Limited prior research that has looked at the effects of multiple soil management practices indicates that metrics for soil health are a product of both inherent soil properties and dynamic soil properties . Whether available soil indicators could translate these soil properties and processes when management systems are complex remains unclear. As an added layer of complexity, field variability is hard to distinguish from management-induced changes in soil properties . To address this challenge, prior studies have suggested increasing samples, the number of sites, and sampling strategies that account for spatial and temporal variability ; however, as farmers themselves expressed in this study, such an approach requires additional time and resources, and may not increase their utility—at least to farmers—in the end. In this sense, farmer knowledge may serve as an important mechanism for ground-truthing soil health assessments, particularly when management is synergistic and does not rely heavily on organic fertilizers. As emphasized by our results above, farmer involvement in soil health assessment studies is imperative to better converge soil indicators with farmer knowledge of their soil. Lastly, our results also highlight the utility of incorporating information about nitrogen-based fertilizer application on sampled field sites, round plastic pot particularly when assessing soil indicators on working farms with a large variation in the quantity of N-based fertilizers applied .
Farms on the low end of additional organic fertilizer application showed minimal differences between farmer selected fields for soil fertility, particularly in terms of soil inorganic nitrogen —which suggests that differences in soil fertility in fields with more circular nutrient use may be less detectable using commonly available soil indicators. This cursory finding here corroborated farmer observations touched on in the previous section above, and requires further investigation to see if similar trends extend to other organic systems. Here, we have identified several gaps in the utility of commonly available indicators for soil fertility among a unique group of organic farmers in Yolo County, California using interviews with farmers and field surveys. Our study highlights that if available soil indicators are to be considered effective by farmers, they must be grounded in farmers’ realities. Moving forward, working in collaboration with farmers to close this continued gap in soil health research will be essential in order to ground widely available soil indicators in real working farms with unique management systems and variable, local soil conditions. This approach is particularly needed among organic farms that do not rely extensively on nitrogen-based organic fertilizers and additional nutrient input to supply their fertility, as available soil indicators do not adequately reflect farmers’ descriptive metrics for soil fertility.
Moreover, our research elevates concerns that currently available soil indicators used in soil health and fertility assessments may not fully capture the complex plant-microbe-soil interactions that regulate soil fertility, particularly on organic farms that use minimal organic fertilizer application. Moving forward, additional studies that pursue a deeper dive into nutrient dynamics across a gradient of management and varying nitrogen-based fertilizer input is needed. Overall, the strong overlap between farmer knowledge in this study and ongoing soil health research speaks to the opportunity to further engage with farmers in developing useful indicators for soil health and fertility that are better calibrated to local contexts and draw on local farmer knowledge. A deeper investigation of farmers knowledge systems, in particular farmer understanding of soil function in connection with crop productivity, soil health, and soil fertility, represents a critical path forward for this research arena. Additionally, we recommend placing greater emphasis on developing descriptive indicators for soil health and fertility in collaboration with farmers that are better integrated with ongoing qualitative soil health and fertility metrics. These descriptive indicators should not be developed in isolation to ongoing research on soil health and fertility assessment, but rather as an integrated research process among scientists, farmers, and extension agents—importantly, with scientists as listeners working toward a shared language. Ants benefit plants . Humans have known this for quite a long time. In fact, ants were described as biological control agents in China around 304 AD . Surveys of tropical forests show that up to one third of all woody plants have evolved ant-attracting rewards .
Some plants provide domatia as ant housing structures, while others attract ants to their tissues with extra-floral nectaries. Some plants are hosts to honeydew-producing hemipterans that excrete honeydew, a sugary substance consumed by ants. Still other plants are simply substrates for ant foraging. The majority of studies conducted across these ant–plant groups show that ants benefit plants by removal of herbivores . Nonetheless, in many agroecosystems, the benefits of pest control services by ants are not recognized. Agricultural managers often view them as pests or annoyances to agricultural production because some ants tend honeydew-producing insects that can damage crops . However, a review of the literature on ant-hemipteran associations suggests that even these associations benefit plants indirectly because ants remove other, more damaging herbivores . Regardless, the literature lacks studies investigating ant–plant interactions in agroecosystems. Here, we broadly survey the pest control services provided by a suite of ant species to better understand the role of ant defense of coffee. Coffee is a tropical crop that occurs as an understory shrub in its native range, and coffee plants are therefore often grown under a canopy of shade trees in agroforestry systems in some parts of the world . This canopy layer provides plantatsions with a forest-like vegetation structure that can help maintain biodiversity . Ant biodiversity is high in many coffee plantations and ants attack and prey on many coffee pests, including the coffee berry borer . For example, Azteca instabilis F. Smith is a competitively dominant ant that aggressively patrols arboreal territories in high densities and previous research has found that it impacts the CBB . Some laboratory and observational field studies have found that Pseudomyrmex spp., Procryptocerus hylaeus Kempf, and Pheidole spp. may limit the CBB . However, other field experiments have not found ants to be biological control agents of the CBB . Further, round pot the pest control effects of many ant species on the CBB have not yet been evaluated and it could be that previously documented effects are specific to only a few species. Natural ant pest control of the CBB is particularly important because chemical insecticides used to control CBB are not always effective. This lack of effectiveness is in part because the CBB lifecycle takes place largely hidden within coffee berries and also because the CBB has developed insecticide resistance . Several of the stages of the CBB life cycle make it vulnerable to attack by ants . First, the CBB hatches from eggs within the coffee berry, where it consumes the seeds . Small ants may enter the berry through the beetle entrance hole and predate the larvae and adults inside . Second, old berries infested with the CBB may not be harvested because they often turn black and remain on the coffee branches or may fall to the ground . These old infested berries may act as a population reservoir of borer populations and ant predation at this stage could be very important for limiting CBB populations in the next season. Third, as adult borers disperse to colonize new berries, ants may prevent them from entering new berries . To date, no field experiment has specifically investigated how coffee-foraging ants limit CBB colonization of berries. Here, we studied the abilities of eight ant species to prevent colonization of berries by the CBB. We hypothesized that only species with high activity on branches would limit CBB colonization of berries. We show that six of eight ant species limit CBB colonization of berries and that the effect of ants is independent of ant activity on branches. This study is the first field experiment to provide evidence that a diverse group of ant species limits the CBB from colonizing coffee berries.Our goal was to capture a broad survey of the ant species that occupy the coffee vegetation in the coffee plantation.
Within the plantation, five Crematogaster spp. forage in the coffee, however field identification at the time was not reliable therefore taxonomic resolution for Crematogaster spp. remained at the genus level. For P. simplex and P. ejectus it was not always possible to find occupied bushes by observation of ant foraging. Instead, for P. simplex and P. ejectus, we determined occupation by removing all dead twigs on the coffee bush and searching these for ant nests within the hollow branches . We reattached the nested hollow branch to a living branch with thin wire and treated these bushes as bushes occupied by P. simplex or P. ejectus. To test the effects of each ant on CBB colonization of berries, we performed an ant exclusion experiment. We surveyed bushes occupied by one of the eight target ant species. We excluded coffee bushes with few branches to control for the size of the foraging area of each ant species. On each bush, we searched for two branches of equal age and position and roughly the same number of coffee berries . On each branch, we removed all berries that had CBB entrance holes. We then removed all ants from one branch and applied tangle foot to the base of the branch near the coffee trunk. On the second branch, we left ants to forage freely . To estimate ant activity, we counted the total number of ants foraging on the stem, leaves, and berries of each branch for 1-min including those that travelled onto the branch during the 1-min survey. We also counted ants on exclusion branches after the experiment and if a branch had more than one ant individual present, we excluded the bush from analysis . To release CBB onto control and treatment branches, we created a leaf platform to aid their chances of encountering berries. The leaf was wedged between the branch stem and a cluster of berries to create a platform surrounding the cluster . A coffee leaf was used as a platform because artificial structures attract attention from many ant species. After waiting several minutes to ensure normal ant activity, we released 20 CBBs on the leaf platforms of the control and exclusion branches. After 24 h, we counted the number of berries per branch that had CBBs inside entrance holes. We did not count partially bored holes in berries, nor CBBs that had bored into twigs and leaves. Multiple bored entrance holes per berry were only counted as one bored berry. We modified the experiment slightly for P. simplex and P. ejectus because of the difficulty in locating these species within a bush using visual cues . For these two species, we used the living branch to which the nest was attached to as the control branch . This was done because we wanted to make sure that ants were actively foraging on control branches after the disturbance of removing nests. To statistically analyze experimental data, we opted to use linear mixed models instead of paired t tests because mixed models allow inclusions of experimental non-independencies through the incorporation of covariates. We included bush as a random effect in the model to pair control and exclusion branches within each bush. Ant species and treatment and the species 9 treatment interaction were included as fixed effects in the model. To control for differences between each branch and bush, we included the number of berries per branch, the number of berries in contact with the leaf platform, and the number of worker ants per branch as covariates in the model. We performed type III F tests of significance for main effects with maximum likelihood to estimate the fixed effect parameters and variance of random effects . We removed non-significant factors from models and compared nested and null models with likelihood ratio tests to determine the best-fit model. We also compared ant activity across different species to determine if this factor might correlate with berries bored and vary across ant species. To determine if ant activity correlated with the number of coffee berries bored, we limited the dataset to only control branches and used a generalized linear model with a Poisson log-link function because data did not meet the assumptions of normality. To determine if ant activity varied by species, we again limited the dataset to only control branches and used ANOVA with Tukey’s HSD analysis.