Due to the fact that only a very small number of microbes can be reliably cultured for further study towards using them as legume crop inoculants, we focused on the PGPB that the plant specifically selects within its root nodules with the goal of finding “helpers” for the nitrogen-fixing rhizobia in supporting plant growth. Soil collections were made in 2017 and 2019 from the farm of the Botswana University of Agricultural and Natural Resources in Notwane from underneath an indigenous Tephrosia purpurea plant . Environmental DNA was isolated from both samples in 2019, at the same time trap experiments were performed, to provide insight into the diversity of the soil microbial community. It should be noted that there was a two-year gap between collecting the 2017 sample and its analysis in contrast to the 2019 sample that was analyzed almost immediately after collection. Because the methodology for DNA extraction was the same, we hypothesized that differences in the percentages of the phyla might occur from changes brought about by storage conditions, or time elapsed. In both the 2017 and 2019 samples, the major phyla were Proteobacteria, Firmicutes, and Actinobacteria. The percentage of Actinobacteria in the 2019 soil was almost twice that of the 2017-collected soil . These results are in line with those obtained by other authors. The bacterial genera responsible for the induction of N2-fixing nodules in legumes belong to the phylum Proteobacteria and are therefore part of the dominant group. The phyla Actinobacteria and Firmicutes contain several genera of bacteria with PGPB activities that are very well documented. The percentages of Gemmatimonadetes, Acidobacteria, and Planctomycetes also varied between the 2017- and 2019-analyzed samples . Whether or not these differences are due to the delay between collection and analysis or other factors such as changes in the surrounding environment such as water content is not known. Nevertheless,plastic pots for planting the data demonstrated that the dominant microbes from the eDNA analysis were Proteobacteria, Firmicutes, and Actinobacteria, all of which are more likely to be cultured and serve as inoculants than the other bacteria listed.

Soil isolates are often considered as sources of inoculants for crops in agriculture, particularly rhizobia and other plant growth-promoting bacteria , but the nodule isolates may be a more specific inoculant for because they are found within nitrogen-fixing nodules. Evidence based on coinoculation experiments with rhizobia also indicates that soil-isolated as well as nodule-associated bacteria may be important for improving plant growth via plant nutrition. Although a large number of soil isolates have been tested for their ability to produce siderophores, solubilize phosphate, fix nitrogen, or perform other plant-growth promoting functions, to our knowledge only a few of them have been actively incorporated into agricultural practices. Due to the sheer numbers of soil isolates potentially available in Botswana soils , we focused our study on microbes housed in legume nodules. Several trap plants, including Vigna unguiculata , Macroptilium atropurpureum , and Tephrosia virginiana, nodulated following inoculation with Botswana soil mixed with an artificial substrate watered with -N medium, but cowpea gave the most consistent results . Bacteria isolated from cowpea nodules included rhizobia , which are known to nodulate cowpea and other legumes . Furthermore, species of Bacillus, including B. safensis and B. pumilus, well known PGPB, were also isolated from cowpea nodules . In addition, several possible opportunistic pathogens including Ochrobactrum anthropi, Burkholderia dolosa, Ralstonia mannitolytica, Staphylococcus pasteuri and others were isolated from cowpea nodules and identified by rrs sequencing. These emerging pathogens, which are often found in plant rhizospheres, were discarded. Non-pathogenic isolates were tested for PGP traits and their ability to grow under salinity stress and at different pH values . A number of isolates exhibited possible PGP activity including phosphate solubilization and siderophore production. Cowpea plants grown in the 2017 Botswana soil sample were harvested after 9 weeks of growth. Control +N plants produced more biomass as measured by dry weight than plants from all other treatments, averaging 1.73 g. The experimental plants were darker green in color than control -N plants and produced more than twice as much biomass, averaging 1.11 g compared to 0.47 g for the -N control.

Cowpea plants grown in the 2019 Botswana soil sample were harvested after 12 weeks of growth because of a lag in growth at the start. Control +N plants were larger, more robust, and darker green than experimental or control -N plants, averaging 0.77 g. Although the experimental plants were not as robust as the +N control plants, a result frequently observed in control plants given super-optimal N, the inoculated cowpeas produced significantly more biomass than the -N controls. All experimental cowpea plants from both soil treatments developed multiple, pink colored root nodules, whereas control -N and control +N plants were devoid of nodules. In both experiments, control -N plants were indistinguishable from control plants grown in soil that was sterilized by auto claving .Because cultivation methods are biased for the reason that very few bacteria are capable of growing on standard bacteriological culture media, we analyzed the cowpea nodule microbiome by isolating eDNA from the nodule tissue and sequenced the eDNA with the goal of obtaining an inventory of the nodule microbial population. We predicted that these analyses would give us insight into the bacteria that were specifically selected by the plant and if they were culturable, they might have potential to be used as commercial inocula. As expected from anatomical studies of determinate nodules such as cowpea, the nodule interior based on eDNA analysis is dominated by Bradyrhizobium spp. . Although DNA sequences from numerous bacterial genera including Microvirga, Rhizobium, Bacillus, Sphingomonas, and others were detected in the nodule microbiome in this study , the exact percentages and diversity of non-rhizobial microbial sequences within the nodule itself are difficult to assess. Nonetheless, several of the genera in the nodule microbiome analysis directly correspond to the nodule isolate genera. The bacterial population of nodules based on sequencing the isolated eDNA differs in terms of representation from the results obtained from isolating microbes from soil. The soil population is dominated by Actinobacteria, Proteobacteria, and Firmicutes . Although a large number of Gram-positive species are detected in the soil microbiome analysis, they are detected at very low levels in the nodule microbiome .

Nevertheless, some genera such as Bacillus as well as actinomycetes, especially Micromonospora, are repeatedly isolated from nitrogen-fixing nodules and also detected in the nodule microbiome analysis. Coupled with the fact that several of these species, when inoculated with rhizobia frequently enhance the symbiosis,drainage for plants in pots this strongly suggests that they have a positive effect on the symbiosis. The difference between the soil and nodule microbial populations in terms of numbers of microbes is reminiscent of the differences in the numbers of bacteria found in the rhizosphere versus the endosphere and between the rhizosphere and rhizoplane communities in other plant systems such as pepper and maize. Whether or not this is a specific selection for a large number of beneficial bacteria to protect the root or leaf surface from pathogen attack as suggested by the camouflage hypothesis or that normally surface bacteria are excluded from internal tissues and only some of the bacteria that enter roots and nodules are “cheaters” is difficult to determine at this time. In contrast, rhizobia are actively selected by the host plant for their symbiotic traits in response to active recognition between the host and its symbiont. Whether a similar recognition system operates between PGPB and plant surfaces is not known. Although the mechanisms used by the non-rhizobial endophytes to enter the root and the nodule frequently involve the secretion of hydrolytic enzymes such as cellulase and pectinase, are these enzymes induced because the bacteria are recognized, and if so, what are the signals to which the endophytes are responding? To our knowledge, the mechanisms underlying how a coinoculation between rhizobia and PGPB triggers plant growth stimulation are not well understood. There is now a large literature examining the impact of climate change on agricultural yields that can be divided into two types of studies. A second category that has been developed more recently looks at statistical relationships between climate or weather and crop yields. The benefits of the former are that it is grounded in a mechanistic, bottom-up understanding of how plants grow. But process-based models are often calibrated to specific field settings which can be data intensive and means the generalizability of results to larger areas is unclear.

Statistical models are typically based on observations of crop growth over large areas in real-world field settings. But the reduced-form relationship between weather variables and yield means the mechanisms driving model results are often unclear and that care should therefore be taken in using results for climate change projections extending beyond the historical record. Despite the fact that both approaches seek to quantify the impacts of climate change on agricultural productivity, there have been relatively few attempts to systematically compare findings. A number of studies have compared process-based and empirical responses for individual crops in individual locations, such as maize and wheat in South Africa or maize in Switzerland . At the global level, Liu et al provide a systematic comparison of the temperature response of wheat yields estimated from regression models, up scaling point estimates from an ensemble of process-based models, and gridded process-based models, generally finding only small differences between the three methods. In this special issue, Lobell and Asseng compare individual published estimates of crop sensitivities to climate impacts from process-based and empirical yield models, finding little difference in the temperature response. Roudier et al , Knox et al , and Knox et al are perhaps most similar to the analysis presented here. These papers present meta-analyses of yield impacts for multiple crops in specific regions and report average differences between process-based and empirical estimates. All papers find that the effect of impact estimation technique is small relative to other sources of variation. Our approach adds to these studies firstly by performing a global analysis and secondly by using a multivariate regression for our meta-analysis, instead of simply splitting the sample of studies. This multivariate approach allows us both to control for potential confounding variables and to estimate continuous response functions that can be globally extrapolated in order to inform an economic analysis. In the agricultural sector, the effect of climate change-induced yield shocks on more policy-relevant variables such as prices, consumption, food-security, and economic welfare will be mediated by the global trade in agricultural commodities and will depend on terms-of-trade effects and the interaction of climate change impacts with existing market distortions . Though several papers have incorporated climate productivity shocks into partial- and general-equilibrium models very few report welfare changes . Understanding the welfare impacts of climate change-induced productivity shocks on agriculture is important both for policy and because the simple integrated assessment models used to calculate the social cost of carbon use damage functions that parameterize changes in economic welfare with temperature. Current agricultural damage functions in these IAMs use studies from the early-to-mid 1990s that are now dated and largely obsolete . This paper contributes to the existing literature in two ways. It is the first systematic, multi-crop, global comparison between empirical and process-based crop models. Although the distinction between these approaches has been widely discussed, this paper quantifies this difference at the global scale and puts it in the context of other uncertainties in future climate change impacts, such as how quickly farmers are able to adapt to climate change. In addition, we advance the literature by examining not just yields but also economic welfare by incorporating estimated yield changes into the Global Trade Analysis Project CGE model, thereby producing results ready to inform IAM damage functions.The basis of the yield-temperature response functions in this paper is a database of studies estimating the climate change impact on yield compiled for the IPCC 5th Assessment Report , also described in a meta-analysis by Challinor et al . This database contains over 1700 point estimates of the impacts of changes in temperature, rainfall, and CO2 concentrations on the yield of 17 different crops compiled from 94 different studies.