Targeting the soil quality and soil health concepts when aiming for the United Nations Sustainable Development Goals and the EU Green Deal

The concepts of soil quality and soil health are widely used as soils receive more attention in the worldwide policy arena. So far, however, the distinction between the two concepts is unclear, and operational procedures for measurement are still being developed. A proposal is made to focus soil health on actual soil conditions, as determined by a limited set of indicators that reflect favourable rooting conditions. In addition, soil quality can express inherent soil conditions in a given soil type (genoform), reflecting the effects of past and present soil management (expressed by various phenoforms). Soils contribute to ecosystem services that, in turn, contribute to the UN Sustainable Development Goals (SDGs) and, more recently, to the EU Green Deal. Relevant soil ecosystem services are biomass production (SDG 2 – zero hunger), providing clean water (SDG 6), climate mitigation by carbon capture and reduction of greenhouse gas emissions (SDG 13 – climate action), and biodiversity preservation (SDG 15 – life on land). The use of simulation models for the soil–water–atmosphere– plant system is proposed as a quantitative and reproducible procedure to derive single values for soil health and soil quality for current and future climate conditions. Crop production parameters from the international yield gap programme are used in combination with soil-specific parameters expressing the effects of phenoforms. These procedures focus on the ecosystem service, namely biomass production. Other ecosystem services are determined by soil-specific management and are to be based on experiences obtained in similar soils elsewhere or by new research. A case study, covering three Italian soil series, illustrates the application of the proposed concepts, showing that soil types (soil series) acted significantly differently to the effects of management and also in terms of their reaction to climate change.

distinction of a threshold frequency value above which the particular indicator exceeds a critical environmental threshold 62 value, sometimes defined by environmental laws and regulations. In their reporting red, orange, yellow and green colours are 63 used to indicate whether or not this occurs. A red label indicates that a given threshold is exceeded and that action is needed, 64 possibly to be based on favourable management experiences obtained elsewhere in soils of the same texture class or by new 65 research. This is attractive because it can directly result in management advice. In an example presented by Moebius values. Soils with such properties are clearly unhealthy. Otherwise, roots require: (i) temperatures that allow growth; (ii) soil 74 structure that allows easy accessibility of the entire soil volume, allowing roots to reach their genetically determined depth; 75 (iii) adequate water, air and nutrient availability during the growing season; (iv) adequate infiltration rates of water at the soil 76 surface; and (v) adequate organic matter content and the associated biological activity that is essential for many soil functions, 77 including nutrient uptake by plants. These five parameters can be measured at a given time and place and the reports by 78 Moebius Clune, (2017) and USDA (2019) contain detailed descriptions of measurement methods. 79 Parameters to be measured at a given point in time should have a semi-permanent character to be diagnostic. Temperature and 80 nutrient status are quite variable, the latter high at the moment of fertilization and increasingly lower as the crop adsorbs 81 nutrients. Of course, this is different in nature areas where inherent nutrient contents are important to allow particular types of 82 vegetation to develop. However, nutrient deficiencies in agricultural soils can be rapidly corrected by fertilization and the 83 nutrient status, though essential for root growth, is therefore less suitable as a parameter in agricultural soils. Soil structure, 84 excluding a limited period after soil tillage, is more permanent and governs infiltration rates and soil water and air regimes as 85 a function of weather conditions and groundwater dynamics. Soil structure is therefore suitable as a parameter. Aggregate 86 1.4 Simulating the soil-water-atmosphere-plant system to obtain a single soil health value 128 Application of simulation models of the soil-water-atmosphere-plant system can integrate the values of the parameters 129 mentioned above as they function as input data for the model, producing a single, integrated value for biomass production. 130 Many operational models are available (e.g., Reynolds et al., 2018 ; SWAP by Kroes et al., 2017; SWAP-WOFOST by Hack-131   ten Broeke et al., 2019; ICASA by White et al., 2013; APSIM by Holzworth et al., 2018; Ma et al., 2012 and others). These 132 models use rooting depth, weather data and when the required hydraulic conductivity and moisture retention data are not 133 available, these values can be estimated with pedotransfer functions using texture (as defined by the soil type), % organic 134 matter and bulk density as input data, the soil health parameters identified above (Bouma, 1989;Van Looy et al., 2017). So 135 rather than have sets of separate parameters for soil health, an integrated expression is obtained by the model that directly 136 addresses a key soil function, which is its contribution to the ecosystem service "biomass production". The term "contribution" 137 needs to be emphasized as "biomass production" is not determined by soils alone but by many other factors and, certainly, by 138 management. Applying modelling, an alternative procedure to define soil health was proposed by Bonfante et al. (2019) where 139 biomass production forms the starting point. Following the agronomic Yield Gap program (van Ittersum et al., 2013) yields 140 are calculated by simulation models of the soil-water-atmosphere-plant system: Yp = potential production determined for a 141 representative crop considering radiation and temperature regimes in a given climate region, assuming that adequate water and 142 nutrients are available and pest and diseases do not occur. This is a science-based value that applies everywhere on earth and 143 yields unique, quantitative and reproducible data. Yw is the water-limited yield, as Yp, but expressing the effect of the actual 144 soil water regime under local conditions, and Ya is the actual yield. The yield gap is Yw-Ya. These parameters of the Yield-145 gap program can be applied to define soil health and soil quality parameters to be discussed in the next section but need to be 146 modified to express the specific impact of the soil. 147 Simulation modelling offers the possibility to express soil functioning, as mentioned in the definition of soil health, by an 148 interdisciplinary modelling effort with input by agronomists, hydrologists and climatologists, each providing basic data for the 149 models. This yields one number, based on an interdisciplinary analysis, which is preferable to a series of separate numbers for 150 soil parameters only as in the US systems. The soil science discipline presents the parameters, mentioned above, to the 151 interdisciplinary research team in the context of a well defined soil type that defines moisture regimes and rooting patterns. 152 This way, the soil type functions as a "carrier of information" or a "class-pedotransfer function" (Bouma, 1989). 153 Moreover, and more importantly, modelling is the only option to explore possible future effects of climate change on soil 154 health and soil quality, as will be demonstrated below. Procedures to define single soil health and soil quality parameters will 155 be presented in the materials and methods section of the paper. 156

Targeting soil health and soil quality towards the SDGs and the Green Deal by focusing on ecosystem services 157
The discussion of soil health and soil quality so far focused on the soil and the way it functions, mentioning goals such as (soil health). As mentioned in the introduction, since 2015, 193 countries have made a United Nations-initiated commitment 160 to reach seventeen Sustainable Development Goals (SDGs). The European Union launched its Green Deal in 2019. The soil 161 quality and soil health concepts are no meaningful goals by themselves and can obtain societal significance when linked to the 162 SDGs and the Green Deal. But there is no direct link, if only because soil management plays a key role in achieving the SDGs 163 and the goals of the Green Deal. The challenge for soil science is to explore ways in which healthy soils can contribute to 164 improving a number of key ecosystem services, that, in turn, contribute to the SDGs (e.g., Bouma, 2014;Keesstra, 2016). This 165 is important because SDGs and goals of the Green Deal are not only determined by ecosystem services but also by e.g., socio-166 economic and political factors that are beyond control by sciences studying crop growth. Attention for the SDGs and the Green 167 deal implies attention for not only biomass production (SDG 2: zero hunger) but also for other ecosystem services that relate 168 directly to environmental quality, such as the quality of ground and surface water (SDG6: clean water and sanitation), carbon 169 sequestration and reduction of greenhouse-gas emissions for climate mitigation (SDG 13: climate action) and biodiversity 170 preservation (SDG 15: life on land). That is why the following definitions of soil health and soil quality are proposed: 171 • Soil health is the actual capacity of a particular soil to function, contributing to ecosystem services 172

•
Soil quality is the inherent capacity of a particular soil to function, contributing to ecosystem services. The four ecosystem services, mentioned above, have a different character. Biomass production (SDG 2) is governed by climatic 176 conditions and soil water regimes as characterized by modelling that yields quantitative and reproducible results for Yp and 177 Yw. Management plays a key role in determining Ya, and the other ecosystem services and is characteristically different for 178 different soil types. Clean water (SDG 6) can e.g., be obtained by precision fertilization, minimizing nutrient leaching to the 179 groundwater, while combatting erosion can minimize surface water pollution. But there are, in contrast to Yp or Yw values 180 for biomass production, no theoretical reference values for this ecosystem service, only threshold values of water quality by 181 environmental laws and regulations. This also applies to carbon sequestration and reduction of greenhouse gas emissions (SDG 182 13) and to life on land (SDG 15) for which as yet no environmental laws have been introduced. Different soils in different 183 climate zones will offer different challenges and opportunities to be met by appropriate management. described by the potential evapotranspiration ETp, irrigation and daily precipitation. Potential evapotranspiration was then 193 partitioned into potential evaporation and potential transpiration according to the LAI evolution, following the approach of 194 Ritchie (1972). The water uptake and actual transpiration were modeled according to Feddes et al., (1978), where the actual 195 transpiration declines from its potential value through the parameter varying between 0 and 1 according to the soil water 196 potential. 197 198

Soil Health and Soil Quality indicators 199
Application of the soil-water-atmosphere-plant simulation model and the yield-gap parameters results in four characteristics: 200 (i) a measure for actual soil health of a given soil type in a given climate zone at a given time by the SH index: 201 where Yw-phenoform expresses Yw for a given phenoform and Yw-ref represents the undisturbed soil phenoform. This 203 index expresses the effect of the soil on the measured yield Ya, a value that is affected by many other factors than the soil; 204

An Italian case study 218
Six prominent Italian soil series were analysed to illustrate the proposed method to define soil health and soil quality. Because 219 of space constraints results of three soils will be discussed in this paper. The modeling process and the background of the IPCC 220 scenarios have been presented elsewhere (Bonfante et al., 2019, 2020; Bonfante and Bouma, 2015) and will be summarized 221 below. 222 The maize was simulated from May (emergence) to the end of August (harvest) with a peak of leaf area index (LAI) of 5.8 m 2 223 m -2 . Finally, the above ground biomass (AGB) to determine the yield values (Yw) was estimated using the normalized water 224 productivity concept (WP; 33 g m -2 for maize; Steduto et al., 2012). 225 The simulation runs were performed for six selected soils using a future climate scenario of a site of southern Italy (Destra 226 Sele plain), where half of the analysed soils occur. The future climate scenarios were obtained by using the high resolution 227

Soil characteristics
loam to loamy sand and organic matter contents in Ap horizons are relatively low, ranging from 1.4 to 2.6%, justifying runs 256 for hypothetical contents of 4%. Based on field observations, the rooting depth of maize was estimated to be 80 cm, implying 257 that not the only Ap horizon but also subsoil horizons contribute to the water supply to maize. 258 The soil hydraulic properties applied in the simulation runs, water retention, θ(h), and hydraulic conductivity, k(θ), curves 259 were measured in the laboratory. Undisturbed soil samples (volume ≈ 750 ml) were collected from all of the recognized 260 horizons of the six soil profiles. Samples were slowly saturated from the bottom and the saturated hydraulic conductivity 261 measured by a falling head permeameter (Reynolds et al., 2002). Then, both couples of θ-h and k-θ data were obtained by 262 means of the evaporation method (Arya, 2002)  Results will therefore only be presented for phenoforms showing effects of erosion and the plowpan and for increaed %OM, 276 as mentioned above. 277

Water-limited yields (Yw) 278
Water-limited yields (Yw) for four climate periods and three phenoforms for each soil are shown in Figure 1a  to the 2070 -2100 climate scenario, particularly for climate scenarios beyond 2040, but due to relatively high standard 281 deviations, not all differences are significant. However, each soil shows significant drops of Yw for the erosion and plowpan

Soil health values for different climate periods 287
The SH index applies to soil health parameter measurements for a given soil at a given time, defining actual conditions with 288 reference to the particular production potential of the soil type that is present as expressed by Yw calculated with optimal soil 289 parameters as discussed above. Yw-phenoform conveys conditions, expressed by the three soil parameters observed at the site. 290 When Yw-phenoform is equal to Yw, the soil health value will be 100, but this is highly unprobable. Lower values indicate 291 room for improvement but offer no information as to factors that lead to these low values (see next section). Calculated SH 292 indexes for three Italian soil series in four climate periods are reported in Table 2. In this study, four soil conditions were 293 simulated that are common in the field, considering four climate periods: a non-degraded soil characterized by optimal soil 294 parameters (producing Yw-ref), and two Yw-phenoform values: erosion of topsoil, formation of a plowpan, and an increase 295 to 4% OM. As actual conditions are discussed here, the current climate of 2010-2040 should be considered. Erosion reduces 296 SH to appr. 88, while the plowpan has much stronger effect with significantly different values of 55 (soil P4), 66 (P5), and 75 297 (P6). Increasing % OM does not deviate from the value of 100, which corresponds with data reported in Figures 1, 2, and 3. 298 To determine the health index at a given time and place in a given soil, the three soil parameters discussed above are measured 299 and the model is used to calculate a (Yw-phenoform) value that is next compared with the Yw-ref value calculated with optimal 300 soil parameneter values for that particular soil. Management practices should be documented that have resulted in the Yw-301 phenoform being considered. 302

Soil quality (SQp) in terms of characteristic ranges of soil health values 303
The SH index, mentioned in the previous section, characterizes soil health at a given time and location, as measured in a can be seen as a measure for inherent soil quality (SQp). Figure 2 shows a range of values obtained for a given soil type 308 assuming, in this case, the occurrence of only three phenoforms. This only illustrates a principle and many observations in the 309 field can and should extend the number of points for Yw-phenoform. This range offers a point of reference for each 310 observation, as discussed in the previous section, and allows conclusions as to advisable management procedures associated 311 with the different phenoforms that, together, determine the observed ranges in Figure 2. 312 Figure 2 shows a decreasing sensitivity for soil degradation moving from soil P4 to soil P6. Soil health ratios change from 56 313 (P4), 66 (P5) to 78 (P6). The effects of climate change on the index are, again, strongest for soil P4. Figure 2 shows that not 314 only the ranges of the health index are significantly different for the three soils but also their resilience to climate change. A 315 particular soil health measurement in a given soil, as described in the previous section, can now be placed into the bar shown 316 in Figure 2 indicating possible room for improvement. As every measurement is combined with an assessment of soil use and 317 management that has resulted in the particular phenoform being observed, the system allows the generation of useful 318 management information for the land user. 319

Comparing different soils in a given region (SQr). 320
So far, particular soil types have been considered. The analysis can be extended to all soils in a given region and climate zone 321 and this comparison of different soils can be valuable for regional land use planning. This requires the definition of Yp for the 322 area that is used for the simulations. For the Italian soils being considered Yp=18 tons ha -1 and this value is maintained for all 323 climate scenarios considered, implicitly assuming that other factors affecting biomass production will not change. Table 3  324 shows significant differences among the soils providing a valuable quantitative assessment. Differences are maintained when 325 different climate periods are considered. Soil P4 scores again the lowest values, with soil P5 intermediate and soil P6 with the 326 highest values but even this soil has a low score of 50 for the last climate period when a plowpan is present. 327

How to assess soil quality in a global context? (SQw). 328
Questions about potential food production in future, considering the effects of climate change require a mechanism to compare 329 different soils in the world in their capacity to produce biomass. Assuming a maximum production to be achieved in the world 330 (Ymax) considering theoretical photosynthesis under particular climate conditions, values of Yp and Yw can be expressed as 331 a function of Ymax. Use of Yw will produce the most realistic values in view of the limited water availability in many areas 332 of the world. Areas with relatively high values have a higher potential than areas with low values and this analysis can be 333 helpful input from soil science contributing to global food production scenarios. Based on current evaluations, a Ymax of 20 334 tons ha -1 is used here as a reference and this results in SQw values that can also be expressed for various phenoforms, showing 335 effects of different forms of degradation Table 4. As in Table 3, differences between the three soils are significant. How these 336 values are to be judged will depend on comparable values to be assembled for other areas of the world. 337

Discussion 338
The Soil Health concept, as defined in the literature and as modified in this study, is inadequate to allow a comparison of the 339 capacity of different soils to function. Two soils may be healthy in their own way, but a healthy clay soil has a significantly 340 different "capacity to function" as compared with a healthy sandy soil. As discussed, the soil quality concept can be based on 341 the range of soil health values observed within a given soil type, thus allowing the distinction of differences among different 342 soil types and effects of management. Rather than separate soils in very broad textural classes we advocate use of specific soil 343 types as "carriers of information"( "pedotransferfunctions") ( van Looy et al, 2017, Bouma, 2020). Still, the soil health concept 344 is relevant and suitable to express the actual condition of a given soil by comparing Yw-phenoform with Yw-ref as discussed 345 in this paper, producing a soil health index SH following a procedure that is applied to all soils in the same way.
Of course, Yw assumes real soil water regimes and well fertilized conditions without pests and diseases. Most often, real yields 347 (Ya) are lower than Yw and reasons will have to be investigated to select proper soil management. Clearly, within fields 348 different soils often occur and this will call for precision techniques. This aspect is, however, beyond the scope of this paper. 349 The advantage of the quantitative procedure to assess SH and SQ is its basis in a quantitative and reproducible scientific 350 analysis of the plant production process as a function of soil moisture regimes, made possible by applying soil-water-351 atmosphere-plant simulation models. Yw-ref and Yw-phenoform reflect the impact of soil conditions on Ya, the measured 352 yield, as water and nutrients are assumed to be optimal and pests and diseases do not occur. Observing the difference between 353 Ya on the one hand and Yw-phenoform and Yw-ref on the other can result in fruitful interaction between soil scientists and 354 agronomists applying a common language as an effective means of communication. 355 When applied to three Italian soils, defined by soil classification in terms of three soil series (genoforms), a range of values is 356 obtained not only for an undisturbed soil but also for soils affected by poor forms of soil management resulting in erosion and 357 compaction (two "phenoforms"), and a third phenoform following "good" management increasing % OM. All of these 358 phenoforms still maintain their genoform classification (Bouma, 1989;Rossiter and Bouma, 2018). In this study effects of 359 only three hypothetical phenoforms were explored. In future, field work is required to distinguish a number of characteristic 360 phenoforms for every genoform, as a function of current and past soil management. Existing soil maps can be used to identify 361 sampling spots (e.g., Pulleman et al., 2000;Sonneveld et al., 2002). The link of soil health and soil quality with primary production allows a direct link with economic aspects (e.g., Priori et al.,376 2019) while consideration of other ecosystem services allows consideration of environmental aspects associated with 377 production. ecosystem services that, in turn, contribute to SDGs and the Green Deal. Soils function in an interdisciplinary context and the 381 implicit hypothesis of soil health assumes that healthy soils will make better contributions to ecosystem services than unhealthy 382 ones and soils with low quality in a regional and world context. But a healthy soil can still make a poor contribution to 383 ecosystem services when poorly managed, illustrating the overriding importance of the management factor. 384 Application of soil-water-atmosphere-plant models is focused on the ecosystem service: "biomass or primary production". 385 However, at the same time, other services have to be provided as well as discussed earlier: water quality protection, reduction 386 of greenhouse gas emissions, carbon capture and biodiversity preservation. Here, applying appropriate management is crucial 387 and, in contrast to the calculations of biomass production, there is no underlying basic theory to identify options. That is why 388 defining a characteristic range of soil health values for any given soil types a measure for inherent soil quality (SQp) is 389 important to link the land user with experiences obtained elsewhere on similar soils in the same climate zone.