Crop Production Management Research Articles

Management of agricultural crops production depending on land quality and intensification factors

The article analyzes the methods of managing the production of agricultural products based on the quality of the land and the intensification factors. The purpose of this article is to determine the main factors that affect the production of agricultural products, develop recommendations to improve the management and analysis of crop production. The objectives of the study are: identification of the main factors that affect the efficiency of production of agricultural products; development of recommendations for the formation of self-sufficient segments of crop production in agricultural organizations and evaluation of the effectiveness of their operation; determination of an algorithm for the analysis and identification of deviations in crop yields due to intensification factors, organizational and management measures and land quality (soil) of cultivated areas. The introduction and use of economic methods to manage agricultural production requires the restructuring and formation of self-sufficient activity segments in medium and large agricultural organizations, which must operate on the principles of independence in matters of control, administration and evaluation of the recovery of the investment. The model (scheme) for the management of the self-sufficient segments of crop production is substantiated, the internal calculation prices are determined to evaluate the products produced by these segments and the net operating income of their activities.

Water Management of Czech Crop Production in 1961–2019

This study aims to evaluate the water balance of the crop mix of the Czech Republic and the tendencies of its development during the period 1961–2019. For calculating water deficits, methodology from ČSN 750434 (Czech technical standards) was used and on its basis, the deficits of the ten most frequently represented crops of the Czech Republic were calculated. These results were then put into the context of the development of precipitation totals and the development of average annual temperatures in the observed period. Furthermore, statistical tools were used for the identification of relationships between the observed variables and the tested hypotheses to verify the statistical significance of the observed changes. The results show that the overall irrigation deficit nearly doubled in Czech agriculture when comparing the averages for the periods 1961–1970 and 2010–2019. This change was evaluated as statistically significant. Furthermore, there were also statistically significant increases in water deficits in the cases of wheat, barley, rye, oats, legumes, and rapeseed. The sowing areas of the observed crops recorded statistically significant change in all cases. Only in the case of wheat, maize and rapeseed were there increases in sowing area, specifically 146%, 642.4%, and 1132.7%, respectively. For other crops, a decrease in sowing areas was observed. This finding points to decreasing commodity diversity in Czech agriculture, which, in combination with a high degree of intensification and selected agrotechnical practices, contributes to a lower retention capacity for the soil and landscape to retain water, which in turn influences the overall water balance of the Czech agrarian sector.

Factors Affecting Use Levels of Farmyard Manure in Vihiga County, Kenya

Several studies have been done to determine factors affecting adoption of new technologies. However, few or no studies have been carried out to determine factors believed to affect use level of recommended technology packages. Majority of farmers in the study area had used farmyard manure in crop production. Framers however used suboptimal levels resulting in low crop yields. This happens despite the fact that farmers are aware of the recommended levels needed for appreciable yields to be realized. Studies need to be done to determine why farmers use suboptimal levels of farmyard materials in the study area. The study analyzed the factors believed to affect intensity of use of farmyard fertilizers with the aim of identifying the role each factor plays in determining level of use of farmyard fertilizers among smallholder farmers. Stratified random sampling was used to select respondents. A total of 120 farmers were selected to provide data needed for the study. Data was collected from three out of six divisions of Vihiga County. At least 30 farmers were selected from each division. Primary data was collected using questionnaires and data collected was analyzed using regression analysis. The results of the analysis indicated that a number of factors were significant in determining use levels of farmyard manure. Out of the ten factors believed to affect use intensity of farmyard manure, five were statistically significant at 95% confidence level. The results of the regression analysis showed that education level of decision-maker, farm size, income of household, availability of labor and access to manure were significant at 95% level in explaining use intensity of farmyard manure in the study area. The study recommended that factors affecting use levels of farm yard manure be addressed with the aim improving the amounts used in order to bridge the gap between the current use level and the recommended level of farmyard fertilizers among smallholder farmers. Keywords: Factors affecting, Use Levels, Farmyard Manure, Kenya DOI: 10.7176/JESD/12-24-05 Publication date: December 31 st 2021

Wheat (Triticum aestivum) adaptability evaluation in a semi-arid region of Central Morocco using APSIM model

In this study, we evaluated the suitability of semi-arid region of Central Morocco for wheat production using Agricultural Production Systems sIMulator (APSIM) considering weather, soil properties and crop management production factors. Model calibration was carried out using data collected from field trials. A quantitative statistics, i.e., root mean square error (RMSE), Nash–Sutcliffe efficiency (NSE), and index of agreement (d) were used in model performance evaluation. Furthermore, series of simulations were performed to simulate the future scenarios of wheat productivity based on climate projection; the optimum sowing date under water deficit condition and selection of appropriate wheat varieties. The study showed that the performance of the model was fairly accurate as judged by having RMSE = 0.13, NSE = 0.95, and d = 0.98. The realization of future climate data projection and their integration into the APSIM model allowed us to obtain future scenarios of wheat yield that vary between 0 and 2.33 t/ha throughout the study period. The simulated result confirmed that the yield obtained from plots seeded between 25 October and 25 November was higher than that of sown until 05 January. From the several varieties tested, Hartog, Sunstate, Wollaroi, Batten and Sapphire were yielded comparatively higher than the locale variety Marzak. In conclusion, APSIM-Wheat model could be used as a promising tool to identify the best management practices such as determining the sowing date and selection of crop variety based on the length of the crop cycle for adapting and mitigating climate change.

Nitrogen losses from food production in the North China Plain: A case study for Quzhou

Nitrogen (N) management is essential for food security. The North China Plain is an important food producing region, but also a hotspot of N losses to the environment. This results in water, soil, and air pollution. In this study, we aim to quantify the relative contribution of different crops and animals to N losses, by taking the Quzhou county as a typical example in the North China Plain. We developed and applied a new version of the NUtrient flows in Food chains, Environment, and Resource use (NUFER) model. Our model is based on updated information for N losses in Quzhou. Our results show that N losses to the environment from crop and animal production in Quzhou were approximately 9 kton in 2017. These high N losses can be explained by the low N use efficiency in food production because of poor N management. For crop production, wheat, maize, and vegetables contributed 80% to N losses. Ammonia emissions and N leaching have dominant shares in these N losses. Pigs and laying hens were responsible for 74% of N losses from animal production. Ammonia emissions to air and direct discharges of manure to water were the main contributors to these N losses. Effective reduction of N losses requires improving the nutrient management in crop (wheat, maize, vegetables) and animal (pigs, laying hens) production. Our work could support the Agricultural Green Development in the North China Plain.

Large spatial variation and stagnation of cropland gross primary production increases the challenges of sustainable grain production and food security in China

Sustainable crop grain production and food security is a grand societal challenge. Substantial investments in China's agriculture have been made in the past decades, but our knowledge on cropland gross primary production in China remains limited. Here we analyzed gross primary production (GPP), solar-induced chlorophyll fluorescence (SIF), terrestrial water storage, crop grain production, and agricultural investment and policy during 2000–2018. We found that based on croplands in 2000, approximately 52 × 106 ha (~37%) had continuous increasing trends in GPP during 2000–2018, which were mainly located in northern China. GPP for 63% of croplands was stagnant, declined, or had no significant change. At the national scale, annual cropland GPP increased during 2000–2008 but became stagnant in 2009–2018, which was inconsistent with the interannual trend in the crop grain production data for 2009–2018. The spatial mismatch between crop production and water availability became worse. The major grain exporting provinces, mostly located in water-stressed regions, experienced increased water resource constraints, which posed a challenge for sustainable grain production. The stagnant cropland GPP and increasing water resource constraints highlight the urgent need for sustainable management for crop production and food security in China.

Wheat yield prediction in relation to climatic parameters using statistical model for Ludhiana district of central Punjab


 Climate change which is one of the main determinants of agricultural production has started affecting the crop growth pattern and yield from past couple of decades in various agro-climatic zones globally. Under such scenario, the prior forecasting of yield of field crops such as wheat via modeling techniques can help in simplifying the crop production management system starting from farmer’s level to policy makers. The present study was thus undertaken to model the wheat yield of Ludhiana district of Indian Punjab through regression analysis of historical data (1993-2017) of wheat yield and climatic conditions in the area. The developed model was successfully validated with a strong positive correlation (R2=0.81) between predicted and observed data. Both observed and predicted yields were having similar trend with a minimum and maximum absolute differential error of 0.1 and 13.9% respectively. The developed model may serve as a powerful tool for predicting the future yield of wheat crop with available futuristic climatic data of the study area.

Simulation of yield and water balance using WHCNS and APSIM combined with geostatistics across a heterogeneous field

Estimating water balance is the foundation of improving water productivity (WP) and managing crop production efficiently in fields with significant spatial variability of soil properties. Agricultural system models are useful tools to simulate crop yield, WP, and water balance; however, they are rarely focused on geostatistical characteristics on the spatial scale. If agricultural system models are used spatially, it is important to consider whether the geostatistical characteristics of the simulations are similar to those based on measured data. In this study, two process-based models, the widely-used Agricultural Production Systems sIMulator (APSIM) and the newly-developed soil Water Heat Carbon Nitrogen Simulator (WHCNS), were used to simulate crop yield, water balance, and WP in a 54-ha field with spatially variable soil properties. Performance of the simulations was good for both models; however, the simulation accuracy of WHCNS was higher than for APSIM. Geostatistical characteristics of measured maize yield and final soil water storage (SWSf) were different from the simulated data. The fitted semivariogram models of simulated yield and SWSf had a higher semivariogram range and lower random variation than that of measured data. The fitted semivariogram models and geostatistical characteristics of simulated water balance also varied between the two models. Although the agricultural system models simulated the spatial distribution of variables efficiently, their spatial structure was changed in comparison with the spatial structure of measured data. This would affect the interpolation precision of spatial distribution maps. More work is required on the robustness and prediction accuracy of both models for their implementation in a spatial way.

Effect of alternate partial root-zone drying (PRD) on soil nitrogen availability to alfalfa

Available soil N is critical for N fertilizer management in crop production but the effects of alternate partial root-zone drying (PRD) on the dynamics of N and the impact of these effects on perennial crops such as alfalfa (Medicago sativa L.) have not been evaluated. A field experiment was conducted to compare two irrigation modes (PRD and conventional furrow irrigation (CFI)) in combination with four irrigation levels (70% ETc (evapotranspiration), 85% ETc, 100% ETc, and 115% ETc). PRD improved the mineralization of organic matter to increase available N and the NO3−-N/NH4+-N ratio. Thus, it increased N uptake, water productivity and maintained seasonal forage yield. The combination of PRD and irrigation level (100% Etc) produced maximum NO3−-N stock. These results present an evaluation of how PRD influences soil N availability to N-fixing perennial crops.

ОСОБЕННОСТИ БУХГАЛТЕРСКОГО И УПРАВЛЕНЧЕСКОГО УЧЕТА СЕЛЬСКОХОЗЯЙСТВЕННОГО ПРОИЗВОДСТВА

The article examines the features of accounting, as well as management accounting at agricultural enterprises. The specific features of agriculture that affect the formulation of accounting and management accounting are considered. The article reveals the issues of the strategy of agricultural development based on the strategy of cost leadership, the essence of which is to achieve competitive advantages due to low costs for certain types of products and, accordingly, lower cost compared to competitors. The analysis of the opinions of researchers on this issue is carried out. The article attempts to systematize the issues of cost accounting and the author puts forward the hypothesis that according to organizational and economic characteristics, the optimal object of management and cost accounting in crop production is a team, and in animal husbandry-a farm that has a manager who has all the rights and powers.

50 years of continuous no-tillage, stubble retention and nitrogen fertilization enhanced macro-aggregate formation and stabilisation in a Vertisol

Farm management practices such as no-tillage, stubble retention, and nitrogen (N) application have a substantial potential to enhance soil aggregation in agricultural soils. This is especially important for Vertisols, which may experience extended waterlogging due to low infiltration rates and low hydraulic conductivity. We evaluated the long-term impacts of the tillage method (no tillage, NT, and conventional tillage, CT), stubble management practices (stubble retention, SR, or stubble burning, SB) and N-fertilization on aggregate size distribution, and distribution of total C and N within different aggregate-size fractions in a Vertisol. Soil samples were collected from 0–0.1 m depth at the Hermitage Research Station in Queensland, Australia, after 50 years of management. We found that the soil under NT with SR and 90 kg N fertilizer application (NTSRN90) contained the highest proportion of large macro-aggregates (31.0 %), the greatest mean weight diameter (MWD) (1.79 mm), and the highest percentage of water stable aggregates (WSA, 90.2 %), whilst the lowest values (3.15 %, 0.71 mm and 72.7 %, respectively) were found in the CTSBN0 treatment. Therefore, reducing soil disturbance by primary tillage operations (NT) and the retention of crop residues (SR) and fertilizer application stimulated the formation of soil macro-aggregates from micro-aggregates. Retention of higher amounts of residues to the system in the SR and N fertilized treatments increased the macro-aggregate occluded particulate organic C (POC) and N (PON), which likely provided loci for aggregate formation and binding agents for stabilisation. Larger aggregates stored more organic C and N, and had lower water retention at saturation (−10 kPa), than smaller aggregates or the silt + clay fraction, thus having the potential to reduce the frequency of waterlogging in the soil under NT, SR, and N fertilization. Therefore, long-term practice of no-tillage along with stubble retention and N fertilisation could significantly enhance the macro-aggregation of fine-textured Vertisols, with this then ensuring the sustainability of the agricultural production system by creating favourable water retention and movement, and porosity for better management for crop production.

Cotton Stand Counting from Unmanned Aerial System Imagery Using MobileNet and CenterNet Deep Learning Models

An accurate stand count is a prerequisite to determining the emergence rate, assessing seedling vigor, and facilitating site-specific management for optimal crop production. Traditional manual counting methods in stand assessment are labor intensive and time consuming for large-scale breeding programs or production field operations. This study aimed to apply two deep learning models, the MobileNet and CenterNet, to detect and count cotton plants at the seedling stage with unmanned aerial system (UAS) images. These models were trained with two datasets containing 400 and 900 images with variations in plant size and soil background brightness. The performance of these models was assessed with two testing datasets of different dimensions, testing dataset 1 with 300 by 400 pixels and testing dataset 2 with 250 by 1200 pixels. The model validation results showed that the mean average precision (mAP) and average recall (AR) were 79% and 73% for the CenterNet model, and 86% and 72% for the MobileNet model with 900 training images. The accuracy of cotton plant detection and counting was higher with testing dataset 1 for both CenterNet and MobileNet models. The results showed that the CenterNet model had a better overall performance for cotton plant detection and counting with 900 training images. The results also indicated that more training images are required when applying object detection models on images with different dimensions from training datasets. The mean absolute percentage error (MAPE), coefficient of determination (R2), and the root mean squared error (RMSE) values of the cotton plant counting were 0.07%, 0.98 and 0.37, respectively, with testing dataset 1 for the CenterNet model with 900 training images. Both MobileNet and CenterNet models have the potential to accurately and timely detect and count cotton plants based on high-resolution UAS images at the seedling stage. This study provides valuable information for selecting the right deep learning tools and the appropriate number of training images for object detection projects in agricultural applications.

Versatile Applications of the CRISPR/Cas Toolkit in Plant Pathology and Disease Management

New tools and advanced technologies have played key roles in facilitating basic research in plant pathology and practical approaches for disease management and crop health. Recently, the CRISPR/Cas (clustered regularly interspersed short palindromic repeats/CRISPR-associated) system has emerged as a powerful and versatile tool for genome editing and other molecular applications. This review aims to introduce and highlight the CRISPR/Cas toolkit and its current and future impact on plant pathology and disease management. We will cover the rapidly expanding horizon of various CRISPR/Cas applications in the basic study of plant-pathogen interactions, genome engineering of plant disease resistance, and molecular diagnosis of diverse pathogens. Using the citrus greening disease as an example, various CRISPR/Cas-enabled strategies are presented to precisely edit the host genome for disease resistance, to rapidly detect the pathogen for disease management, and to potentially use gene drive for insect population control. At the cutting edge of nucleic acid manipulation and detection, the CRISPR/Cas toolkit will accelerate plant breeding and reshape crop production and disease management as we face the challenges of 21st century agriculture.

Feature Fusion Approach for Temporal Land Use Mapping in Complex Agricultural Areas

Accurate temporal land use mapping provides important and timely information for decision making for large-scale management of land and crop production. At present, temporal land cover and crop classifications within a study area have neglected the differences between subregions. In this paper, we propose a classification rule by integrating the terrain, time series characteristics, priority, and seasonality (TTPSR) with Sentinel-2 satellite imagery. Based on the time series of Normalized Difference Water Index (NDWI) and Vegetation Index (NDVI), a dynamic decision tree for forests, cultivation, urban, and water was created in Google Earth Engine (GEE) for each subregion to extract cultivated land. Then, with or without this cultivated land mask data, the original classification results for each subregion were completed based on composite image acquisition with five vegetation indices using Random Forest. During the post-reclassification process, a 4-bit coding rule based on terrain, type, seasonal rhythm, and priority was generated by analyzing the characteristics of the original results. Finally, statistical results and temporal mapping were processed. The results showed that feature importance was dominated by B2, NDWI, RENDVI, B11, and B12 over winter, and B11, B12, NDBI, B2, and B8A over summer. Meanwhile, the cultivated land mask improved the overall accuracy for multicategories (seven to eight and nine to 13 during winter and summer, respectively) in each subregion, with average ranges in the overall accuracy for winter and summer of 0.857–0.935 and 0.873–0.963, respectively, and kappa coefficients of 0.803–0.902 and 0.835–0.950, respectively. The analysis of the above results and the comparison with resampling plots identified various sources of error for classification accuracy, including spectral differences, degree of field fragmentation, and planting complexity. The results demonstrated the capability of the TTPSR rule in temporal land use mapping, especially with regard to complex crops classification and automated post-processing, thereby providing a viable option for large-scale land use mapping.

The economic value of sustainable soil management in arable farming systems – A conceptual framework

Soil quality is an important determinant of agricultural productivity, farm resilience and environmental quality. Despite its importance, the incorporation of sustainable soil management in economic models is lacking. This study approaches farmers as decision makers on soil management. Sustainable soil management may be an investment that goes at the expense of short-term returns but increases future soil quality. Hence, the key problem is economic: establishing long-term sustainable soil management at a minimized loss of income. In this study, we define the Economic Value of Land (EVL) as the cumulative returns of a piece of land over a period in time. Maximum long-term EVL is obtained if a soil’s potential is maximally utilized in a sustainable way. From this follows that the Economic Value of Sustainable soil Management (EVSM) is defined as the difference between a sustainable and unsustainable EVL. To acquire a fundamental understanding of EVSM, agronomic and technical factors must be integrated with economics. Production management, the complete set of physical and non-physical inputs is the primary determinant of future soil quality and hence EVL. Maximizing EVL first requires a fundamental understanding of soil quality management: What are the properties of soil quality and how are these influenced by crop production? Subsequently, production management has to be organized in such a way EVL is maximized. This study provides an overview of soil quality management and crop production management linked to economics. The framework provides a qualitative blueprint for bio-economic modelling and a basis for policies to enhance sustainable soil management.

Insecticidal potentials of dry powder and solvent extracts of Tithonia diversifolia (Hemsl.) A. Gray flower against rice meal moth, Corcyra cephalonica (Stainton)

Insecticidal potentials of dry powder and solvent extracts of Tithonia diversifolia (Hemsl.) A. Gray flower against rice meal moth, Corcyra cephalonica (Stainton)

Evaluating management strategies for sustainable crop production under changing climate conditions: A system dynamics approach

The increasing frequency and severity of drought pose significant threats to sustainable agricultural production across the world. Managing drought risks is challenging given the complexity of the interdependencies and feedback between climate drivers and socio-economic and ecological systems. To better understand the dynamics that drive the impacts of drought and water scarcity on crop production, a system dynamics model has been developed to explore complex interactions between factors in associated with drought and agricultural production, and examine how these might impact agricultural sustainability, using a case study in a coffee production system in Viet Nam. The model shows that water- and land-use drivers and their interactions with ecological and socio-economic factors play a more significant role than drought in determining the sustainability of coffee production. Results of policy scenario analyses indicate that drought conditions might exacerbate problems related to water shortages for irrigation but their impacts could be substantially minimized through applying intervention strategies, including restriction of the total area of land available for coffee production (to ~ 190,000 ha) and a 25% reduction in the irrigation amount per hectare of coffee compared to the common practices. Overall, the model findings add significant insight into drought and water resources management for sustainable crop production and the developed model can serve as a decision-support tool to inform strategic policy-making.

Influence of Peanut, Sorghum, and Soil Salinity on Microbial Community Composition in Interspecific Interaction Zone.

Soil microorganisms play important roles in crop production and sustainable agricultural management. However, soil conditions and crop selection are key determining factors for soil microbial communities. This study investigated the effect of plant types and soil salinity on the microbial community of interspecific interaction zone (II) based on the sorghum/peanut intercropping system. Microbial community diversity and composition were determined through PacBio single molecule, real-time sequencing of 16S rDNA and internal transcribed spacer (ITS) genes. Results showed Proteobacteria, Bacteroidota, and Acidobacteriota to be the dominant bacterial phyla in IP, II, and IS, whereas Ascomycota, Basidiomycota, and Mucoromycota were the dominant fungal phyla. Under salt-treated soil conditions, the plants-specific response altered the composition of the microbial community (diversity and abundance). Additionally, the interspecific interactions were also helpful for maintaining the stability and ecological functions of microbial communities by restructuring the otherwise stable core microbiome. The phylogenetic structure of the bacterial community was greatly similar between IP and II while that of the fungal community was greatly similar between IP and IS; however, the phylogenetic distance between IP and IS increased remarkably upon salinity stress. Overall, salinity was a dominant factor shaping the microbial community structure, although plants could also shape the rhizosphere microenvironment by host specificity when subjected to environmental stresses. In particular, peanut still exerted a greater influence on the microbial community of the interaction zone than sorghum.

Assessing the frequency of drought/flood severity in the Luvuvhu River catchment, Limpopo Province, South Africa

The Luvuvhu River catchment experiences rainfall variability with a high frequency of extremely dry and wet conditions. Understanding the frequency of drought and floods in this catchment area is important to the agriculture sector for managing the negative impacts of these natural hazards. This study was undertaken to investigate the frequency and severity of drought/floods and linkages with the El Niño Southern Oscillation (ENSO) phenomenon. Poor and resource-limited small-scale farmers in the Luvuvhu River catchment area struggle to adjust due to decreasing crop yields and livestock mortality caused by drought and floods. Monthly rainfall data from 15 grid points (0.5° × 0.5°) was used to compute the Standardised Precipitation Index (SPI) for the period between 1979 and 2016. The 3-month SPI was calculated for the December–January–February (DJF) period. The second half of the agricultural season was selected because the influence of ENSO is high during the late summer season (DJF) in the catchment. The SPI results indicate that the agricultural seasons 1982/83, 1991/92 and 2015/16 were characterised by extreme drought. Conversely, the SPI values also show that the wettest seasons were recorded in 1998/99 and 1999/00. The catchment experiences a high frequency of moderate to severe drought in the north and north-eastern parts. Spatially, the occurrence of moderate to severe dry conditions covers large areas in the north and south-western parts. Severe to extreme wet conditions cover large areas in the north and south-eastern parts of the catchment. The SST index (Niño 3.4) shows a strong influence on rainfall variability in the catchment, resulting in either dry or wet conditions. Therefore, this study recommends further research focusing on more climatic modes that influence rainfall variability, as well as further development of drought and flood forecasting to improve farmers’ adaptations options and reliability of weather forecasts used as a tool to manage crop production.

Long term crop rotation effect on subsequent soybean yield explained by soil and root-associated microbiomes and soil health indicators

Crop rotation is an important management tactic that farmers use to manage crop production and reduce pests and diseases. Long-term crop rotations may select groups of microbes that form beneficial or pathogenic associations with the following crops, which could explain observed crop yield differences with different crop sequences. To test this hypothesis, we used two locations each with four long-term (12–14-year), replicated, rotation treatments: continuous corn (CCC), corn/corn/soybean (SCC), corn/soybean (CSC), and soybean/corn (SCS). Afterwards, soybean was planted, and yield and soil health indicators, bulk soil microbiome, and soybean root-associated microbiome were assessed. Soybean yields, as well as soil protein, and POXC as soil health indicators were higher following CCC than in the other three treatments at both locations. A bacterial taxon in family JG30-KF-AS9 was enriched in CCC, whereas Microvirga, Rhodomicrobium, and Micromonosporaceae were enriched in SCS. Several ascomycetes explain lowered yield as soybean pathogens in SCS. Surprisingly, Tumularia, Pyrenochaetopsis and Schizothecium were enriched in soybean roots after CCC, suggesting corn pathogens colonizing soybean roots as nonpathogens. Our finding of associations between soil health indicators related to microbiomes and soybean yield has wide-ranging implications, opening the possibility of manipulating microbiomes to improve crop yield potential.