parameters listed below only need to be recorded once because geographic conditions do not change annually. Since this calculation framework focuses on the individual field, the information has to be entered directly by the farmer. The person who farms, owns or leases the land over several years knows the land and the local conditions. It is also conceivable to find out the off-crop parameters via satellite images.

Parameter 1) Mean field size: offM1 (double weighting)

Table

<2 ha	between 2 ha and 5 ha	>5 ha
1 point	0.5 point	0 points

The size of the fields has an impact on biodiversity. The smaller the size of fields, the more heterogeneous the landscape and the better the impact on biodiversity (Gonthier et al., 2014; Landis, 2017; Tscharntke et al., 2016). Because land is often leased from farmers or has been for decades, the farmer has no control over its size. Many smaller fields in one place have a particularly positive effect on biodiversity because of the diversity present. Although farmers have no influence on this, this effect should nevertheless be taken into account in the tool. Small fields (<2 ha) get the full score (Török et al., 2021). Farms between two and five ha get half the number of points and farms >5 ha no points.

Parameter 2) Cultures of neighbouring fields: offM2

Table

4 or more	2 to 3	less than 2
1 point	0.5 point	0 points

Crops grown on neighbouring fields affect biodiversity by creating natural pathways that ensure that individual species can migrate into neighbouring ecosystems. In general, the more heterogeneous the crops of neighbouring fields, the greater the biodiversity benefits (Sirami et al., 2019; Tscharntke et al., 2021). Because management can vary due to crop rotation or changing tenure, this information is one of the few off-crop metrics that has to be obtained each year.

Parameter 3) Longitudinal elements: offM3 (double weighting)

Table

>2/3 field and field margins	>1/3 to <2/3 field and field margins	<1/3 field and field margins
1 point	0.5 point	0 points

Field margins play an overriding role in the assessment of biodiversity, because in these areas neither fertilizer nor spraying is used, i.e. no intensive agriculture is practiced. Here, many species can retreat and reproduce largely undisturbed (Marja et al., 2018). In addition, the interconnectedness of field and field margins allows species to spread across different ecosystems and occupy other habitats. For field and field edges, it depends on how long such longitudinal elements are. Often one field is adjacent to another, making such habitats unavailable. Much more frequently, only one or two sides are surrounded by such marginal strips. However, the primary concern is not the number of edge strips, but rather how long they are in total in relation to the perimeter of the field as a whole.

Parameter 4) Hedges/flower strips in or at the edge of the field: offM4

Table

hedges/flower strips in or around the field	hedges/flower strips sporadically in or around the field	no hedges/flower strips
1 point	0.5 point	0 points

The positive effect of hedges (Cardona et al., 2021; von Königslöw et al., 2021) and flower strips (Tschumi, Albrecht, Bärtschi, et al., 2016) on biodiversity is well known. The more hedges/flower strips there are on or next to the field, the more refuge opportunities there are for different species. Since hedges and flowering strips should stand for several years to achieve a certain benefit (Tschumi, Albrecht, Bärtschi, et al., 2016), this parameter belongs to the off-crop area. Although farmers themselves can plant hedges/flower strips, which will have a biodiversity-enhancing effect over the years, this effect will only be noticeable and measurable after some time. In this scenario, the planting of new hedges/flower strips is taken into account in the following calculation years.

Parameter 5) Surface watercourse: offM5

Table

surface watercourse at the edge of the field	no surface watercourse at the edge of the field
1 point	0 points

Watercourses directly adjacent to agricultural land is a good example of an off-crop parameter, as farmers have no control over where their fields are located. Since watercourses with their associated banks are considered biotopes (Dyson & Yocom, 2015), this information is of great importance for a holistic biodiversity calculation.

Parameter 6) Isolated trees in the field: offM6

Table

3 or more trees in the field per ha	1 to 2 trees in the field per ha	no trees in the field
1 point	0.5 point	0 points

Trees in the field provide year-round shelter for certain creatures, thereby increasing biodiversity in the landscape (Prevedello et al., 2018). Similar to agroforestry systems, trees have many different positive attributes especially in relation to climate change and biodiversity (Dollinger & Jose, 2018; Udawatta et al., 2019). In warm years, they shade the soil resulting in less evaporation. In addition, their root systems increase the amount of water available to crops grown with them for longer periods of time. Nitrogen is transported into the soil through the roots of the trees, which also has positive effects. Leaves that fall off and roots that die are decomposed and thus actively contribute to the build-up of humus.

Parameter 7) Sealed area (street, residential area): offM7

Table

<1/3 sealing around the field	between 1/3 and 2/3 sealing around the field	>2/3 sealing around the field
1 point	0.5 point	0 points

Whether federal roads, motorways or residential areas, sealed surfaces have a negative impact on biodiversity because they are not natural habitats (Gharehbaghi et al., 2019; Tobias et al., 2018). In addition, they are unbridgeable for many species. Roads are often heavily traveled, making crossing dangerous or impossible, and species are trapped in certain areas. In addition, there are numerous negative effects such as greenhouse gas emissions, light pollution (Challéat et al., 2021), microplastics (Padha et al., 2021) due to tire abrasion, for example and the permanent alteration of the microclimate (Stojanovic et al., 2021). This is because sealing causes the immediate area to heat up more, especially in the warmer summer months, and precipitation is also less well drained which can lead to flooding and erosion. In addition, the drainage of rain has a negative effect on the place where the water percolates, as there is more water in a shorter time, which causes problems for the soil inhabitants (e.g. earthworms that drown).