Next Article in Journal
The Central Role of Soil Organic Matter in Soil Fertility and Carbon Storage
Next Article in Special Issue
Conservation Agriculture as a Sustainable System for Soil Health: A Review
Previous Article in Journal
Effects of Elemental Sulfur on Soil pH and Growth of Saskatoon Berry (Amelanchier alnifolia) and Beaked Hazelnut (Corylus cornuta) Seedlings
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Soil Systems
Volume 6
Issue 2
10.3390/soilsystems6020032
soilsystems-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Assessment of the Interrelationships of Soil Nutrient Balances with the Agricultural Soil Emissions and Food Production

by
Vítor João Pereira Domingues Martinho
1,*,
José L. S. Pereira
2,3 and
José Manuel Gonçalves
4
1
Agrarian School (ESAV) and CERNAS-IPV Research Centre, Polytechnic Institute of Viseu (IPV), 3504-510 Viseu, Portugal
2
Agrarian School (ESAV), Polytechnic Institute of Viseu (IPV), Quinta da Alagoa, 3500-606 Viseu, Portugal
3
Centre for the Research and Technology of Agro-Environmental and Biological Sciences (CITAB), Inov4Agro, University of Trás-os-Montes and Alto Douro, Quinta de Prados, 5000-801 Vila Real, Portugal
4
Agrarian School (ESAC), Polytechnic Institute of Coimbra (IPC), Bencanta, 3045-601 Coimbra, Portugal
*
Author to whom correspondence should be addressed.
Soil Syst. 2022, 6(2), 32; https://doi.org/10.3390/soilsystems6020032
Submission received: 18 January 2022 / Revised: 23 March 2022 / Accepted: 29 March 2022 / Published: 31 March 2022
(This article belongs to the Special Issue Integrated Soil Management: Food Supply, Environmental Impacts, and Socioeconomic Functions)
Browse Figures
Versions Notes

Abstract

:
Sustainable and adjusted soil management practices are crucial for soil quality, namely in terms of the nutrient budget. On the other hand, soil characteristics are interlinked with agricultural sustainability and food supply. In other words, soil quality influences agricultural performance and food chains, but it is also impacted by agricultural activities. In this context, this research aims to evaluate the spatial correlations of the soil nutrient balance around the world and analyse how this variable is interrelated with agricultural soil emissions, agricultural output, and food supply. To achieve these goals, data from the FAOSTAT database were considered. This statistical information was analysed with spatial autocorrelation approaches to identify spatial clusters around the world that can be considered as a basis for designing common policies. To perform panel data regressions to identify marginal effects between variables, data were first evaluated using correlation matrices and factor analysis. The results highlight that there is space for common strategies worldwide to preserve soil quality, as in some parts of the world the problems are similar. In these frameworks, the international organizations may have a determinant contribution.

1. Introduction

Information about land characteristics is an important factor in integrated soil management and here, beyond the scientific contributions [1], knowledge of local populations about the soil properties provides relevant contributions [2]. Adjusted management plans may make local needs compatible with soil quality conservation [3], where agricultural practices determine the results obtained [4] in terms of sustainability, along with the farming systems adopted [5] and the crops species [6]. Sustainable practices differ for each agricultural activity and also between countries and regions [7].
Different tillage, fertilisation techniques and rotation approaches are agronomic practices that may make a difference in the quality of the soil [8], in rice-wheat systems for example. Soil conservation techniques are often interrelated with water management approaches [9], because the dynamics of these two resources (soil and water) are mutually dependent [10]. Soil quality also impacts the characteristics of the crops obtained [11] and the health of animal activities [12].
To promote sustainable and best management practices in the agricultural sector, with benefits for the environment and soil quality, farmers need to be supported with technical knowledge, and in these conditions, extension services are crucial [13], as well as training programs to increase the technical skills of stakeholders related with the farming activities. The agricultural institutions (national and international), organizations (cooperatives and associations, for example) and policies (Common Agricultural Policy in European Union, for instance) are crucial in order to achieve sustainable development goals [14].
The new challenges created by the world population growth and the needs of dealing with the climate change contexts call for alternative ways of better managing the available resources [15], specifically in contexts of agricultural intensification [16] and soil erosion [17]. In these frameworks, the agricultural activities are sources and sinks of greenhouse gases, where the soil carbon sequestration is fundamental for the sustainability [18]. The soil degradation and erosion are threats that particularly concern the several society stakeholders [19].
Considering these issues of the soil management and its interrelationships with the several dimensions of the agricultural sector, this study intends to analyse the soil nutrient balance worldwide through spatial assessments to identify clusters between the countries. These analyses will be a basis for the design of joint policies and combining efforts for together solve common problems related with the soil quality. In addition, this research aims to assess the main interlinkages between the soil nutrient budget and the soil emissions and the food supply.

2. Literature Survey

Agricultural soils are impacted by the agronomic practices adopted by the farmers, where the tillage, for example, has its influence in the physical properties [20] and quality [21]. Hence, this must be considered by several stakeholders, particularly farmers and policymakers. Conventional tillage may reduce the soil organic matter and increase the carbon dioxide (CO2) emissions [22] by soil respiration. Minimum tillage is suggested to achieve the compromise among the agricultural productions loss and the soil preservation [23]. Specifically, soil erosion is comparable to the water erosion [24]. Soil and water dynamics are correlated [25], wherein the formation of the organic matter is a complex process dependent from diverse drivers [26], such as soil temperature and humidity and carbon/nitrogen ratio of the manures.
Other farming practices have their impacts on the soil characteristics and composition, such as compost or manure application (with benefits for the agricultural activities, but with changes in the microbial community) [27], organic/conventional productions [28], organic/inorganic fertilisers [29], pasture in rotation [30], agrochemicals (affects the bacterial diversity [31], for instance) [32], plastic mulching [33], land use changes [34], straw return [35], soil fumigation [36], harvest practices [37], forest-agriculture conversion [38], conventional practices [39], field fallow [40], polymers use [41] and cover crops [42].
The soil quality is influenced by several factors, some of them from extreme phenomena [43] and the climate changes [44], nonetheless the various dimensions associated with the farming contexts explain a part of the sources of problems that bring degradation of the land, specifically those associated with salinity [45].
Soil is a key factor of production for the agricultural sector [46] and food supply [47], however, it is under pressures by the economic activities [48]. A permanent assessment of the soil quality (mainly the soil physical properties [49]) through new techniques [50], approaches [51] and technologies [52] is crucial for an adjusted soil management [53]. Namely, to maintain the levels of carbon and nitrogen through conservation practices [54] and preserve the human health [55] from toxic contaminants [56], including phthalate esters [57], heavy metals (with impacts on food safety [58]) [59] and copper balance [60]. For these evaluations, the availability of information [61] worldwide [62] is fundamental. The assessment of soil quality is also important to support strategy proposals [63] and characteristics prediction [64] under the global warming challenges [65].
The agricultural soil management is responsible for greenhouse gas emission [66], with several environmental impacts originating in the following gases: nitrous oxide (N2O) [67] by nitrification and denitrification processes, methane (CH4) by anaerobic conditions and CO2 by aerobic or anaerobic environment. These greenhouse gas emissions are particularly influenced by soil type, climate, water management and composition of organic matter [68]. Hence, the agricultural soil management is interrelated with the agricultural practices and environmental impacts [69]. Thus, the interlinkages have impacts, for example, on the ecosystems services [70], soil biodiversity [71] and humus composition [72]. For example, the use of biochar into the soil may be an interesting alternative to reduce the environmental impacts and mitigate the climate change consequences [73]. Additionally, adjusted soil management may prevent soilborne diseases [74] and increase the soil organic carbon [75].
For a sustainable agricultural soil management, the agricultural policies and institutions are called to play relevant roles [76] to promote soil conservation practices [77]. This issue is particularly important in the European Union contexts, under the framework of the Common Agricultural Policy (CAP) [14], and to deal with problematic cases created by the post-Second World War contexts [78]. The public policies are specifically important in the cases where the negative impacts are self-reinforced or have dynamics of rebound effects [79].

3. Materials and Methods

To achieve the objectives proposed and considering the several relationships associated with the soil properties highlighted in the literature review, statistical information for the following variables was obtained from the FAOSTAT [80] database: agricultural soil emissions (CO2eq, namely N2O emissions.) in kilotonnes per ha of cropland; average value of food production (constant 2004–2006 I$ (international dollar, an international dollar would buy in a country a comparable amount of products a U.S. dollar would buy in the United States [81])/cap, 3-year average); gross agricultural production value per ha (constant 2014–2016, 1000 I$ per ha of cropland); and cropland nutrient flow per unit area (kg per ha). These variables were selected to represent the characteristics of the soil and their different interlinkages, namely those related with the environment, agricultural production, and food supply. Considering the availability of data for the various variables, it was considered the period 2001–2017. To associate the average valued of food production with the other indicators, the middle year for each group of three years was considered.
These indicators were first analysed through spatial autocorrelation, to identify spatial clusters worldwide, where it may be possible to design common strategies to deal with an integrated agricultural soil management. For the spatial assessments, global and local autocorrelation approaches were considered following GeoDa procedures [82,83]. For the global spatial autocorrelation, the Moran’s I statistics were used [84]. The Moran’s I statistics range between −1 and 0, for negative spatial autocorrelation (the values of a variable are negatively correlated with the values of the same variable in the neighbour countries), and 0 and 1, for positive autocorrelation. For the local spatial autocorrelation, cluster maps were considered. In these maps, the clusters high-high and low-low highlight positive local spatial autocorrelation for higher and lower values, respectively. The clusters high-low and low-high represent negative spatial autocorrelation. For this spatial analysis, shapefiles from the Eurostat [85] for the world countries were used that were explored through the QGIS software [86].
After this first assessment, the variables were considered to obtain indices for the integrated agricultural soil management through factor analysis [87,88,89,90,91] and to find marginal effects based on panel data regressions [91,92,93]. To identify the best models for the panel data regressions, the Spearman correlations [94] and the Granger cause statistics [95] were carried out.

4. Spatial Autocorrelation Analysis

The spatial autocorrelation analysis reported in this section was assessed using queen contiguity matrix, for an order of contiguity of 1. Figure 1, Figure 2, Figure 3 and Figure 4 show the level of global and local spatial autocorrelation and the distribution of values of the several variables considered worldwide.
The global and local spatial autocorrelation was weak for the agricultural soil emissions (CO2eq) per ha of cropland, and this was a consequence of values relatively low (exception for the case of New Zealand, for example) verified for this variable across the world countries (Figure 1).
As can be observed in Figure 2, the scenario was different for the average value of food production, where there are signs of relevant positive global spatial autocorrelation and high-high local spatial autocorrelation in North and South America, Russia, and some European countries. Hence, this means that the strategies developed by the countries inside of each cluster high-high spread among neighbour countries, lead to good findings for future policies.
The gross agricultural production value per ha of cropland was, in general, low worldwide (exception for New Zealand and some European countries, for example), and this explains, at least in part, the reduced level of spatial autocorrelation for this variable (Figure 3). The cropland nutrient flow per unit area had significant signs of positive high-high local spatial autocorrelation in the European countries (Figure 4).

5. Identifying Indices for an Integrated Agricultural Soil Management

To facilitate the readability of the results presented here, and improve the robustness of the findings, it was obtained a balanced panel data (in which the countries and years with missing values were removed, remaining 183 countries with data for the full period of 2001–2017) and the agricultural soil emissions were converted from kilotonnes per ha into kg per ha and the gross agricultural production from 1000 I$ per ha into I$ per ha.
Table 1 highlights that the stronger correlations are between the agricultural soil emissions per ha, the gross agricultural production per ha and the cropland nutrient flow per ha. There was also strong correlation among the cropland nutrient flow per ha and the gross agricultural production per ha.
As can be observed in Table 2, it was intended to obtain an integrated agricultural soil management index, through factor analysis, with the most correlated variables. The agricultural soil emissions per ha were not considered in the factor analysis, because it was expected to contribute for the soil sustainability in a different way of the gross agricultural production and the cropland nutrient flow. Hence, the consideration of these three variables (agricultural soil emissions, gross agricultural production and cropland nutrient flow) in the index hampers the interpretation of its results. Thus, the selection of the variables reported in this study considered the objectives proposed (analyse how soil nutrient balance is interrelated with agricultural soil emissions, agricultural output and food supply), nonetheless in future studies could be interesting to benchmark these results with those obtained considering other variables.
Table 3 shows the top 10 countries for the integrated agricultural soil management index and highlights that the countries with higher gross agricultural production per ha, cropland nutrient flow per ha and consequent greater agricultural soil emissions per ha are not the same with better food supply per person.

6. Panel Data Regressions

The Granger causality tests highlight that the cropland nutrient flow per ha impacts the agricultural soil emissions per ha of cropland and the gross agricultural production per ha of cropland. Based on these findings, on the assessments carried out before and on the literature review, the results presented in Table 4 and Table 5 were obtained.
The results obtained in this study revealed the following statistical problems: cross sectional dependence, heteroscedasticity, and autocorrelation of the data sample. To deal with these frameworks, the Prais–Winsten regression, correlated panels corrected standard errors (PCSEs), following Stata [91] and Torres-Reyna [93] procedures were considered.
These findings reveal that when the cropland nutrient flow increases 1 kg/ha the agricultural soil emissions worldwide increase 41.279 kg/ha and the gross agricultural production increases 25.094 I$ per ha.
These results highlight serious problems of sustainability in the agricultural soil management worldwide because the cropland nutrient flow and the agricultural production are associated with more agricultural soil emissions, but this context is disconnected from the food supply per person.

7. Discussion

This study aimed to analyse the framework of the soil nutrient balances across the world countries and assess their interrelationships with the agricultural soil emissions and the food supply. For that, geographic information system (GIS) approaches were considered, namely, to identify evidence of spatial autocorrelations between the countries for the variables considered. Factor analysis to find indices and panel data regressions to obtain relationships among the variables were also carried out.
The literature review highlighted the impacts on the agricultural soils from the agronomic practices, where the tillage, fertilisation, rotations, and land use changes, for example, have their implications. However, the agricultural soils are also responsible by environmental impacts through the greenhouse gas emissions. Sometimes, these interrelationships create contexts with self-reinforced effects, where the agricultural policies and institutions play a determinant role to reduce the negative externalities.
The spatial autocorrelation analysis shows that the global and local spatial correlations are weak for the agricultural soil emissions, in consequence of relatively values worldwide. For the average value of food production, there are signs positive global and local spatial autocorrelation. These evidences are interesting findings for the several stakeholders, namely for the policymakers, because this means that interventions in countries positively correlated may spread for the neighbours. There are also evidences of positive spatial autocorrelation in some European countries for the cropland nutrient flow per unit area.
A correlation matrix and factor analysis reveal that there are strong correlations between the agricultural soil emissions per ha, gross agricultural production per ha and the cropland nutrient flow per ha. The agricultural soil management was interrelated with agricultural practices and has environmental impacts [66,67,69]. This means that in countries with higher, per unit of area, gross agricultural production, for example, it was expected to find greater agricultural soil emissions and cropland nutrient flow. The regressions with panel data show that there are relevant signs that is the cropland nutrient flow per ha that impacts the agricultural soil emissions per ha and the gross agricultural production per ha. The governments and international organizations may have here important contributions to design policies that encourage adjusted soil management practices that maintain the soil nutrients balances and the agricultural production without compromise the sustainability.

8. Conclusions

There is a great heterogeneity between the countries across the world; however, the clusters found from the spatial autocorrelation analysis, for the food supply and soil nutrient balances, may be relevant findings to support common strategies that promote more sustainable practices. This is particularly important when there are relevant signs that the soil nutrient balances impact the farming production and the agricultural soil emissions. In fact, when the cropland nutrient flow increases 1 kg/ha, the agricultural soil emissions rise 41.279 kg/ha and the gross agricultural production rises 25.094 I$ per ha.
In terms of practical implications, the results obtained in this research highlight that the agricultural soil management is determinant to promote a soil nutrient balance able to maintain or increase the agricultural production to achieve the world demand for food and mitigate the agricultural soil emissions. In these contexts, it is suggested, in terms of policy recommendation, that the public, private, national, and international institutions design policies that mitigate the environmental impacts from the cropland nutrient flow.
For future research, the weak correlation between the food supply per capita, the agricultural production per ha and the soil nutrient flow per ha deserve special attention. In fact, despite the environmental impacts found for the agricultural production, this is not compensated by good indicators for food supply per capita.

Author Contributions

Conceptualization, V.J.P.D.M. and J.L.S.P.; methodology, V.J.P.D.M.; software, V.J.P.D.M.; validation, V.J.P.D.M., J.L.S.P. and J.M.G.; formal analysis, V.J.P.D.M., J.L.S.P. and J.M.G.; investigation, V.J.P.D.M., J.L.S.P. and J.M.G.; data curation, V.J.P.D.M.; writing—original draft preparation, V.J.P.D.M.; writing—review and editing, V.J.P.D.M., J.L.S.P. and J.M.G. All authors have read and agreed to the published version of the manuscript.

Funding

This work is funded by National Funds through the FCT—Foundation for Science and Technology, I.P., under the projects UIDB/00681/2020 and UIDB/04033/2020.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Acknowledgments

Furthermore, we would like to thank the CERNAS Research Centre and the Polytechnic Institute of Viseu for their support.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Kone, B.; Diatta, S.; Sylvester, O.; Yoro, G.; Mameri, C.; Desire, D.D.; Ayemou, A. Estimating the inherent fertility of ferralsol using color. Can. J. Soil Sci. 2009, 89, 331–342. [Google Scholar] [CrossRef]
  2. Barrios, E.; Trejo, M.T. Implications of Local Soil Knowledge for Integrated Soil Management in Latin America. Geoderma 2003, 111, 217–231. [Google Scholar] [CrossRef] [Green Version]
  3. Cardelus, C.L.; Mekonnen, A.B.; Jensen, K.H.; Woods, C.L.; Baez, M.C.; Montufar, M.; Bazany, K.; Tsegay, B.A.; Scull, P.R.; Peck, W.H. Edge Effects and Human Disturbance Influence Soil Physical and Chemical Properties in Sacred Church Forests in Ethiopia. Plant Soil 2020, 453, 329–342. [Google Scholar] [CrossRef]
  4. Carsky, R.J.; Oyewole, B.; Tian, G. Integrated Soil Management for the Savanna Zone of W. Africa: Legume Rotation and Fertilizer, N. Nutr. Cycl. Agroecosyst. 1999, 55, 95–105. [Google Scholar] [CrossRef]
  5. Fujisao, K.; Khanthavong, P.; Oudthachit, S.; Matsumoto, N.; Homma, K.; Asai, H.; Shiraiwa, T. Impacts of the Continuous Maize Cultivation on Soil Properties in Sainyabuli Province, Laos. Sci. Rep. 2020, 10, 11231. [Google Scholar] [CrossRef]
  6. Kintomo, A.A.; Akintoye, H.A.; Alasiri, K.O. Role of Legume Fallow in Intensified Vegetable-Based Systems. Commun. Soil Sci. Plant Anal. 2008, 39, 1261–1268. [Google Scholar] [CrossRef]
  7. Santos, M.; Galindro, A.; Santos, C.; Marta-Costa, A.; Martinho, V. Sustainability Evolution of North and Alentejo Vineyard Regions. Rev. Port. De Estud. Reg. 2019, 50, 49–63. [Google Scholar]
  8. Zhao, Z.; Gao, S.; Lu, C.; Li, X.; Li, F.; Wang, T. Effects of Different Tillage and Fertilization Management Practices on Soil Organic Carbon and Aggregates under the Rice-Wheat Rotation System. Soil Tillage Res. 2021, 212, 105071. [Google Scholar] [CrossRef]
  9. Shaheen, A.; Naeem, M.A.; Jilani, G.; Shafiq, M. Integrated Soil Management in Eroded Land Augments the Crop Yield and Water-Use Efficiency. Acta Agric. Scand. Sect. B Soil Plant Sci. 2010, 60, 274–282. [Google Scholar] [CrossRef]
  10. Zhao, Z.; Zheng, W.; Ma, Y.; Wang, X.; Li, Z.; Zhai, B.; Wang, Z. Responses of Soil Water, Nitrate and Yield of Apple Orchard to Integrated Soil Management in Loess Plateau, China. Agric. Water Manag. 2020, 240, 106325. [Google Scholar] [CrossRef]
  11. Srivastava, A.K.; Singh, S. Citrus Decline: Soil Fertility and Plant Nutrition. J. Plant Nutr. 2009, 32, 197–245. [Google Scholar] [CrossRef]
  12. Scamell, J.M. Healthy Land for Healthy Cattle. Cattle Pract. 2006, 14, 143–152. [Google Scholar]
  13. Jayne, T.S.; Snapp, S.; Place, F.; Sitko, N. Sustainable Agricultural Intensification in an Era of Rural Transformation in Africa. Glob. Food Secur. Agric. Policy 2019, 20, 105–113. [Google Scholar] [CrossRef]
  14. Martinho, V.J.P.D. Output Impacts of the Single Payment Scheme in Portugal: A Regression with Spatial Effects. Outlook Agric. 2015, 44, 109–118. [Google Scholar] [CrossRef]
  15. Killham, K. Integrated Soil Management—Moving towards Globally Sustainable Agriculture. J. Agric. Sci. 2011, 149, 29–36. [Google Scholar] [CrossRef]
  16. Takoutsing, B.; Weber, J.; Aynekulu, E.; Rodriguez Martin, J.A.; Shepherd, K.; Sila, A.; Tchoundjeu, Z.; Diby, L. Assessment of Soil Health Indicators for Sustainable Production of Maize in Smallholder Farming Systems in the Highlands of Cameroon. Geoderma 2016, 276, 64–73. [Google Scholar] [CrossRef]
  17. Vanwalleghem, T.; Gomez, J.A.; Infante Amate, J.; Gonzalez de Molina, M.; Vanderlinden, K.; Guzman, G.; Laguna, A.; Giraldez, J.V. Impact of Historical Land Use and Soil Management Change on Soil Erosion and Agricultural Sustainability during the Anthropocene. Anthropocene 2017, 17, 13–29. [Google Scholar] [CrossRef]
  18. Komatsuzaki, M.; Ohta, H. Soil Management Practices for Sustainable Agro-Ecosystems. Sustain. Sci. 2007, 2, 103–120. [Google Scholar] [CrossRef]
  19. Xue, J.; Lyu, D.; Wang, D.; Wang, Y.; Yin, D.; Zhao, Z.; Mu, Z. Assessment of Soil Erosion Dynamics Using the GIS-Based RUSLE Model: A Case Study of Wangjiagou Watershed from the Three Gorges Reservoir Region, Southwestern China. Water 2018, 10, 1817. [Google Scholar] [CrossRef] [Green Version]
  20. Alonso, A.; Froidevaux, M.; Javaux, M.; Laloy, E.; Mattern, S.; Roisin, C.; Vanclooster, M.; Bielders, C. A Hybrid Method for Characterizing Tillage-Induced Soil Physical Quality at the Profile Scale with Fine Spatial Details. Soil Tillage Res. 2022, 216, 105236. [Google Scholar] [CrossRef]
  21. Weninger, T.; Kreiselmeier, J.; Chandrasekhar, P.; Julich, S.; Feger, K.-H.; Schwaerzel, K.; Bodner, G.; Schwen, A. Effects of Tillage Intensity on Pore System and Physical Quality of Silt-Textured Soils Detected by Multiple Methods. Soil Res. 2019, 57, 703–711. [Google Scholar] [CrossRef]
  22. Fernandez-Romero, M.L.; Parras-Alcantara, L.; Lozano-Garcia, B.; Clark, J.M.; Collins, C.D. Soil Quality Assessment Based on Carbon Stratification Index in Different Olive Grove Management Practices in Mediterranean Areas. Catena 2016, 137, 449–458. [Google Scholar] [CrossRef]
  23. Sun, H.Y.; Koai, P.; Gerl, G.; Schro, R.; Joergensen, R.G.; Munch, J.C. Water-Extractable Organic Matter and Its Fluorescence Fractions in Response to Minimum Tillage and Organic Farming in a Cambisol. Chem. Biol. Technol. Agric. 2017, 4, 15. [Google Scholar] [CrossRef] [Green Version]
  24. Fiener, P.; Wilken, F.; Aldana-Jague, E.; Deumlich, D.; Gomez, J.A.; Guzman, G.; Hardy, R.A.; Quinton, J.N.; Sommer, M.; Van Oost, K.; et al. Uncertainties in Assessing Tillage Erosion—How Appropriate Are Our Measuring Techniques? Geomorphology 2018, 304, 214–225. [Google Scholar] [CrossRef] [Green Version]
  25. Urbanek, E.; Hallett, P.; Feeney, D.; Horn, R. Water Repellency and Distribution of Hydrophilic and Hydrophobic Compounds in Soil Aggregates from Different Tillage Systems. Geoderma 2007, 140, 147–155. [Google Scholar] [CrossRef]
  26. Schulten, H.; Leinweber, P. Influence of the Mineral Matrix on the Formation and Molecular Composition of Soil Organic-Matter in a Long-Term, Agricultural Experiment. Biogeochemistry 1993, 22, 1–22. [Google Scholar] [CrossRef]
  27. Gravuer, K.; Scow, K.M. Invader-Resident Relatedness and Soil Management History Shape Patterns of Invasion of Compost Microbial Populations into Agricultural Soils. Appl. Soil Ecol. 2021, 158, 103795. [Google Scholar] [CrossRef]
  28. Guemene, D.; Germain, K.; Aubert, C.; Bouvarel, I.; Cabaret, J.; Chapuis, H.; Corson, M.; Jondreville, C.; Juin, H.; Lessire, M.; et al. Organic poultry production in France: Status, bottlenecks, advantages and perspectives. Prod. Anim. 2009, 22, 161–178. [Google Scholar]
  29. Hernandez, T.; Berlanga, J.G.; Tormos, I.; Garcia, C. Organic versus Inorganic Fertilizers: Response of Soil Properties and Crop Yield. AIMS Geosci. 2021, 7, 415–439. [Google Scholar] [CrossRef]
  30. Le Guillou, C.; Prevost-Boure, N.C.; Karimi, B.; Akkal-Corfini, N.; Dequiedt, S.; Nowak, V.; Terrat, S.; Menasseri-Aubry, S.; Viaud, V.; Maron, P.-A.; et al. Tillage Intensity and Pasture in Rotation Effectively Shape Soil Microbial Communities at a Landscape Scale. MicrobiologyOpen 2019, 8, e676. [Google Scholar] [CrossRef]
  31. Tan, H.; Barret, M.; Mooij, M.J.; Rice, O.; Morrissey, J.P.; Dobson, A.; Griffiths, B.; O’Gara, F. Long-Term Phosphorus Fertilisation Increased the Diversity of the Total Bacterial Community and the PhoD Phosphorus Mineraliser Group in Pasture Soils. Biol. Fertil. Soils 2013, 49, 661–672. [Google Scholar] [CrossRef] [Green Version]
  32. Meena, R.S.; Kumar, S.; Datta, R.; Lal, R.; Vijayakumar, V.; Brtnicky, M.; Sharma, M.P.; Yadav, G.S.; Jhariya, M.K.; Jangir, C.K.; et al. Impact of Agrochemicals on Soil Microbiota and Management: A Review. Land 2020, 9, 34. [Google Scholar] [CrossRef] [Green Version]
  33. Mo, F.; Yu, K.-L.; Crowther, T.W.; Wang, J.-Y.; Zhao, H.; Xiong, Y.-C.; Liao, Y.-C. How Plastic Mulching Affects Net Primary Productivity, Soil C Fluxes and Organic Carbon Balance in Dry Agroecosystems in China. J. Clean Prod. 2020, 263, 121470. [Google Scholar] [CrossRef]
  34. Navarrete, A.A.; Kuramae, E.E.; de Hollander, M.; Pijl, A.S.; van Veen, J.A.; Tsai, S.M. Acidobacterial Community Responses to Agricultural Management of Soybean in Amazon Forest Soils. FEMS Microbiol. Ecol. 2013, 83, 607–621. [Google Scholar] [CrossRef] [Green Version]
  35. Shan, A.; Pan, J.; Kang, K.J.; Pan, M.; Wang, G.; Wang, M.; He, Z.; Yang, X. Effects of Straw Return with N Fertilizer Reduction on Crop Yield, Plant Diseases and Pests and Potential Heavy Metal Risk in a Chinese Rice Paddy: A Field Study of 2 Consecutive Wheat-Rice Cycles. Environ. Pollut. 2021, 288, 117741. [Google Scholar] [CrossRef]
  36. Thuerig, B.; Fliessbach, A.; Berger, N.; Fuchs, J.G.; Kraus, N.; Mahlberg, N.; Nietlispach, B.; Tamm, L. Re-Establishment of Suppressiveness to Soil- and Air-Borne Diseases by Re-Inoculation of Soil Microbial Communities. Soil Biol. Biochem. 2009, 41, 2153–2161. [Google Scholar] [CrossRef]
  37. Val-Moraes, S.P.; de Macedo, H.S.; Kishi, L.T.; Pereira, R.M.; Navarrete, A.A.; Mendes, L.W.; de Figueiredo, E.B.; La Scala, N.; Tsai, S.M.; de Macedo Lemos, E.G.; et al. Liming in the Sugarcane Burnt System and the Green Harvest Practice Affect Soil Bacterial Community in Northeastern So Paulo, Brazil. Antonie Van Leeuwenhoek 2016, 109, 1643–1654. [Google Scholar] [CrossRef] [Green Version]
  38. Valadares-Pereira, A. de A.; Alves Martins Oliveira, E.C.; Navarrete, A.A.; de Oliveira, W.P.; Tsai, S.M.; Peluzio, J.M.; de Morais, P.B. Fungal Community Structure as an Indicator of Soil Agricultural Management Effects in the Cerrado. Rev. Bras. Cienc. Solo 2017, 41, e0160489. [Google Scholar] [CrossRef] [Green Version]
  39. Stahl, P.D.; Parkin, T.B.; Christensen, M. Fungal Presence in Paired Cultivated and Uncultivated Soils in Central Iowa, USA. Biol. Fertil. Soils 1999, 29, 92–97. [Google Scholar] [CrossRef]
  40. Wipf, H.M.-L.; Xu, L.; Gao, C.; Spinner, H.B.; Taylor, J.; Lemaux, P.; Mitchell, J.; Coleman-Derr, D. Agricultural Soil Management Practices Differentially Shape the Bacterial and Fungal Microbiomes of Sorghum Bicolor. Appl. Environ. Microbiol. 2021, 87, e02345-20. [Google Scholar] [CrossRef]
  41. Yakupoglu, T.; Rodrigo-Comino, J.; Cerda, A. Potential Benefits of Polymers in Soil Erosion Control for Agronomical Plans: A Laboratory Experiment. Agronomy 2019, 9, 276. [Google Scholar] [CrossRef] [Green Version]
  42. Wang, Y.; Liu, L.; Tian, Y.; Wu, X.; Yang, J.; Luo, Y.; Li, H.; Awasthi, M.K.; Zhao, Z. Temporal and Spatial Variation of Soil Microorganisms and Nutrient under White Clover Cover. Soil Tillage Res. 2020, 202, 104666. [Google Scholar] [CrossRef]
  43. Garger, E.K.; Paretzke, H.G.; Tschiersch, J. Measurement of Resuspended Aerosol in the Chernobyl Area Part III. Size Distribution and Dry Deposition Velocity of Radioactive Particles during Anthropogenic Enhanced Resuspension. Radiat. Environ. Biophys. 1998, 37, 201–208. [Google Scholar] [CrossRef]
  44. Holman, I.P.; Hess, T.M.; Rose, S.C. A Broad-Scale Assessment of the Effect of Improved Soil Management on Catchment Baseflow Index. Hydrol. Process. 2011, 25, 2563–2572. [Google Scholar] [CrossRef]
  45. Endo, T.; Sadahiro, Y.; Haruta, T.; Kitamura, Y.; Li, Z.; Li, P.; Honna, T. Soil Salinization Related to Soil Morphological and Physicochemical Characteristics in the Luohui Irrigation Scheme, China. Arid Land Res. Manag. 2012, 26, 122–136. [Google Scholar] [CrossRef]
  46. Koch, A.; Chappell, A.; Eyres, M.; Scott, E. Monitor Soil Degradation or Triage for Soil Security? An Australian Challenge. Sustainability 2015, 7, 4870–4892. [Google Scholar] [CrossRef] [Green Version]
  47. Tiefenbacher, A.; Sanden, T.; Haslmayr, H.-P.; Miloczki, J.; Wenzel, W.; Spiegel, H. Optimizing Carbon Sequestration in Croplands: A Synthesis. Agronomy 2021, 11, 882. [Google Scholar] [CrossRef]
  48. Aznar-Sanchez, J.A.; Velasco-Munoz, J.F.; Lopez-Felices, B.; del Moral-Torres, F. Barriers and Facilitators for Adopting Sustainable Soil Management Practices in Mediterranean Olive Groves. Agronomy 2020, 10, 506. [Google Scholar] [CrossRef] [Green Version]
  49. Weninger, T.; Kamptner, E.; Dostal, T.; Spiegel, A.; Strauss, P. Detection of Physical Hazards in Soil Profiles Using Quantitative Soil Physical Quality Assessment in the Pannonian Basin, Eastern Austria. Int. Agrophys. 2020, 34, 463–471. [Google Scholar] [CrossRef]
  50. Lutz, F.; Herzfeld, T.; Heinke, J.; Rolinski, S.; Schaphoff, S.; von Bloh, W.; Stoorvogel, J.J.; Mueller, C. Simulating the Effect of Tillage Practices with the Global Ecosystem Model LPJmL (Version 5.0-Tillage). Geosci. Model Dev. 2019, 12, 2419–2440. [Google Scholar] [CrossRef] [Green Version]
  51. Schillaci, C.; Perego, A.; Valkama, E.; Marker, M.; Saia, S.; Veronesi, F.; Lipani, A.; Lombardo, L.; Tadiello, T.; Gamper, H.A.; et al. New Pedotransfer Approaches to Predict Soil Bulk Density Using WoSIS Soil Data and Environmental Covariates in Mediterranean Agro-Ecosystems. Sci. Total Environ. 2021, 780, 146609. [Google Scholar] [CrossRef]
  52. Papadopoulos, A.; Papadopoulos, F.; Tziachris, P.; Metaxa, I.; Iatrou, M. Site Specific Agricultural Soil Management with the Use of New Technologies. Glob. Nest. J. 2014, 16, 59–67. [Google Scholar]
  53. Boafo, D.K.; Kraisornpornson, B.; Panphon, S.; Owusu, B.E.; Amaniampong, P.N. Effect of Organic Soil Amendments on Soil Quality in Oil Palm Production. Appl. Soil Ecol. 2020, 147, 103358. [Google Scholar] [CrossRef]
  54. Castro, J.; Fernandez-Ondono, E.; Rodriguez, C.; Lallena, A.M.; Sierra, M.; Aguilar, J. Effects of Different Olive-Grove Management Systems on the Organic Carbon and Nitrogen Content of the Soil in Jaen (Spain). Soil Tillage Res. 2008, 98, 56–67. [Google Scholar] [CrossRef]
  55. Frkova, Z.; Vystavna, Y.; Koubova, A.; Kotas, P.; Grabicova, K.; Grabic, R.; Kodesova, R.; Chronakova, A. Microbial Responses to Selected Pharmaceuticals in Agricultural Soils: Microcosm Study on the Roles of Soil, Treatment and Time. Soil Biol. Biochem. 2020, 149, 107924. [Google Scholar] [CrossRef]
  56. Ma, T.; Zhou, W.; Chen, L.; Christie, P.; Luo, Y.; Wu, P. Phthalate Ester Contamination in Intensively Managed Greenhouse Facilities and the Assessment of Carcinogenic and Non-Carcinogenic Risk: A Regional Study. Int. J. Environ. Res. Public Health 2019, 16, 2818. [Google Scholar] [CrossRef] [Green Version]
  57. Niu, L.; Xu, Y.; Xu, C.; Yun, L.; Liu, W. Status of Phthalate Esters Contamination in Agricultural Soils across China and Associated Health Risks. Environ. Pollut. 2014, 195, 16–23. [Google Scholar] [CrossRef]
  58. Zhang, X.; Zhong, T.; Liu, L.; Ouyang, X. Impact of Soil Heavy Metal Pollution on Food Safety in China. PLoS ONE 2015, 10, e0135182. [Google Scholar] [CrossRef] [Green Version]
  59. Niu, L.; Yang, F.; Xu, C.; Yang, H.; Liu, W. Status of Metal Accumulation in Farmland Soils across China: From Distribution to Risk Assessment. Environ. Pollut. 2013, 176, 55–62. [Google Scholar] [CrossRef]
  60. Rehman, M.; Liu, L.; Wang, Q.; Saleem, M.H.; Bashir, S.; Ullah, S.; Peng, D. Copper Environmental Toxicology, Recent Advances, and Future Outlook: A Review. Environ. Sci. Pollut. Res. 2019, 26, 18003–18016. [Google Scholar] [CrossRef]
  61. Porwollik, V.; Rolinski, S.; Heinke, J.; Mueller, C. Generating a Rule-Based Global Gridded Tillage Dataset. Earth Syst. Sci. Data 2019, 11, 823–843. [Google Scholar] [CrossRef] [Green Version]
  62. Paustian, K.; Collier, S.; Baldock, J.; Burgess, R.; Creque, J.; DeLonge, M.; Dungait, J.; Ellert, B.; Frank, S.; Goddard, T.; et al. Quantifying Carbon for Agricultural Soil Management: From the Current Status toward a Global Soil Information System. Carbon Manag. 2019, 10, 567–587. [Google Scholar] [CrossRef] [Green Version]
  63. Kebonye, N.M.; Eze, P.N.; Agyeman, P.C.; John, K.; Ahado, S.K. Efficiency of the T-Distribution Stochastic Neighbor Embedding Technique for Detailed Visualization and Modeling Interactions between Agricultural Soil Quality Indicators. Biosyst. Eng. 2021, 210, 282–298. [Google Scholar] [CrossRef]
  64. Keshavarzi, A.; Tuffour, H.O.; Oppong, J.C.; Zeraatpisheh, M.; Kumar, V. Dealing with Soil Organic Carbon Modeling: Some Insights from an Agro-Ecosystem in Northeast Iran. Earth Sci. Inform. 2021, 14, 1833–1845. [Google Scholar] [CrossRef]
  65. Smith, J.; Smith, P.; Wattenbach, M.; Gottschalk, P.; Romanenkov, V.A.; Shevtsova, L.K.; Sirotenko, O.D.; Rukhovich, D.I.; Koroleva, P.V.; Romanenko, I.A.; et al. Projected Changes in the Organic Carbon Stocks of Cropland Mineral Soils of European Russia and the Ukraine, 1990–2070. Glob. Chang. Biol. 2007, 13, 342–356. [Google Scholar] [CrossRef]
  66. Eranki, P.L.; Devkota, J.; Landis, A.E. Carbon Footprint of Corn-Soy-Oats Rotations in the US Midwest Using Data from Real Biological Farm Management Practices. J. Clean Prod. 2019, 210, 170–180. [Google Scholar] [CrossRef]
  67. Mummey, D.L.; Smith, J.L.; Bluhm, G. Assessment of Alternative Soil Management Practices on N2O Emissions from US Agriculture. Agric. Ecosyst. Environ. 1998, 70, 79–87. [Google Scholar] [CrossRef]
  68. Oertel, C.; Matschullat, J.; Zurba, K.; Zimmermann, F.; Erasmi, S. Greenhouse Gas Emissions from Soils—A Review. Geochemistry 2016, 76, 327–352. [Google Scholar] [CrossRef] [Green Version]
  69. Hansen, E.M.O.; Hauggaard-Nielsen, H.; Justes, E.; Ambus, P.; Mikkelsen, T.N. The Influence of Grain Legume and Tillage Strategies on CO2 and N2O Gas Exchange under Varied Environmental Conditions. Agriculture 2021, 11, 464. [Google Scholar] [CrossRef]
  70. Paul, C.; Kuhn, K.; Steinhoff-Knopp, B.; Weisshuhn, P.; Helming, K. Towards a Standardization of Soil-Related Ecosystem Service Assessments. Eur. J. Soil Sci. 2021, 72, 1543–1558. [Google Scholar] [CrossRef]
  71. Thiele-Bruhn, S.; Bloem, J.; de Vries, F.T.; Kalbitz, K.; Wagg, C. Linking Soil Biodiversity and Agricultural Soil Management. Curr. Opin. Environ. Sustain. 2012, 4, 523–528. [Google Scholar] [CrossRef]
  72. Schulten, H.; Hempfling, R. Influence of Agricultural Soil-Management on Humus Composition and Dynamics—Classical and Modern Analytical Techniques. Plant Soil 1992, 142, 259–271. [Google Scholar] [CrossRef]
  73. Laghari, M.; Naidu, R.; Xiao, B.; Hu, Z.; Mirjat, M.S.; Hu, M.; Kandhro, M.N.; Chen, Z.; Guo, D.; Jogi, Q.; et al. Recent Developments in Biochar as an Effective Tool for Agricultural Soil Management: A Review. J. Sci. Food Agric. 2016, 96, 4840–4849. [Google Scholar] [CrossRef] [PubMed]
  74. Liu, L.; Huang, X.; Zhao, J.; Zhang, J.; Cai, Z. Characterizing the Key Agents in a Disease-Suppressed Soil Managed by Reductive Soil Disinfestation. Appl. Environ. Microbiol. 2019, 85, e02992-18. [Google Scholar] [CrossRef] [Green Version]
  75. Wiesmeier, M.; Mayer, S.; Burmeister, J.; Huebner, R.; Koegel-Knabner, I. Feasibility of the 4 per 1000 Initiative in Bavaria: A Reality Check of Agricultural Soil Management and Carbon Sequestration Scenarios. Geoderma 2020, 369, 114333. [Google Scholar] [CrossRef]
  76. Gustavo Belduma Belduma, R.; Barrezueta-Unda, S.; Vargas Gonzales, O.; Sanchez Romero, O. Management and Use of Agricultural Land in the Rural Area of the Canton Chilla from a Socioeconomic Perspective. Rev. Univ. Soc. 2020, 12, 299–306. [Google Scholar]
  77. Prager, K.; Schuler, J.; Helming, K.; Zander, P.; Ratinger, T.; Hagedorn, K. Soil Degradation, Farming Practices, Institutions and Policy Responses: An Analytical Framework. Land Degrad. Dev. 2011, 22, 32–46. [Google Scholar] [CrossRef]
  78. Van der Ploeg, R.R.; Gieska, M.; Schweigert, P. Impact of postwar agricultural soil management on surface hydrology and river peak discharge. Ber. Landwirtsch. 2001, 79, 447–465. [Google Scholar]
  79. Paul, C.; Techen, A.-K.; Robinson, J.S.; Helming, K. Rebound Effects in Agricultural Land and Soil Management: Review and Analytical Framework. J. Clean Prod. 2019, 227, 1054–1067. [Google Scholar] [CrossRef]
  80. FAOSTAT Several Statistics. Available online: https://www.fao.org/faostat/en/#home (accessed on 8 January 2022).
  81. World Bank Several Statistics and Information. Available online: https://www.worldbank.org/en/home (accessed on 16 January 2022).
  82. Anselin, L.; Syabri, I.; Kho, Y. GeoDa: An Introduction to Spatial Data Analysis. Geogr. Anal. 2006, 38, 5–22. [Google Scholar] [CrossRef]
  83. GeoDa. GeoDa Software. Available online: https://geodacenter.github.io/ (accessed on 8 January 2022).
  84. Moran, P.A.P. Notes on Continuous Stochastic Phenomena. Biometrika 1950, 37, 17–23. [Google Scholar] [CrossRef] [PubMed]
  85. Eurostat Countries Shapefiles. Available online: https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units/countries (accessed on 8 January 2022).
  86. QGIS.org. QGIS Geographic Information System. QGIS Association. 2022. Available online: https://qgis.org/en/site/ (accessed on 8 January 2022).
  87. Vincent, J.L. Factor Analysis in International Relations: Interpretation, Problem, Areas, and an Application; Univ of Florida Press: Gainesville, FL, USA, 1971; ISBN 978-0-8130-0315-3. [Google Scholar]
  88. Torres-Reyna, O. Getting Started in Factor Analysis (Using Stata) (Ver. 1.0 Beta/Draft) n.d. Available online: https://www.princeton.edu/~otorres/Factor.pdf (accessed on 8 January 2022).
  89. StataCorp. Stata 15 Base Reference Manual; Stata Press: College Station, TX, USA, 2017. [Google Scholar]
  90. StataCorp. Stata Statistical Software: Release 15; StataCorp LLC: College Station, TX, USA, 2017. [Google Scholar]
  91. Stata Stata: Software for Statistics and Data Science. Available online: https://www.stata.com/ (accessed on 8 January 2022).
  92. Baltagi, B.H. Econometric Analysis of Panel Data, 4th ed.; Wiley: Chichester, UK; Hoboken, NJ, USA, 2008; ISBN 978-0-470-51886-1. [Google Scholar]
  93. Torres-Reyna, O. Panel Data Analysis Fixed and Random Effects Using Stata (v. 4.2); Priceton University: Princeton, NJ, USA, 2007. [Google Scholar]
  94. Spearman, C. The Proof and Measurement of Association between Two Things. Am. J. Psychol. 1904, 15, 72–101. [Google Scholar] [CrossRef]
  95. Granger, C.W.J. Investigating Causal Relations by Econometric Models and Cross-Spectral Methods. Econometrica 1969, 37, 424–438. [Google Scholar] [CrossRef]
Soilsystems 06 00032 g001 550
Figure 1. Global and local spatial autocorrelation and worldwide distribution for the agricultural soil emissions (CO2eq) per ha of cropland, on average over the period 2001–2017 (kilotonnes per ha of cropland); (a) Global spatial autocorrelation, (b) Local spatial autocorrelation; (c) Worldwide distribution.
Figure 1. Global and local spatial autocorrelation and worldwide distribution for the agricultural soil emissions (CO2eq) per ha of cropland, on average over the period 2001–2017 (kilotonnes per ha of cropland); (a) Global spatial autocorrelation, (b) Local spatial autocorrelation; (c) Worldwide distribution.
Soilsystems 06 00032 g001
Soilsystems 06 00032 g002 550
Figure 2. Global and local spatial autocorrelation and worldwide distribution for the average value of food production (constant 2004–2006 I$/cap) (3-year average), on average over the period 2001–2017 (I$ per person); (a) Global spatial autocorrelation, (b) Local spatial autocorrelation; (c) Worldwide distribution.
Figure 2. Global and local spatial autocorrelation and worldwide distribution for the average value of food production (constant 2004–2006 I$/cap) (3-year average), on average over the period 2001–2017 (I$ per person); (a) Global spatial autocorrelation, (b) Local spatial autocorrelation; (c) Worldwide distribution.
Soilsystems 06 00032 g002
Soilsystems 06 00032 g003 550
Figure 3. Global and local spatial autocorrelation and worldwide distribution for the gross agricultural production value (constant 2014–2016 thousand I$) per ha of cropland, on average over the period 2001–2017 (thousand I$ per ha of crop land); (a) Global spatial autocorrelation, (b) Local spatial autocorrelation; (c) Worldwide distribution.
Figure 3. Global and local spatial autocorrelation and worldwide distribution for the gross agricultural production value (constant 2014–2016 thousand I$) per ha of cropland, on average over the period 2001–2017 (thousand I$ per ha of crop land); (a) Global spatial autocorrelation, (b) Local spatial autocorrelation; (c) Worldwide distribution.
Soilsystems 06 00032 g003
Soilsystems 06 00032 g004 550
Figure 4. Global and local spatial autocorrelation and worldwide distribution for the cropland nutrient flow per unit area, on average over the period 2001–2017 (kg per ha); (a) Global spatial autocorrelation, (b) Local spatial autocorrelation; (c) Worldwide distribution.
Figure 4. Global and local spatial autocorrelation and worldwide distribution for the cropland nutrient flow per unit area, on average over the period 2001–2017 (kg per ha); (a) Global spatial autocorrelation, (b) Local spatial autocorrelation; (c) Worldwide distribution.
Soilsystems 06 00032 g004
Table
Table 1. Spearman’s rank correlation matrix for several variables over the period 2001–2017 and across world countries.
Table 1. Spearman’s rank correlation matrix for several variables over the period 2001–2017 and across world countries.
Agricultural Soil Emissions
(kg per ha)		Average Food Production
(I$ per Person)		Gross Agricultural Production
(I$ per ha)		Cropland Nutrient Flow
(kg per ha)
Agricultural soil emissions (kg per ha)1.000
Average food production (I$ per person)0.0920 *1.000
(0.000)
Gross agricultural production (I$ per ha)0.5996 *0.2278 *1.000
(0.000)(0.000)
Cropland nutrient flow (kg per ha)0.6691 *0.2099 *0.6560 *1.000
(0.000)(0.000)(0.000)
Note: *, statistically significant at 1%.
Table
Table 2. Factor analysis to obtain an integrated agricultural soil management index over the period 2001–2017 and across world countries.
Table 2. Factor analysis to obtain an integrated agricultural soil management index over the period 2001–2017 and across world countries.
Method: Principal-Component Factors; Rotation: Orthogonal Varimax (Kaiser Off)
FactorVarianceDifferenceProportionCumulative
Factor11.668 0.8340.834
Rotated Factor Loadings and Unique Variances
VariableFactor1Uniqueness
Gross agricultural production (I$ per ha)0.9130.166
Cropland nutrient flow (kg per ha)0.9130.166
Table
Table 3. Top 10 countries for the integrated agricultural soil management index, on average over the period 2001–2017.
Table 3. Top 10 countries for the integrated agricultural soil management index, on average over the period 2001–2017.
CountriesAgricultural Soil Emissions
(kg per ha)		Average Food Production
(I$ per Person)		Gross Agricultural Production
(I$ per ha)		Cropland Nutrient Flow
(kg per ha)		Index
Belgium281247310,2612873
Malta231017510,8342603
Switzerland286830490182512
China, Taiwan Province of194020987542242
Luxembourg250334140002982
Egypt363522886582202
United Arab Emirates496210086151912
Trinidad and Tobago294510240552572
Republic of Korea196619083511732
Israel189734799661141
Note: The country with the highest index is Djibouti, nonetheless because difficulties in validating the data it was not considered in this table.
Table
Table 4. Panel data regression with the agricultural soil emissions per ha as dependent variable over the period 2001–2017 and across world countries.
Table 4. Panel data regression with the agricultural soil emissions per ha as dependent variable over the period 2001–2017 and across world countries.
ModelPrais-Winsten Regression, Correlated Panels Corrected Standard Errors (PCSEs)
Constant−34.717
(−0.100)
[0.917]
Cropland nutrient flow (kg per ha)41.279 *
(5.910)
[0.000]
Pesaran’s test of cross sectional independence3.009 *
[0.002]
Modified Wald test for groupwise heteroskedasticity6.2 × 1010 *
[0.000]
Wooldridge test for autocorrelation1137.221 *
[0.000]
Note: *, statistically significant at 1%.
Table
Table 5. Panel data regression with the gross agricultural production per ha as dependent variable over the period 2001–2017 and across world countries.
Table 5. Panel data regression with the gross agricultural production per ha as dependent variable over the period 2001–2017 and across world countries.
ModelPrais-Winsten Regression, Correlated Panels Corrected Standard Errors (PCSEs)
Constant1358.298 *
(7.970)
[0.000]
Cropland nutrient flow (kg per ha)25.094 *
(7.610)
[0.000]
Pesaran’s test of cross sectional independence54.380 *
[0.000]
Modified Wald test for groupwise heteroskedasticity1.2 × 108 *
[0.000]
Wooldridge test for autocorrelation528.496 *
[0.000]
Note: *, statistically significant at 1%.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Martinho, V.J.P.D.; Pereira, J.L.S.; Gonçalves, J.M. Assessment of the Interrelationships of Soil Nutrient Balances with the Agricultural Soil Emissions and Food Production. Soil Syst. 2022, 6, 32. https://doi.org/10.3390/soilsystems6020032

AMA Style

Martinho VJPD, Pereira JLS, Gonçalves JM. Assessment of the Interrelationships of Soil Nutrient Balances with the Agricultural Soil Emissions and Food Production. Soil Systems. 2022; 6(2):32. https://doi.org/10.3390/soilsystems6020032

Chicago/Turabian Style

Martinho, Vítor João Pereira Domingues, José L. S. Pereira, and José Manuel Gonçalves. 2022. "Assessment of the Interrelationships of Soil Nutrient Balances with the Agricultural Soil Emissions and Food Production" Soil Systems 6, no. 2: 32. https://doi.org/10.3390/soilsystems6020032

APA Style

Martinho, V. J. P. D., Pereira, J. L. S., & Gonçalves, J. M. (2022). Assessment of the Interrelationships of Soil Nutrient Balances with the Agricultural Soil Emissions and Food Production. Soil Systems, 6(2), 32. https://doi.org/10.3390/soilsystems6020032

Article Metrics

Back to TopTop