Legacy of Intensive Agricultural History in the Health of (Sub)Tropical Landscapes


 Soil health conceptualized as a measurable ecosystem property provides a powerful tool for monitoring progress in restoration projects or implementation of best management practices to promote sustainable agroecosystems. We surveyed soils collected from a range of land uses (i.e., protected native and non-native forest, managed pasture, unmanaged previously intensive agricultural lands, organic cropland, and conventional cropland) across a range of soil orders (Oxisol, Mollisol, Andisol, Inceptisol, and Vertisol) on three Hawaiian Islands. Forty-six metrics associated with soil health and encompassing biological, chemical, and physical properties were measured. In this multivariate survey, the most distinct group was the unmanaged, previously intensive agriculture lands, which was significantly different from all other land uses regardless of mineralogy. Importantly, the soil health of well-managed pastures in Hawaiʻi was not different from protected forests, suggesting that well-managed grazing lands may be as healthy and resilient as protected forests. A suite of 11 readily measured parameters emerged out of a first-principle approach to determining a holistic indication of soil health across a range of soils and systems in Hawaiʻi encompassing much of the diversity in the tropics and subtropics. Every land use may improve its soil health status within a reasonable range of expectations for a soil’s land use history, current land use, and mineralogy. Key drivers of the measures for soil health, including intensive land use history, current land use practices, and mineralogy, must be interwoven into the soil health index, which should set minimum and maximum benchmarks and weight parameters according to equitable standards.

erosion control, C storage, nutrient transformation, water filtration, and essential food, indigenous 50 crop, forest, etc. production). Through this lens, the well-being of humans improves as a result of 51 enhanced soil health and function, thus directly supporting a number of sustainability goals 12 52 such as UN Sustainable Development goals 2 (Zero Hunger) and 3 (Good Health and Well-53 Being) 13 . Thus, soil organic matter and healthy soils increasingly are linked to healthy societies 54 14 . 55 56 To this point, the technical discussion surrounding soil health centers primarily on agronomic 57 systems to target improving crop yields and economic return and biological properties of the soil 58 microbiome using innovative technology not readily accessible 10,15,16 . No study has yet embraced 59 the heterogeneity of natural and working lands and multiple land use needs to broadly define soil 60 health for a more expansive role in diverse, multi-functional landscapes. Complex, competing 61 demands on natural and working landscapes for food, fiber, fuel, and urbanization will continue 62 to drive sustainable development plans. Land use and management options that reconcile 63 productivity with maintenance and enhancement of biodiversity, soil health, and associated 64 ecosystem services in human-dominated landscapes are critical 17 . 65 66 The unique diversity of tropical/subtropical soils and ecosystems (including natural and working 67 lands, or agroecosystems) in the small geographic space of Hawaiʻi is an opportunity to explore 68 complex relationships between land use, land use history, soil type, and soil health. In Hawaiʻi, 69 the reconciliation of potentially competing issues of development, food production, and 70 biodiversity, together with the added pressure of climate change 18 , is urgent. In the last few 71 decades, large-scale plantation agriculture declined drastically, leaving large areas of abandoned 72 agricultural lands across the islands. Current state law mandates improvements in soil health, C 73 sequestration, and yields across agricultural sectors and forested land while in pursuit of 74 achieving at least state-level C neutrality by 2045. But, like other regions across the tropics and 75 subtropics, there are not yet science-based programs in place to support this outcome due, in part, 76 to insufficient science specific to Hawaiʻi's soils and systems. Knowledge addressing this gap in 77 Hawaiʻi, serving as a model system, can be transferred to other tropical and subtropical regions.

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In this context, we asked: as a dynamic ecosystem property not limited to agricultural systems, 80 what were the predominant drivers of healthy soils and how is soil health most effectively 81 assessed across tropical and subtropical regions and volcanic islands? We hypothesized that, 82 within the land uses and soils studied, volcanic ash-derived soil would exhibit fundamentally 83 different soil health characteristics than the others and that current land uses would affect soil 84 health parameters secondarily to inherent soil differences.

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Results 87 88 What comprises soil health? 89 Soil was collected from a range of land uses (identified a priori as protected native and non-90 native forest, managed pasture, unmanaged previously intensive agricultural lands (UPIAL), 91 organic cropland, and conventional cropland) (Fig. 1) across a range of soil orders and clay 92 mineralogy (Oxisol, Mollisol, Andisol, Inceptisol, and Vertisol) on three islands. When possible, 93 pairs or triplets of sites were obtained on the same, or related, soil series but different land use. 94 Forty-six parameters across biological, chemical, and physical soil properties were measured and 95 analyzed with a multivariate approach to 1) test for the predominant drivers of soil health on a 96 heterogenous landscape and 2) deduce a key set of indicators that represent soil health as an 97 ecosystem property. Four significant principle components analysis (PCA) axes cumulatively 98 explained 71.7 % of the variance within the soil health dataset. The two dominant axes explained 99 43.0 and 12.3 % of variance, followed by the next two that explained a further 9.0 and 7.4 %. 100 Many of the parameters across biological, chemical and physical soil properties, strongly 101 correlated (i.e., r > 0.5) to the positive or negative side of axis 1 (Table S1). 102 103 104 Figure 1. Images from field sites in each category of current land use. Protected forest included 105 both native (left) and non-native (right) stands. Unmanaged, previous intensive agriculture lands 106 (PIAL) included forest stands (left), shrub lands (center), and grasslands (right). Pasture sites 107 were managed grazing lands; croplands included organic and conventional managements. 108 109 110 Land use, specifically the legacy of intensive cultivation, predominated over soil type to influence 111 soil health. Visualization of axis 1 and 2 of the PCA showed that regardless of current land use 112 and soil type, sites with a history of long-term intensive cultivation clustered independently from 113 other land uses within forest and pasture classifications (Fig. 2). Sites without a plantation 114 agricultural history (i.e., 80+ years of sugarcane or pineapple) separated out from those sites with 115 intensive land use history along axis one. The top five strongest (i.e., r > 0.95) parameters driving 116 the sites with no intensive agricultural history toward the negative side of axis 1 were high gram 117 negative bacteria, total phospholipid fatty acids (PLFA), organic carbon (OC) concentration, total 118 N concentration, and actinomycetes. The negative side of axis 1 was also driven by high values of 119 many additional biological parameters not listed as well (Table S1). In contrast, those sites with a 120 plantation history had low concentration of those parameters negatively related to axis one, and 121 high bulk density (BD), dissolved OC (DOC) to dissolved organic N (DON) ratio, actinomycetes 122 to bacteria ratio, clay concentration, and crystalline Fe oxides. Axis 2 did not provide a clear 123 separation among the past or current land use classifications.

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To a certain degree, some current land uses correspond to areas with an intensive agricultural past 126 and others to areas without due to land availability and suitability. For example, the sampling 127 sites classified as conventional cropland and UPIAL (by its own definition) reside exclusively 128 within the cluster defined by an intensive agricultural history. Likewise, all the sampled protected 129 forests occurred in areas without the agricultural past. However, organic croplands and pastures 130 resided within both types of areas with and without the intensive agricultural past. Visualization 131 of axes two and three showed no separation among land use history or current status (Fig. S1) Soil type, as defined by soil order and more broadly by mineralogical class (i.e., high activity 145 clays, low activity clays, poorly and non-crystalline minerals, and histic) was not strongly 146 associated with axis 1 (Fig. 3). Both high and low activity clays aligned with the positive side of 147 axis 1 associated with high BD, DOC:DON, clay and crystalline minerals. However, high activity 148 clays included Vertisols and Mollisols, and associated with the positive side of axis two, driven 149 by high extractable Ca 2+ , K + , P, and Na 2+ , and sand concentration. Low activity clays included 150 Ultisols and Oxisols, and associated more with the negative side of axis 2, driven by high ratio of 151 pyrophosphate to hydroxylamine extractable Al, crystalline Fe oxides, concentration of mega size 152 class water stable aggregates, soil hardness at the surface layers, and BD. Histic soils fell on the 153 negative side of Axis 1 which correlated highly with total OC, total N, Actino:Bacteria, and AM 154 Fungi. Visualization of axis 2 and 3 helped further separate out the histic and poorly and non-155 crystalline mineral (PNCM) groups (Fig. S1). Particularly with respect to the Andisols and andic 156 Inceptisols having high concentration of poorly and non-crystalline minerals (AlH+0.5FeH), 157 PNCM separation from other soils was driven by silt and sand concentration, fungi to bacteria 158 ratio, and concentration of "mega" size class water stable aggregates.  Multiple multi-response permutation procedure (MRPP), a nonparametric multivariate test of 173 differences between groups 19 , models were run among the categorical classifications and selected 174 combinations for hypothesis testing to determine the drivers of soil health parameters (Table S2). 175 The combination of agricultural history and disturbance level was the second most significant 176 contrast (adjusted p = 0.008, A value = 0.2636). The significant pairwise comparisons indicate 177 that those sites classified as PIAL-medium are different than those PIAL-high and none-high; 178 none-low is different than PIAL-medium and none-high. These results indicate first that sites with 179 a plantation history and medium disturbance classification were more like one another than to 180 those with a high disturbance level (i.e., currently in intensive cultivation), regardless of whether 181 there was plantation past land use or not. Second, undisturbed sites with no plantation history 182 were more like one another than to those with a plantation history and medium current 183 disturbance classification or those with no plantation history but currently under intensive 184 practices. The combination of agricultural history and current land use was also among the 185 significant contrasts tested and yielded similar results to the interaction with plantation history 186 and disturbance level. In the pairwise comparisons, those sites classified as PIAL and currently 187 unmanaged (UPIAL) are different from PIAL and currently in conventional cropland, and from 188 sites with no plantation history and currently in organic cropland, pasture, or protected forest. The 189 simpler current land use model had the third highest A value (0.2583) and 10 pairwise contrasts 190 that separated from one another along the dominant PCA axis.

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The most distinct group was the UPIAL land use class, which was significantly different from all 193 other land uses (Fig. 4). Pasture sample units spanned axis 1 yet were significantly different than 194 UPIAL and organic croplands in multivariate space indicating a latent interaction with 195 mineralogy unable to be explored further within the constraints of our dataset. Pastures were not 196 different from protected forests and the two groups showed a lot of overlap in the 2-dimensional 197 visualization of axis 1 and 2, also signaling a potential influence of similar mineralogy. In the 198 case of conventional agriculture, the sample units were so dispersed within the group, that no 199 differences emerged between it and the other current land uses, except for UPIAL. However, 200 organic croplands were similar enough to one another to be significantly different than UPIAL, 201 pastures, and protected forests.

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The combination of cropland and mineral classifications also was among the significant contrasts 204 tested (Table S2) and provided additional insight into the nature of the interaction of minerals 205 with land use by sub-setting the dataset to reduce the complexity of the five current land use 206 classes to simply cropland (organic and conventional) and not cropland (UPIAL, pasture and 207 protected forest). The non-cropland PNCM sites were different from non-cropland HAC and 208 LAC. For both HAC and LAC, those sites in croplands were different from those not in cropland.

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Within the constraints of the dataset, which has greater coverage of HAC and LAC across the 210 cropland/not cropland designations, being in cropland affected soil health for both HAC and 211 LAC. Additionally, HAC in cropland was different from LAC not in cropland, and vice versa.

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Key soil health indicators for tropical/subtropical soils 214 Eleven dynamic soil parameters emerged from a multi-step dimension reduction process as 215 indicators of ecosystem health across diverse land uses, histories, and soil types. First, at its 216 foundation PCA is a dimension reduction approach, and 26 parameters correlated strongly (≥ 0.5 217 or ≤ -0.5) with axis one. A correlation matrix of the untransformed values showed covariance 218 among many of those 26 parameters (Fig. S2). From this covarying block, consideration of the 219 practicality of the parameter's inclusion in a rapid, accessible soil health index (i.e., cost and 220 difficulty) and coverage of biological, chemical, and physical parameters further reduced the list 221 to CO2 burst, HWEC, PMN, total OC %, and WHC. Among the parameters not included in the 222 block: ß-glucosidase, ß-glucosaminidase, and water stable mega aggregates (mega WSA), the 223 DOC to DON ratio, actinomycetes to bacteria ratio, and BD remained. Because actinomycetes to 224 bacteria ratio is not feasible in a rapid soil health test, it was removed from the final list. 225 226 227 Figure 4. Axes 1 and 2 of the principal components analysis for all potential soil health indicators 228 including the multi-response permutation procedures results comparing the multivariate within 229 and between group testing among the current land uses. Conventional cropland (white square), 230 organic cropland (grey square), pasture (black circle), protected forest (grey triangle), or 231 unmanaged previously intensive agriculture (UPIAL, upside down white triangle). The amount 232 of variability explained by each axis is in parentheses. Groups with different letters have 233 statistically greater similarity within the group than to others in multivariate space. 234 235 236 With the criteria of strong relationship to PCA axis 1, non-covariance, practicality, and inclusion 237 of biological, chemical, and physical parameters, 11 parameters emerged as potential indicators 238 of a soil health gradient across the soils and ecosystems in Hawaiʻi ( Table 1). Summary values 239 show the range, mean, and median of each parameter across the dataset ( Table 2). For 240 contextualization, all parameters except pH and BD are greater in soils without an intensive 241 plantation history than in those with. For several of these parameters (total OC %, CO2 burst, ß-242 glucosidase, ß-glucosaminidase, PMN, and HWEC), protected forests are greater than 243 conventional croplands, while the DOC to DON ratio was lower. Similar differences were also 244 present for pasture compared to conventional cropping except for a few (PMN and HWEC). 245 Among those parameters with significant contrasts, total OC %, CO2 burst, PMN, and HWEC are 246 the same for UPIAL versus conventional cropland, but ß-glucosidase and ß-glucosaminidase are 247 greater while the DOC to DON ratio was lower for UPIAL than conventional cropland. There 248 were fewer differences between organic and conventional cropping, but organic cropping had 249 greater total OC%, and ß-glucosaminidase than conventional. 250 251 Table 1. After assessing the sensitivity, interpretation value, and feasibility (i.e., resources required for field collection and laboratory assays), the recommended indicators to use in a routine soil health test for Hawaiʻi and potentially other tropical-subtropical and volcanic regions, were reduced to 11 parameters.

Proposed Hawaiʻi Soil Health Indicators
Parameter Function and interpretation Total organic carbon (%) As the backbone of soil organic matter, a proxy measurement of the amount of soil organic matter; higher value typically relates to benefits of multiple biological, chemical, and physical aspects of soil function Biological Properties 24 hr CO2 burst (µg g -1 ) Soil respiration in response to readily available substrate; higher value indicates high microbial activity and high quality organic matter pools Proximate microbial metabolism of amino-containing substrate; higher value indicates nutrient, predominantly N, mineralization ß-glucosaminidase (mg p-nitrophenol kg -1 soil h -1 ) Potential N supply; higher value indicates bioavailable N forms to support soil productivity Mineralizable nitrogen (µg g -1 ) Potential N supply; higher value indicates bioavailable N forms to support soil productivity Chemical Properties pH Biological and nutrient availability; 6.0-7.0 is ideal, this is the pH range where plant essential elements are most available, and toxicities are negligible DOC:DON ratio Integrated indicator of the balance of organic carbon and organic nitrogen pools; lower is better; higher value indicates disturbance -high DOC indicates available microbial substrate but also potential runoff, priming, and loss if too high, DON is readily broken down by soil microbes into inorganic forms, but low values are associated with N-deposition or poor nutrient management in disturbed systems Hot water extractable carbon (µg g -1 ) Readily available metabolic substrate; higher value indicates soluble organic matter and lysed microbial cells that support microbial activity Physical Properties Water holding capacity (%) Plant-water relations; higher values indicate improved water storage Water stable mega-aggregates (%) Water infiltration, porosity, aeration; higher values improve retention/transport water, promote root growth, provide habitat for microbes, reduce bulk density, and resist erosion Bulk density (g cm -3 ) Infiltration, porosity, and rooting environment; lower values indicate soils that are light, aerated, porous, promote root growth, and more workable

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For the subset of soils with adequate representation across mineralogy (HAC and LAC) and 254 current land use (UPIAL, organic cropland, and conventional cropland) the interactive effects of 255 these parameters on several soil health indicators, total OC %, CO2 burst, ß-glucosidase and ß-256 glucosaminidase, PMN, HWEC, and mega-WSA, were significant (Fig. 5). For LAC soils, many 257 parameters were consistently lower for conventional than organic croplands, including OC %, 258 CO2 burst, ß-glucosidase, ß-glucosaminidase, PMN, and mega-WSA. In general, UPIAL tended 259 to have lower values than organic management, but greater than conventional cropland, which 260 was especially apparent for total OC %. In contrast to LAC soils, very few significant effects of 261 land use on HAC soils were detected, and only PMN and HWEC was less in conventional than in 262 organic croplands while mega-WSA were greater. Of those parameters without significant 263 interactions, mineralogy was significant for the DOC to DON ratio and WHC, and in both cases 264 HAC was greater than LAC (Fig. 6). Current land use was significant for the DOC to DON ratio 265 (conventional > UPIAL), WHC (organic > UPIAL and conventional croplands), and BD (UPIAL 266 > organic croplands) (Fig. 7). Mineralogy and land use had no detectable effect on soil pH. Soil health is an ecosystem property 290 Soil health conceptualized as a measurable ecosystem property provides a powerful tool for 291 monitoring progress in restoration projects or implementation of best management practices to 292 promote sustainable agroecosystems. A new paradigm of soil organic matter dynamics, which is 293 central to soil health, is driving the development of new compartmental models tied to 294 measurable soil parameters 20 . Soil organisms, particularly microbial communities that are 295 proximately responsible for the flow of nutrients, C, and energy in the soil ecosystem, rely on 296 accessible organic matter for metabolic substrate. Many of the emergent process-based ecosystem 297 models are microbial models (e.g., 21,22 ) and hold promise to improve both decision support tools 298 and earth system projections.

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In policy and programs intended to incentivize maintaining and aggrading soil health across 301 multifunctional landscapes and diverse stakeholders, expectations must be gauged accordingly. 302 Long-term, intensive monocrop agriculture, which in Hawaiʻi was predominantly pineapple and 303 sugarcane plantations established post-Western contact, leaves a detrimental legacy on soil 304 health. The adverse effects on soil biological properties and microbial communities persists 305 following both abandonment and land use/management change to practices consistent with soil 306 health management principles (e.g., perennial grasses or crops, organic matter inputs, and no or 307 reduced tillage). Especially because the legacy of intensive cultivation history may carry-over 308 into the success of land-based initiatives now and into the future, it is important to understand the 309 resultant differences in baseline conditions as well as the limitations to improvements in soil 310 health when building decision support tools and programs.

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Soil health metrics interwoven with process-based ecosystem models that underpin decision 313 support tools used by policy makers may also assist in accessing aid and improved economic 314 outcomes that are critical to success in overcoming adoption barriers. Ecosystem functions such 315 as greenhouse gas emission, C storage, nutrient transformation, biomass production, and 316 regulation of hazards and extreme events link directly to key services contributing to human well-317 being 12,23 . Conventional soil organic matter models (e.g., RothC, DNDC, EPIC, and 318 CENTURY) embedded within established decision support tools that assist land managers and 319 policy makers alike contain some of these ecosystem functions. However, their ability to 320 simulate (sub)tropical or volcanic soils which have very different properties from the temperate 321 or continental soils for which they were developed currently limits their usefulness. 322 Alternatively, microbial models such as (MEMS v1.0) developed from the Microbial Efficiency-323 Matrix Stabilization framework incorporates measurable parameters that constrain C pools sizes 324 and modulate fluxes 22 . Measurable pools and rate modifiers in MEMS v1.0 include microbial 325 biomass and turnover, dissolved organic matter, sorption/desorption dynamics, and exoenzyme 326 activity. The overlap between these parameters and the measures of soil health suggest that 327 compartmental models designed to simulate soil organic SOM matter dynamics and nutrient and 328 GHG fluxes may benefit from the integration of soil health into their initialization and projections 329 for (sub)tropical soils.

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In the process of aggrading soil health, landscapes regain resilience through improved soil 332 functions. For Hawaiʻi and other (sub)tropical and volcanic regions, land-based management 333 relating to conservation, biocultural restoration, climate action for mitigation/adaptation, and 334 increased local food production strive for their specific goals. But, also contribute more broadly 335 to sustainability when viewed through the lens of improved soil health and the associated 336 expansive network of co-benefits and regulation services. Understanding, representing, and 337 projecting outcomes associated with soil health is critical to incorporating their full value into 338 complex watershed-based management and interdisciplinary social-ecological forecasting that 339 link directly to building resilient, climate ready landscapes and communities. 340 341 Land use history, current land use practices, and mineralogy are predominant drivers of 342 ecosystem soil health 343 Key drivers of the measures for soil health, including land use history, current land use practices, 344 and mineralogy, must be understood and integrated into the development of a soil health index. 345 Any index should set minimum and maximum benchmarks and weigh parameters according to 346 equitable standards. Therefore, the state of each driver (e.g., timeline of intensive use history, 347 time since implementation of current land use and management, and predominant mineralogy) 348 must be ascertained and recorded in databases designed for syntheses of soil health into the 349 future.

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The legacy of a plantation history is a strong driver of soil health, but greater complexity 352 associated with current land use, management, and soil type also is present and important to 353 understand while developing a robust soil health index for the (sub) tropics. For example, the 354 level of disturbance in current management practices and, outside of croplands, mineralogy both 355 also affected soil health. Results suggest that soil health may differ inherently for high versus low 356 activity clays and whether a system is cultivated intensively for food production (cropland) under 357 conventional versus organic management affects soil health regardless of mineralogy.

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Importantly, the soil health of pastures was not different from protected forests, suggesting that 360 well-managed grazing lands may be as healthy and resilient as protected forests. However, the 361 most distinct group was the unmanaged, previously intensive agriculture lands (UPIAL land use 362 class), which was significantly different from all other land uses. Unmanaged abandoned 363 agricultural lands are more similar to each other than to sites that remain in intensive cultivation. 364 But, they are also more similar to each other than to sites without plantation history and currently 365 are in organic croplands, pasture, or protected forest. Upon further inspection, the univariate 366 analysis suggests that, while the relationship between organic versus conventional cropland was 367 largely consistent across soil type for most soil health parameters, abandoned cropland was more 368 variable. For UPIAL, in some cases, soil health indicators fell in between organic and 369 conventional croplands, while sometimes aligning more closely to conventional or organic for 370 other indicators. This finding further highlights the imprint that intensive agriculture may have on 371 the health of a soil and demonstrates the constraints to rebuilding soil health upon the cessation of 372 soil disturbance without proactive management strategies. 373 374

Proposed "Hawaiʻi Soil Health Indicators" for (sub)tropical and volcanic soils and systems 375
A suite of readily measured parameters emerged out of a first-principle approach to determining a 376 holistic indication of soil health across a range of soils and systems in Hawaiʻi encompassing 377 much of the diversity in the tropics and subtropics. These parameters integrate biological, 378 chemical, and physical properties with key functions associated with soil C and nutrient cycling, 379 water relations, and generally, the provisions of a soil environment conducive to a diverse soil 380 organismal community. These parameters are consistent with current measures of soil health, but 381 developed with a more organic and equitable process, without carry over of ingrained bias. In the 382 development of a soil health index, parameters may be weighted differentially for systems.

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Further, cropping systems should be paired with additional fertility testing and nutrient 384 management for optimal environmental and yield outcome. Every land use may improve its soil 385 health status within a reasonable range of expectations when considering land use history, current 386 land use, and mineralogy.

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The measurement of soil health as a dynamic ecosystem property is only possible by properly 389 identifying the right suite of parameters specific to a region and metering that measurement to 390 appropriate benchmarks for a system defined by past land use, current land use, and mineralogy. 391 Moving forward, providing a soil health index of (sub)tropical and volcanic soils will help to 392 assist currently underserved producers and land managers improve the health and productivity of 393 their lands and simultaneously reap co-benefits of a healthier environment and society. Within 394 this framework, fair and equitable programs can be established to improve economic outcomes as 395 well as C neutrality goals. 396 397 Conclusion 398 Land use, particularly where a legacy of intensive cultivation existed, predominated soil health 399 metrics, which supports continued policies and programs that help incentivize producers and land 400 managers to implement best practices. Because of the close association of soil health and C 401 cycling, climate change mitigation is a powerful co-benefit of improving soil health in degraded 402 systems. As Amundson and Biardeau 24 put forward, "soil carbon sequestration is an elusive 403 climate mitigation tool." However, soil health is a more inclusive measure of the holistic value of 404 improving the state of a natural resource key to achieving multiple sustainability goals 405 worldwide.

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Competing demands for food, fiber, fuel, and urbanization will continue. In Hawaiʻi, especially, 408 competing land uses associated with development, food production, and biodiversity under 409 climate change is a pressing issue. Improved land use projections are critical for reducing 410 uncertainties in indicators for ecosystem services in a changing environment 25 . Land use and 411 management options that reconcile production with maintenance and enhancement of 412 biodiversity, soil health and associated ecosystem services in human dominated landscapes now 413 and into the future are critical. We conclude that soil health is a measurable ecosystem property 414 and that land use history, current land use practices, and mineralogy are all predominant drivers 415 of soil health in landscapes. Our proposed "Hawaiʻi Soil Health Indicators" may be further 416 validated for (sub)tropical and volcanic soils and systems and are critical to developing regionally 417 appropriate incentives programs and policy.

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Materials and Methods 420 Site selection and general approach Twenty-two sites were selected across three islands (Oahu, 421 Maui, Molokai) within the main Hawaiian archipelago to cover a diversity of soil management, 422 fertility, and taxonomy to maximize the variance in parameters associated with soil health. 423 Preliminary assessments helped categorize sites into 1) current land management/use, 2) land use 424 history, 3) disturbance level, 4) soil order, and 5) predominant mineralogy. Current land 425 management/uses included protected forests (managed to preserve long-term non-native or native 426 forest, greater than 100 years no disturbance from feral ungulates), unmanaged previously 427 intensive agricultural lands (UPIAL, previously monocrop plantation with no current 428 management system, grasses, shrubs, or forest as dominate cover, and less than 100 years no 429 disturbance), pasture (managed with pasture grasses for rearing livestock), organic croplands (no 430 use of chemical pesticides), and conventional croplands (use of chemical pesticides). Land use 431 history indicated simply whether an intensive plantation history (for Hawaiʻi, this is typically 432 sugarcane or pineapple) was present or absent. Disturbance level was defined categorically as 433 low, medium, and high (Table 3). Soil order was according to final GPS coordinates of sample 434 location and NRCS NCSS taxonomic classification (Web Soil Survey). Predominant mineralogy 435 was assigned using taxonomic classification and a diagnostic key (Table S3). 436 437 Table 3. Assessment of level of disturbance for each study site is categorized based on the time frames described since the most recent soil disturbance, based on available history of land use. Disturbance was considered to be land that has undergone man-made change to soil's surface layer by physical disruption of the soil structure and ecosystem, such as tillage or compaction. The final compilation included an integration of sites across soil types, land use history, and 439

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natural versus agricultural landscapes that is representative of Hawaiʻi (Table 4). However, as is 440 reflective of reality, some soil types are more represented in some land uses and some current 441 land uses are more likely to be represented in one past land use history or another. Therefore, we 442 purposefully designed this study as a multivariate approach to identifying parameters indicative 443 of soil function, specifically healthy soil function, for the ecosystems of Hawaiʻi (and other 444 similar tropical/subtropical, and volcanic regions). Then, we narrowed down to key parameters 445 that can be linked to drivers to facilitate the next steps of developing an index of soil health and 446 refining the parameters for specific systems with the goal of assisting landowners, managers, and 447 farmers to improve the health and resilience of their lands. soil from five cores was homogenized into one sample in a bucket in the field. Three quadrants 455 that each produced one soil sample for a site were located at least five m apart within the site. 456 Thus, 66 soil samples were packaged in a cooler with ice and transported to the lab for analysis. 457 Samples were transported to processing and storage facilities at UH Mānoa and subsets were 458 frozen at -20 °C and air-dried (<10 % moisture). Additionally, a subset for phospholipid fatty acid 459 testing (see below) was kept chilled, not frozen, and shipped immediately under refrigeration to 460 the analysis facility.

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Soil health parameters Forty-six parameters classified as biological, chemical, or physical and 463 tied to soil function or health were measured for each of the 66 samples (Table 5). 464 Table 5. Methods and functional interpretation of forty-six parameters classified as biological, chemical, or physical and tied to soil function or health were measured for each of the 66 samples. The normally distributed soil health matrix was analyzed using a standardized PCA with a 489 correlation cross-products matrix, which produces correlation coefficients among the variables 490 and further standardizes non-comparable response variables. This method provides a broken stick 491 eigenvalue; the broken stick eigenvalue was less than the actual eigenvalue for the first four axes, 492 therefore these are all presented and interpreted to some degree 19 . Rnd-Lambda randomization 493 results agreed with the broken stick method, the last useful axis is four with p = 0.001 and 494 cumulative variance explained at 71.7 %.

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Multi-response permutation procedure (MRPP) is a nonparametric multivariate test of differences 497 between groups 19 . The A statistic describes effect size with respect to how similar within-group 498 samples are compared to outside the group samples. When A = 1, sample units within each group 499 are identical, when A = 0, groups are no more different than expected by chance. We tested all 500 possible models based on the site attributes -agricultural history, current land use, soil order, 501 mineralogical class, disturbance level, and cropland versus non-cropland. The MRPP was run 502 with Euclidean distance measure on the transformed data. Any contrast with less than two sites (6 503 sample points) was excluded. In the final model, an adjusted p value was calculated by dividing 504 the model p value by the number of pairwise contrasts, the adjusted p value was used to 505 determine whether a pairwise contrast was significant or not. 506 507 Dimension reduction The list of 46 potential indicators of soil health was reduced to a short list of 508 key indicators that meet multiple criteria for capturing the breadth of soil health as an ecosystem 509 property, reducing multicollinearity with other variables, and practicality for inclusion in a 510 routine soil test. First, potential parameters were removed if r < 0.50 or > -0.50 with axis 1 in the 511 PCA, which left 26 selected for further assessment. A hierarchical ordering correlogram of the 512 untransformed values for remaining 26 selected parameters was performed in R. Highly 513 correlated parameters were reduced further on the basis of practicality (i.e., combination of cost 514 and difficulty). The final list was cross checked to maintain balanced coverage across biological, 515 chemical, and physical properties. Within the constraints of the original sample design, the key 516 parameters of soil health were compared across a subset of mineral and land use classes to assess 517 their utility as indicators of soil health. 518 519 Univariate analyses were conducted to first determine the effect of past land use (PIAL versus 520 none) and then assess the effect of current land use (protected forest, pasture, UPIAL, organic 521 cropland, versus conventional cropland) on each of the 11 soil health indicators. A mixed model 522 ANOVA approach was used to assess the main effect of past or present land use with soil 523 mineralogical classes as the random effect (lmer function in the lme4 package 28 ). General linear 524 hypothesis testing (glht function in multcomp package 29 ) was performed to compare group 525 means (Tukey-adjusted). For a subset of the data (including UPIAL, organic cropland, and 526 conventional cropland in LAC and HAC soils), mixed factorial ANOVA was performed to 527 examine the interactive effect of soil mineralogical class and current land use on each soil health 528 indicator with farm/location as the random effect (lmer function in the lme4 package  Table S1. Correlation values of indicators to all four significant principal components analysis axes. The five parameters most positively and negatively correlated to each axis are in bold italics. Parameters with correlation values to axes ±0.75-1.0 are dark grey, ±0.50-0.75 are medium grey, and ±0.25-0.50 are light grey. The biological factors and total organic C (OC) and TN concentrations were strongly negatively correlated with the axis 1. One exception was the actinomycetes to bacteria ratio, which was one of the strongest correlates with the positive side of axis 1. Other strong positive correlates to the first axis included the ratio of dissolved organic carbon (DOC) to dissolved organic nitrogen (DON) and bulk density (BD). Biological parameters did not contribute to the spread across axis 2, whereas chemical and physical parameters correlated strongly with both sides (positive and negative). The strongest correlates with axis 3 were fewer than for the first two axes, but largely derived from the chemical and physical categories. Interestingly, biological parameters emerged again as strong negative correlates with axis 4, and chemical and physical parameters were associated with both sides of that axis.  Table S2. Results of a multi-response permutation procedure to test significance of multidimensional spatial differences between the proposed varying management groups. Higher A value indicates stronger model, adjusted p-value is 0.05 divided by the number of pairwise contrasts and was used to determine the significance of pairwise comparisons in the final model. Groups with fewer than one site (i.e., three within-site sample units) were excluded from the model. These classifications include agricultural history (previously intensive agricultural lands "PIAL" or "none"), current land use (conventional cropland, organic cropland, pasture, protected forest, or unmanaged previously intensive agriculture), soil order (Mollisol, Vertisol, Inceptisol, Oxisol, Ultisol, and Andisol) mineralogical class (high activity clays "HAC", low activity clays "LAC", poorly and non-crystalline minerals "PNCM", and histic "HIS"), disturbance ("low" at least 50 yr no tillage, "medium" tilled in the last 10-50 yr, and "high" tilled in the last 10 yr), and cropland ("not cropland" or "cropland"). The categorical combination of current land use and minerals was the most significant model tested (A = 0.2926, p < 0.0001). However, due to the high numbers of pairwise contrasts and excluded groups, this model was not accepted as valid.