Digital Soil Mapping Approaches for Assisting Site-Specific Soil Management in Sugarcane Growing Areas

The Australian sugarcane industry has developed the “Six Easy Steps” nutrient and ameliorant management guidelines with the aim of optimising productivity and profitability, without adversely influencing the soil condition and causing off-farm effects. This involves knowing the spatial variation of soil properties, such as; cation exchange capacity (CEC), exchangeable calcium (Exch. Ca) and magnesium (Mg) and exchangeable sodium percentage (ESP). One way to generate soil information is to use a digital soil mapping (DSM) approach. Specifically, combine limited soil data with easier to collect ancillary data via mathematical models. This thesis focusses on developing digital soil maps (DSM) in different Australia sugarcane growing districts. Chapter 1 describes the need for DSM while Chapter 2 describes the basic components of DSM, including proximal sources of ancillary data and mathematical models. Moreover, the literature is reviewed to provide demonstrated case studies of DSM of various soil properties (e.g. CEC, Exch. Ca, Exch. Mg and ESP), with gaps identified and research chapters presented to bridge these. In Chapter 3, the application of DSM to predict CEC is explored to assist with the quantification of uncertainty due to ancillary data. In Chapter 4, the aim was to determine optimal components for DSM of topsoil Exch. Ca and Mg. In Chapter 5, the potential of wavelet analysis was explored where there was complex variation in ancillary data relative to topsoil ESP. In Chapter 6, a comparison was made of DSM to account for topsoil (0 – 0.3 m) ESP using mathematical or numerical clustering (FKM) models to create soil classes with a conventional Soil Order map (e.g. soils and land suitability of Burdekin River Irrigation Area). The results showed DSM can be applied to a wide range of soil properties and classes, especially when all the available ancillary data was used in combination. Useful guidelines on operational aspects including transect spacing (7.5 – 30 m) and soil samples for calibration (1 per hectare) were described. Future research should explore other ancillary data sources (e.g. crop yield), mathematical models (e.g. machine learning) and follow up improvement in soil condition as a function of the application of nutrient and ameliorants in accordance with the “Six Easy Steps” guidelines in the various study areas

Enabling Precision Fertilisers Application Using Digital Soil Mapping in Australian Sugarcane Areas

Sugar is Australia's second largest export crop after wheat, generating a total annual revenue of almost $2 billion. It is produced from sugarcane, with approximately 95% grown in Queensland. While highly productive and contributing to the area’s economic sustainability, the soils in these areas have low fertility. The soils typically contain sand content > 60%, low organic carbon (SOC 6%). Hence, sugarcane farmers need to apply fertilisers and ameliorants to maintain soil quality and productivity. Unfortunately, the high intensity rainfall in the region results in sediments, nutrients, and ameliorants run-off from these farms, resulting in environmental degradation and threats to marine ecology in the adjacent World Heritage Listed Great Barrier Reef. To mitigate these issues, the Australian sugarcane industry introduced the Six-Easy-Step Nutrient Management Guidelines. To apply these guidelines, a labour-intensive high-density soil sampling is typically required at the field level, followed by expensive laboratory analysis, spanning the myriad of biological, physical, and chemical properties of soils that need to be determined. To assist in sampling site selection, remote (e.g., Landsat-8, Sentinel-2, and DEM-based terrain attributes) and/or proximal sensing (e.g., electromagnetic [EM] induction and gamma-ray [γ-ray] spectrometry) digital data are increasingly being used. Moreover, the soil and digital data can be modelled using geostatistical (e.g., ordinary kriging [OK]), linear (e.g., linear mixed model [LMM]), machine learning (e.g., random forest [RF], quantile regression forest [QRF], support vector machine [SVM], and Cubist) and hybrid (e.g., RFRK, SVMRK, and CubistRK) approaches to enable prediction of soil properties from the rich source of digital data. However, there are many questions that need to be answered to determine appropriate recommendations including but not limited to i) which modelling approach is optimal, ii) which source of digital data is optimal and does fusion of various sources of digital data improve prediction accuracy, iii) which methods can be used to combine these digital data, iv) what is a minimum number of samples to establish a suitable calibration, v) which soil sampling designs could be used, and vi) what approaches are available to enable prediction of soil properties at various depths simultaneously? In this thesis, Chapter 1 introduces the research questions and defines the problems facing the Australian Sugarcane Industry in terms of the applications of the Six-Easy-Steps Nutrient Management Guidelines, research aims and thesis structure. Chapter 2 is a systematic literature review on various facets of DSM, which includes digital and soil data, models and outputs, and their application across various spatial scales and properties. In Chapter 3, prediction of topsoil (0-0.3 m) SOC is examined in the context of comparing predictive models (i.e., geostatistical, linear, machine learning [ML], and hybrid) using various digital data (i.e., remote [Landsat-8] and proximal sensors [EM and γ-ray]) either individually or in combination and determining minimum number of calibration samples. Chapter 4 shows to predict top- (0-0.3 m) and subsoil (0.6-0.9 m) Ca and Mg, various sampling designs (simple random [SRS], spatial coverage [SCS], feature space coverage [FSCS], and conditioned Latin hypercube sampling [cLHS]) were assessed, with different modelling approaches (i.e., OK, LMM, QRF, SVM, and CubistRK) and calibration sample size effect evaluated, using a combination of proximal data (EM and γ-ray) and terrain (e.g., elevation, slope, and aspect, etc.) attributes. Chapter 5 shows to enable the three-dimensional mapping of CEC and pH at topsoil (0-0.3 m), subsurface (0.3-0.6 m), shallow- (0.6-0.9 m) and deep-subsoil (0.9-1.2 m), an equal-area spline depth function can be used, with remote (Sentinel-2) and proximal data (EM and γ-ray) used alone or fused together, and various fusion methods (i.e., concatenation, simple averaging [SA], Bates-Granger averaging [BGA], Granger-Ramanathan averaging [GRA], and bias-corrected eigenvector averaging [BC-EA]) investigated. Chapter 6 explored the synergistic use of proximal (EM and γ-ray), and time-series of remote data (Landsat-8 and Sentinel-2) to map top- (0-0.15 m) and subsoil (0.30-0.45 m) ESP. The results show that, across these case studies, hybrid and ML models generally achieved higher prediction accuracy. The fusion of remote and proximal data produced better predictions, compared to single source of sensors. Granger-Ramanathan averaging (GRA) and concatenation were the most effective methods to combine digital data. A minimum of less than 1 sample ha-1 would be required to calibrate a good predictive model. There were differences in prediction accuracy amongst the sampling designs. The application of depth function splines enables the simultaneous mapping of soil properties from various depths. The produced DSM of soil properties can be used to inform farmers of spatial variability of soils and enable them to precisely apply fertilisers and/or ameliorants based on the Six-Easy-Step Nutrient Management Guidelines

Annals [...].

Pedometrics: innovation in tropics; Legacy data: how turn it useful?; Advances in soil sensing; Pedometric guidelines to systematic soil surveys.Evento online. Coordenado por: Waldir de Carvalho Junior, Helena Saraiva Koenow Pinheiro, Ricardo Simão Diniz Dalmolin

Digital soil mapping of soil physical and chemical properties using proximal and remote sensed data in Australian cotton growing areas

In Australian cotton-growing areas, information of soil physical and chemical properties is required as they decide soil structure, nutrient availability and water holding capacity. However, using conventional laboratory methods to determine these properties is impractical as they are time-consuming and costly. This is especially the case when considering samples from different depths and across heterogenous fields and districts. Thus, there is a need for efficient and affordable methods to enable data generation. To answer this need, digital soil mapping (DSM) can be used, in which limited laboratory measured soil data is coupled with cheaper-to acquire digital data through models and then the model and spatially exhaustive digital data are used to predict soil properties on unsampled locations. This thesis evaluates DSM methods for the prediction of soil physical (e.g., clay content) and chemical (e.g., cation exchange capacity [CEC] and exchangeable [exch.] cations) properties at various depths across cotton growing areas in south-eastern Australia, at field and district scales. Chapter 1 is the general introduction where research problems are defined, and research objectives are introduced. To point out gaps in the application of DSM on the prediction of soil properties, Chapter 2 comprehensively reviews DSM concepts, the applicability of proximally (e.g., electromagnetic induction (EM), visible near-infrared spectroscopy (vis-NIR)) or remotely (e.g., γ-ray spectrometer) sensed digital data for prediction of soil properties at various depths and the modelling techniques. The first research chapter (Chapter 3) compares various strategies to build the vis-NIR spectral library for clay content prediction at two depths across seven cotton growing areas using Cubist model. The results show that the area-specific vis-NIR library achieve the best results. The improvement in model performance is possible using spiking. The Chapter 4 compares multivariate methods for estimating clay content and its uncertainty map at two depths and the effect of weighted model averaging is evaluated. The results show that random forest (RF) model generally performs the best and model averaging could further improve the prediction accuracy. The Chapter 5 evaluates the potential of vis-NIR as a tool for the simultaneous prediction of soil physical and chemical properties across cotton growing areas and considering two calibration models. The results show that satisfactory predictions of clay and CEC are achieved with silt and sand prediction moderate, while the prediction of pH and exchangeable sodium percentage (ESP) are unsatisfactory. A multi-depth vis-NIR library generally performs better than depth-specific libraries on prediction of soil properties. The Chapter 6 builds a topsoil (0 – 0.3 m) vis-NIR spectral library to predict topsoil exch. cations considering four different calibration models and explores the applicability of the topsoil library to predict exch. cations at deeper depths considering spiking or not. The results show that the vis-NIR could provide satisfactory prediction of exch. calcium and magnesium. Topsoil spectral library could be used to predict exch. cations at deeper depth with spiking further improving the result. The Chapter 7 estimates spatial variation of CEC at various depths using quasi-3d joint inversion of EM38 and EM31 data in an irrigated cotton field. The results indicate that the joint-inversion approach developed in this study could generate accurate 3D predictions of soil CEC in the cotton growing field. This thesis explores DSM methods for the prediction of soil physical and chemical properties in Australian cotton growing areas and the results deliver new evidence of the potential to use proximally and remotely sensed digital data and state-to-art models for rapid and efficient generation of soil information. New findings will serve to advance the existing knowledge on application of DSM at field and district scales

Development of a Proximal Soil Sensing System for the Continuous Management of Acid Soil

The notion that agriculturally productive land may be treated as a relatively homogeneous resource at thewithin-field scale is not sound. This assumption and the subsequent uniform application of planting material,chemicals and/or tillage effort may result in zones within a field being under- or over-treated. Arising fromthese are problems associated with the inefficient use of input resources, economically significant yield losses,excessive energy costs, gaseous or percolatory release of chemicals into the environment, unacceptable long-term retention of chemicals and a less-than-optimal growing environment. The environmental impact of cropproduction systems is substantial. In this millennium, three important issues for scientists and agrariancommunities to address are the need to efficiently manage agricultural land for sustainable production, the maintenance of soil and water resources and the environmental quality of agricultural land.Precision agriculture (PA) aims to identify soil and crop attribute variability, and manage it in an accurate and timely manner for near-optimal crop production. Unlike conventional agricultural management where an averaged whole-field analytical result is employed for decision-making, management in PA is based on site-specific soil and crop information. That is, resource application and agronomic practices are matched with variation in soil attributes and crop requirements across a field or management unit. Conceptually PA makes economic and environmental sense, optimising gross margins and minimising the environmental impact of crop production systems. Although the economic justification for PA can be readily calculated, concepts such as environmental containment and the safety of agrochemicals in soil are more difficult to estimate. However,it may be argued that if PA lessens the overall agrochemical load in agricultural and non-agricultural environments, then its value as a management system for agriculture increases substantially.Management using PA requires detailed information of the spatial and temporal variation in crop yield components, weeds, soil-borne pests and attributes of physical, chemical and biological soil fertility. However,detailed descriptions of fine scale variation in soil properties have always been difficult and costly to perform.Sensing and scanning technologies need to be developed to more efficiently and economically obtain accurate information on the extent and variability of soil attributes that affect crop growth and yield. The primary aim of this work is to conduct research towards the development of an 'on-the-go' proximal soil pH and lime requirement sensing system for real-time continuous management of acid soil. It is divided into four sections.Section one consists of two chapters; the first describes global and historical events that converged into the development of precision agriculture, while chapter two provides reviews of statistical and geostatistical techniques that are used for the quantification of soil spatial variability and of topics that are integral to the concept of precision agriculture. The review then focuses on technologies that are used for the complete enumeration of soil, namely remote and proximal sensing.Section two comprises three chapters that deal with sampling and mapping methods. Chapter three provides a general description of the environment in the experimental field. It provides descriptions of the field site,topography, soil condition at the time of sampling, and the spatial variability of surface soil chemical properties. It also described the methods of sampling and laboratory analyses. Chapter four discusses some of the implications of soil sampling on analytical results and presents a review that quantifies the accuracy,precision and cost of current laboratory techniques. The chapter also presents analytical results that show theloss of information in kriged maps of lime requirement resulting from decreases in sample size. The messageof chapter four is that the evolution of precision agriculture calls for the development of 'on-the-go' proximal soil sensing systems to characterise soil spatial variability rapidly, economically, accurately and in a timely manner. Chapter five suggests that for sparsely sampled data the choice of spatial modelling and mapping techniques is important for reliable results and accurate representations of field soil variability. It assesses a number of geostatistical methodologies that may be used to model and map non-stationary soil data, in this instance soil pH and organic carbon. Intrinsic random functions of order k produced the most accurate and parsimonious predictions of all of the methods tested.Section three consists of two chapters whose theme pertains to sustainable and efficient management of acid agricultural soil. Chapter six discusses soil acidity, its causes, consequences and current management practices.It also reports the global extent of soil acidity and that which occurs in Australia. The chapter closes by proposing a real-time continuous management system for the management of acid soil. Chapter seven reports results from experiments conducted towards the development of an 'on-the-go' proximal soil pH and lime requirement sensing system that may be used for the real-time continuous management of acid soil. Assessment of four potentiometric sensors showed that the pH Ion Sensitive Field Effect Transistor (ISFET)was most suitable for inclusion in the proposed sensing system. It is accurate and precise, drift and hysteresis are low, and most importantly it's response time is small. A design for the analytical system was presented based on flow injection analysis (FIA) and sequential injection analysis (SIA) concepts. Two different modes of operation were described. Kinetic experiments were conducted to characterise soil:0.01M CaCl2 pH(pHCaCl2) and soil:lime requirement buffer (pH buffer) reactions. Modelling of the pH buffer reactions described their sequential, biphasic nature. A statistical methodology was devised to predict pH buffer measurements using only initial reaction measurements at 0.5s, 1s, 2s and 3s measurements. The accuracy of the technique was 0.1pH buffer units and the bias was low. Finally, the chapter describes a framework for the development of a prototype soil pH and lime requirement sensing system and the creative design of the system.The final section relates to the management of acid soil by liming. Chapter eight describes the development of empirical deterministic models for rapid predictions of lime requirement. The response surface models are based on soil:lime incubations, pH buffer measurements and the selection of target pH values. These models are more accurate and more practical than more conventional techniques, and may be more suitably incorporated into the spatial decision-support system of the proposed real-time continuous system for the management of acid soil. Chapter nine presents a glasshouse liming experiment that was used to authenticate the lime requirement model derived in the previous chapter. It also presents soil property interactions and soil-plant relationships in acid and ameliorated soil, to compare the effects of no lime applications, single-rate and variable-rate liming. Chapter X presents a methodology for modelling crop yields in the presence of uncertainty. The local uncertainty about soil properties and the uncertainty about model parameters were accounted for by using indicator kriging and Latin Hypercube Sampling for the propagation of uncertainties through two regression functions; a yield response function and one that equates resultant pH after the application of lime. Under the assumptions and constraints of the analysis, single-rate liming was found to be the best management option

Capacity building and public awareness raising on Nitrates Directive in the countries aspiring to EU accession

The governments of the EU Member States have agreed to potentially enlarge the Union with seven South East European countries (Croatia, the Former Yugoslav Republic of Macedonia, Albania, Bosnia and Herzegovina, Montenegro, Serbia, Kosovo under UN Security Council Resolution 1244) and Turkey. However, these countries will be granted full member status only when all political, legislative and administrative requirements for membership are fulfilled. Transposition, implementation and enforcement of the EU Nitrate Directive (91/676/EC) is one of these requirements. Many policy makers, farmers and consumers from the EU accession countries perceive the Nitrate Directive as a very demanding piece of legislation with little relevance for their countries. Moreover, there is a widespread belief that the Nitrate Directive can potentially limit the competitiveness of their agricultural sector. Limited or partial information and misconceptions about the Nitrate Directive in these countries provokes fear (and sometimes anger), notably by farmers. Consequently, the adoption of the Nitrate Directive receives low political priority. In most countries aspiring to EU membership, there are no other driving forces besides EU accession pushing Governments to adopt the Nitrate Directive. Pressure exerted by health, consumer or environmental protection NGOs hardly exists. Training and education on the Nitrate Directive is poorly covered and addressed by the curricula at higher education organisations, as well as by NGO training programmes. Consequently experts from these countries do not have much opportunity to get acquainted with the Nitrate Directive. The problem persists when these people become governmental officials, extension officers, farm managers, etc., and are supposed to make policy decisions and administer the Nitrate Directive – or advise farmers and manage farms according to EU Nitrates Directive requirements. In order to remedy this problem, several international projects, financed by the Global Environmental Facility fund (administered by the World Bank or the UN Development Programme), the European Commission, and the EU Member State Governments (notably the Netherlands) have been initiated recently. The experience from these projects shows that transfer of information - capacity building and public awareness raising programmes – play a vital role in understanding the rationale behind the Nitrate Directive and in accepting the farming practices it requires. Participatory training, demonstration of nutrient management planning software, on-farm water quality testing with mobile kits, experiments using piezometers/lysimeters and field trials involving various cover crops, buffer strips, etc. accompanied with Web pages, demonstration videos, posters, leaflets, etc. have been shown to be powerful tools to demonstrate the link between water quality and (adverse) agricultural practices. The valuation (“monetisation”) of ecosystem services and environmental costs generated by the fertiliser industry and farming is a newly emerging concept that seems to be a particularly promising tool for awareness raising on the Nitrate Directive. Emerging assessments from the accession countries suggest that hidden costs (public investments and environmental costs) associated with elevated content of nitrates in water can be substantial. Making policy makers and the public at large aware of these costs and of potential savings on them by practising water friendly farming methods (e.g. organic or pastoral farming) can foster the adoption of the EU Nitrate Directive in EU candidate counties and beyond

International Workshop on Nutrient Balances for Sustainable Agricultural Production and Natural Resource Management in Southeast Asia, Bangkok, Thailand, 20-22 February 2001: selected papers and presentations

Soil management / Soil properties / Soil fertility / Soil degradation / Crop production / Farmers / Agricultural extension / Farming systems / Sustainability / Rice / Cassava / Vegetables / Maize / Fertilizers / Decision support tools / Economic aspects

Assessment of Land Degradation Patterns in Western Kenya : Implications for Restoration and Rehabilitation

Land degradation remains a major threat to the provision of environmental services and the ability of smallholder farmers to meet the growing demand for food. Understanding patterns of land degradation is therefore a central starting point for designing any sustainable land management strategies. However, land degradation is a complex process both in time and space making its quantification difficult. There is no adequate monitoring of many of the land degradation issues both at national and local scale in Kenya. The objective of this study conducted between 2009 and 2012 was to assess the land degradation patterns in Kenya as a basis for making recommendations for sustainable land management. The correlation between vegetation and precipitation and the change in vegetation over the period 2001-2009 was assessed using 250 m resolution Moderate Resolution Imaging Spectroradiometer - Normalized Difference Vegetation Index (MODIS/NDVI) and time-series rainfall data. The assessment at national levels revealed that, irrespective of the direction of change, there was a significant correlation between vegetation (NDVI) and annual precipitation for 32% of the land area. The inter-annual change in vegetation cover, depicted by the NDVI slope, was between -0.067 and +0.068. A negative NDVI slope (indication of degradation) was observed for areas around Lake Turkana and several districts in eastern Kenya. Positive NDVI trends were observed in Wajir and Baringo, which are located in the dry land areas, showing that the vegetation cover was increasing over the years. NDVI difference between the baseline (2001-2003) and end line (2007-2009) showed an absolute change in NDVI of -0.42 to +0.48. But the relative change was between -74% for the degrading areas and +238% for the improving areas with most of the dramatic positive changes taking place in the drylands. Relative to the baseline, 21% of the land was experiencing a decline in the vegetation cover, 12% was improving, while 67% was stable. Classification of Landsat imagery for the period 1973, 1988 and 2003 showed that there were significant changes in land use land cover (LULC) in the western Kenya districts with the area under agricultural activities increasing from 28% in 1973 to 70% in 2003 while those under wooded grassland decreasing from 51% to 11% over the same period. Detailed field observations and measurements showed that over 55% of the farms sampled lacked any form of soil and water conservation technologies. Sheet erosion was the most dominant form of soil loss observed in over 70% of the farms. There was a wide variability in soil chemical properties across the study area with values of most major properties being below the critical thresholds needed to support meaningful crop production. Notable was the high proportion (90%) of farms with slightly acidic to strongly acidic (pH Erfassung und Bewertung verschiedener Erscheinungsformen von Landdegradation in West Kenia: Konsequenzen für Restaurierungs- und Rehabilitierungsmaßnahmen Landdegradation stellt eine der größten Gefahren für die Bereitstellung von Umweltdienstleistungen dar und für die Kleinbauern hinsichtlich des wachsenden Bedarfs an Nahrungsmitteln. Die Entwicklung nachhaltiger Landnutzungsstrategien beginnt daher mit dem Erkennen und Verstehen von Landdegradationsmustern. Die komplexen Prozesse der Landdegradation über Raum und Zeit erschweren jedoch eine Quantifizierung. Bisher existiert in Kenia kein adäquates Monitoring der Landdegradation, weder auf nationaler noch auf lokaler Ebene. Das Ziel des von 2009 bis 2012 durchgeführten Studie war die Erfassung von Landdegradationsmustern in Kenia, um Empfehlungen für nachhaltige Landmanagementstrategien geben zu können. Die Korrelation zwischen Vegetation und Niederschlag und der Vegetationsveränderungen im Zeitraum 2001 bis 2009 wurde mittels einer MODIS/NDVI (Moderate Resolution Imaging Spectroradiometer (250 m-Auflösung) - Normalized Difference Vegetation Index) ermittelt. Die Untersuchungen auf nationaler Ebene ergaben, dass, unabhängig von der Richtung des Änderungsprozesses, eine signifikante Korrelation zwischen Vegetation (NDVI) und jährlicher Niederschlagsmenge für 32% der Landfläche besteht. Die Änderung der Vegetationsdecke über mehrere Jahre, dargestellt durch die NDVI-Linie, lag zwischen -0.067 und +0.068. Eine abfallende NDVI-Linie (als Indikator für Degradation) konnte für Flächen rund um Turkana See und in mehreren Distrikten Ost-Kenias beobachtet werden. Positive NDVI-Trends traten in den Trockengebieten Wajir und Baringo auf; dies deutet darauf hin, dass die Vegetationsdichte hier über die Jahre zunahm. Die Differenz des NDVI zwischen Ausgangswerten (2001-2003) und Endwerten (2007-2009) zeigte eine absolute NDVI-Veränderung von -0.42 bis +0.48. Die relative Veränderung war jedoch -74% für degradierende Flächen und +238% für Flächen mit zunehmender Vegetationsbedeckung, wobei die höchsten positiven Veränderungen in den Trockengebieten festgestellt wurden. Im Vergleich zu den Basisdaten fand auf 21% der Flächen eine Abnahme der Vegetationsbedeckung statt, 12% der Landflächen erfuhr eine Verbesserung und 67% verzeichnete keine Veränderungen. Die Klassifizierung der Landsat-Aufnahmen von 1973, 1988 und 2003 zeigte signifikante Veränderungen in der Landbedeckung bzw. Landnutzung in den Distrikten West Kenias . Der Anteil der landwirtschaftlich genutzten Fläche stieg von 28% im Jahre 1973 auf 70% in 2003 an, während der Flächenanteil der Baum- und Strauchsavanne im gleichen Zeitraum von 51% auf 11% abnahm. Detaillierte Felduntersuchungen ergaben, dass mehr als 55% der untersuchten Farmen keine Boden- oder Wasserschutzmaßnahmen durchführen. Bodenerosion stellte die Hauptursache von Bodenverlust dar und konnte bei über 70% der Farmen festgestellt werden. Die chemischen Bodeneigenschaften im Untersuchungsgebiet waren sehr variabel; viele der wichtigsten Bodeneigenschaften lagen unter den kritischen Grenzwerten, die für erfolgreichen Pflanzenbau notwendig sind. Auffällig war der hohe Anteil an Farmen (90%) mit leicht bis sehr sauren Böden (pH<5.5). In den Böden von über 55% der Farmen lag der organischer Kohlenstoffgehalt unter 2%. Potentieller Nährstoffvorrat und -aufnahme der Böden waren sehr variabel. Flächen, die als sehr fruchtbar klassifiziert wurden, hatten ein dreifach höheres Vorratspotential an Stickstoff und Phosphor im Vergleich zu Flächen mit geringer Fruchtbarkeit. Der geschätzte potenzielle Maisertrag der Böden lag zwischen 1.6 t/ha und 2.8 t/ha. Der aktuelle Ertrag lag mit weniger als 1 t/ha jedoch darunter. Insgesamt waren die Farmer der Meinung, dass die Produktivität der Landnutzung, Tierhaltung, und Forst- und Wasserressourcen gesunken sei. Durch die Kombination verschiedener Erfassungs- und Monitoringmethoden konnten verschiedene Aspekte der Landdegradation und damit wichtige Informationen für die Entwicklung nachhaltiger Landnutzungsstrategien erfasst werden. Um Bodennährstoffmangel und niedrige Bodenproduktivität positiv zu verändern, müsste ein integriertes Bodenmanagement zur Erhöhung der Bodenfruchtbarkeit umgesetzt werden