The magnitude of the potential acidity of the soil depends on the type and amount of clay and organic matter. Although there is some inverse parallelism between acidity saturation and base saturation, these terms are not complementary because they have different denominators . Contrary to Al toxicity and acidity saturation, there is no direct association between base saturation and crop yields. Instead, a minimum base saturation threshold is defined such that, above it, no soil acidity problems are detected . Therefore, recommended target base saturation levels must be defined locally for each crop type . Most lime requirement methods used in temperate regions target soil pH , estimating with locally-calibrated models the lime rate required to raise the soil pH to a specific level . In acid tropical soils, maximum yields can be obtained with a pH as low as 5, depending on other soil chemical properties , and raising the pH to higher values can result in a loss of soil structure and other problems . Therefore, a target pH level is seldomly used, and when used, it must be defined locally . There is a negative exponential relationship between exchangeable acidity and soil pH . Very high exchangeable acidity values are only found in soils with a low pH,garden grow bags but not all soils with a low pH have high exchangeable acidity.
Exchangeable acidity approaches 0 at a pH above 5.5, and there is virtually no exchangeable acidity at a pH above 6 . Therefore, a target pH between 5.5 and 6 should be high enough to address Al toxicity problems.Acid tropical soils have very low plant-available phosphorus because of the high P fixation capacity of Fe and Al oxides often present in weathered tropical soils. Liming has the associated benefit of increasing P availability, which might result in significant yield responses, particularly when P fertilization is low . However, liming can only provide short-term relief to P deficiencies in soils with low P reserves . Therefore, phosphorus availability is not considered a direct target of liming, and lime requirement models do not consider it. However, the increase in P availability can be an important reason for observing a yield response to lime.The lime recommendation models were evaluated using data from four soil incubation studies that measured the effect of liming on exchangeable acidity and ECEC or acidity saturation . Studies only measuring the effect of liming on pH were not considered. Soil incubation studies are experiments in which soil samples are mixed with different lime treatments and incubated under controlled conditions for about a month to ensure that all lime reacts with the soil. The liming effect is assessed by measuring chemical soil properties before and after each treatment. The data from Kamprath , Cochrane et al. , and Ananthacumaraswamy and Baker were readily available, but Teixeira et al. soil data were not. The Teixeira et al. data was reconstructed in two steps: the initial soil properties were back solved from lime requirement formulas and lime rates, and the final soil properties were estimated using the regression formulas provided in the supplementary information .
Table 1 describes the main features of these four data sets. We have shown important differences in lime rates and prediction accuracy depending on the target soil property and model . When the target is to ameliorate the Al toxicity of the soil by neutralizing its acidity saturation to a certain level, both Kamprath and Cochrane et al. models provided reasonable accuracy . Nevertheless, the new model formulated in Eq. 12 offers improved accuracy and the advantage of being sustained by a formal mathematical derivation that can be expanded . Similarly, the base saturation model also has great prediction accuracy, particularly for target base saturation levels of around 50% . In contrast, no model based on a target pH can deliver accurate results without additional soil tests, and they need to be developed locally . The model presented here is the only model based on a target acidity saturation with greater accuracy than the original Cochrane et al. model . The authors of the ACID4, NuMaSS, and MG5 models claimed that they modified the Cochrane et al. model to improve the accuracy for their target region. However, there are no available data supporting those statements. The decreased accuracy that we found may partly be because we did not have access to these data. However, we believe that these more complex models suffer from overfitting to the datasets used to build them. In other words, they may perform better in particular regions, but this has come at the expense of general validity. Conversely, the new model is more robust than previous models because it is based on mathematical foundations and strong empirical relations. These relations are consistent through a wide range of soils from different regions.
However, we observed a small incubation study effect in the relations shown in Figure 3, which might be a consequence of the soil region or, more likely, because of the incubation study per se . Experimental results have an error component, including systematic errors that are consistent within one experiment but differ between experiments, introducing statistical bias. This bias can be reduced with standardized procedures. However, lime incubation studies are not fully standardized, and they differ in the incubation time and temperature, liming materials, and water additions, among other variables. For instance, we excluded data from an incubation study that used tap water rather than distilled water to keep the soil samples moist during the incubation because control treatments had significantly more exchangeable Ca2+ and less exchangeable acidity than the initial conditions. A more thorough standardization of experimental procedures for measuring liming effects would help the development of general models for lime requirement estimation. A novel feature of the model formulated in Eq. 12 is that the lime factor is a continuous function of TAS. The Cochrane et al. model modifies the lf depending on TAS and the initial acidity saturation, using a discontinuous rule with two fixed lf . However, the proposed rule does not always improve accuracy, not even in their data, as shown by the points with incorrect lf . In the MG5 and NuMaSS methods, the lf depends on clay content or activity. Our review does not show evidence for a need to adjust the lf as a function of clay, despite the wide range of clay content and soils included in the four soil incubation studies used here. Adjusting the lf and lime rates by clay content might be a work-around to account for differences in soil bulk density when the method returns lime rates in tons per hectare without directly including the soil bulk density in the formulas. Nevertheless, clay type and content could be considered in future corrections of the TAS method, particularly if there are high deviations in the association between lime rate and Δexch. acid and Δexch. bases. It seems counter intuitive that, while both the acidity saturation and base saturation models are highly accurate on their target,tomato grow bags the lime requirement they predict can be so sharply different . These differences highlight the importance of identifying the soil chemical property most associated with the crop yield response to liming. It might be that reaching a given level for some property, such as a base saturation of 70% or a pH of 6, guarantees that all soil acidity problems are solved without leading to over liming problems. However, this approach can also result in lime requirement estimates that are much too high , which might be particularly problematic when lime is expensive, and its manipulation cumbersome. The alternative is to target the most limiting factor for crop yield, which is frequently Al toxicity in acid tropical soils . However, this approach can under predict lime requirements when Al toxicity is the only target but not the acidity problem most limiting crop yields.
A comprehensive approach would predict the lime rate needed to tackle every potential acidity problem while considering other management alternatives. However, crop responses to other acidity problems, such as calcium and magnesium deficiencies, are unclear, and their liming requirements have not been defined. Thus, more research on crop responses to lime in soils with these specific acidity problems is needed to develop a lime requirement method that tackles them all. Lime requirement models can be useful for strategic research on potential lime use in tropical regions where liming is still a rare practice and the experimental evidence is scarce . These models estimate the lime rate needed to reach a target soil condition based on readily available standard soil data . Such information could be used together with the crop response to that soil condition to estimate the effect of liming on crop yield. For instance, there is ample evidence of the association between acidity saturation and crop yields . Therefore, the expected yield response to lime can be predicted by estimating what fraction of the maximum yield is observed at the current acidity saturation level while assuming that the final yield after liming is the inverse of that fraction. If data on lime and grain prices is available, such functions can be used to get a first approximation of the potential profitability of liming. Such analysis can help identify regions where liming investments might be more successful, pinpointing national governments and private sector efforts. However, this does not mean that the readily available soil data used by the models reviewed here has sufficient quality for farm-level recommendations . Therefore, farm-level lime requirements would be more accurate when based on soil properties measurements or additional local soil-quality indicators, such as soil color, soil texture, or presence of specific plant species . The soil properties used by the lime requirement models reviewed here are wet-lab measurements, which are costly and may be inaccessible for farmers in the tropics. Therefore, farmers in the tropics could benefit from cost effective, quick tests for lime requirement prediction, but these need to be developed locally.Worldwide, biodiversity is declining at unprecedented rates, threatening species persistence as well as the benefits humans gain from ecosystems. These benefits, known as ecosystem services, have become an increasingly important argument for biodiversity conservation. The economic and other benefits from ecosystems can motivate conservation action, and are more and more being used in payment for ecosystem service schemes. Once an economic value of the service has been determined, it can be captured in commercial markets or quantified in terms comparable with economic services and manufactured capital. These economic values can then potentially be used to support biodiversity conservation within policies. The use of ecosystem services arguments for justifying biodiversity conservation is, however, not without risk or controversy. Many experimental studies show that biodiversity increases the magnitude and/or stability of ecosystem functioning , and that most species contribute to ecosystem functioning in some way. However, such studies do not consider the costs of maintaining or promoting biodiversity, even though costs are generally a limiting factor for implementing real-world conservation policies. When the economic pay-off from ecosystem services is the main factor motivating conservation, the cost-effective action is to conserve the subset of species that provide the greatest return at relatively short timescales. Because real-world communities are almost invariably dominated by a small number of species that often respond readily to conservation management, we hypothesize that in real-world landscapes the majority of the services is provided by a relatively small number of species; that these species are generally common, and that threatened species rarely contribute to present ecosystem service delivery; and that the most important ecosystem-service-providing species can be easily enhanced by simple management actions that are insufficient to support threatened species. Support for these hypotheses would suggest that delivery of ecosystem services is insufficient as a general argument for biodiversity conservation. Here we test these hypotheses using data from 90 studies and 1,394 crop fields on crop-visiting bee communities from five continents. Pollination is an important ecosystem service. The economic contribution of pollinators to crop production is significant, and bees are generally considered the most important pollinators of crops. We find that wild bee communities contribute on average over $3,000 ha 1 to the production of insect-pollinated crops. However, a limited subset of all known bee species provides the majority of pollination services because, across different crops, years and large bio geographical regions, crop-visiting bee communities are dominated by a small number of common species and rarely contain regionally threatened species.