1.

Introduction

The demand for food globally has risen tremendously and is expected to increase to 59–
98% by the year 2050 [1]. However, the growing concerns are that the agricultural food
production systems are unable to match the high demand, especially in poor nations, causing an
intensifying level of food insecurities [2]. The inefficiency of the food production systems is also
one of the reasons for food insecurity [3]. How best to facilitate increased food production
without jeopardizing land and water resources, energy, and the environment is a momentous task
that government and policymakers have to address [4].
In many low- and middle-income countries (LMICs), most of the food production is rural-
based, dominated by smallholder and subsistence farmers. Enhancing smallholders’
sustainability requires that farmers are empowered with practicable information that enables
them not only to make evidence-informed decisions but also to implement them in activities that
could increase their farm productivity and sustainability. In efforts toward transforming the weak
and often inefficient traditional subsistence production practices, sustainable production
approaches [5,6] that support production-efficiency-enhancement and better agronomic practices
are needed. These include planting climate-resilient crops, high-yielding crop varieties, crop
yield forecasting, integrated pest management, as well as integrating biodiversity solutions in
sustainable food production systems [7,8] Ultimately, these novel interventions would require
comprehensive, up-to-date datasets (spatial and non-spatial) and the adoption of advanced GIS
technologies that can synthesize and integrate social, spatial, economic, demographic, and
environmental data in agriculture. The output of this synthesis would be evidence-based spatial
knowledge that improves our understanding of agriculture sustainability and in supporting better
policies and decision-making processes.
Contemporary advances in Geographic Information Systems (GIS), Remote Sensing (RS),
and Geographic Positioning Systems (GPS) technologies present an opportunity to acquire and
operationalize high-resolution satellite imagery and digital spatial data [9]. In the agriculture
sector, these spatial data have aided in the investigation of the spatial linkages of social, physical,
agroecological, and environmental complexities and how they affect agriculture sustainability.
GIS technology provides users with a mixture of geo-spatial information management tools and
methods that allow users to collect, store, integrate, query, display, and analyze geospatial data at
various scales [10]. Remote sensing technology acquire images and other information about
crops and soil from sensors mounted on different platforms including satellites, airborne remote
sensing (manned drones and unmanned aerial vehicles), and ground-based equipment that is then
processed by computers to aid agricultural decision-making systems [11,12].
The spatial context of agriculture can be viewed from the perspective of farmers’
differentiated access to livelihood capitals, local resources, and access to essential infrastructure
and services existing in a locality. In a GIS system, the data containing each of these aspects can
be deconstructed as nested spatial layers, each rooted in local geography by geographic
coordinates captured using GPS [13]. These spatial layers can then be processed and analyzed in
a GIS system in multiple ways to reveal crop and soil conditions and spatial interactions, predict
crop trends, monitor land-use change, monitor pests, and in biodiversity conservation
[14,15,16,17]. They can also be used to map and reveal spatial impediments to agricultural
production, or even new information for improving agricultural sustainability.
In recent times, the increasing complexity associated with agriculture production systems
has aroused policymakers’ interest in investigating how the spatial aspect “dimension” of
agriculture can be exploited using advanced GIS, RS, and GPS technologies to improve
agricultural productivity and better production practices [18,19]. The integration of GIS
technologies in agriculture has increased the opportunities for the development of even better
spatial explicit frameworks that support the creation of dynamic agriculture databases and
interactive systems [20]. Such database systems allow users to interact with spatially referenced
agriculture data in real-time, while accurately providing precise positional data, thus providing
enhanced frameworks for decision making. New fields that apply GIS in agriculture have
emerged as a result. These include precision agriculture, automated farm systems, crop yield
forecasting, climate change detections, and the real-time monitoring of crop production
[11,12,21]. These have the capability of improving agricultural production and food security.
In this regard, several recent systematic literature reviews have been conducted to
illuminate and consolidate various ways GIS, RS, and GPS technologies have been applied in the
agriculture sector. García-Berná et al. [11] used a systematic mapping study to focus on the
current trend and what new opportunities in remote sensing techniques offer in agriculture. Their
study found increased uptake of RS technologies in the acquisition and extracting of
georeferenced data from satellite imagery and unmanned aerial vehicles. Spatial data from these
technologies have been applied in several areas including crop growth and yield estimation,
cropland parameter extraction, weed and disease detection, and the monitoring of water and
nutrients in plants. How this application could be integrated to improve spatial-based agriculture
policymaking was not elaborated by the authors. The Al-Ismaili [22] review highlighted the
integrated application of RS and GIS techniques in precision agriculture and in the mapping,
detection, and classification of the greenhouse through aerial images and satellites. How such a
technique could be assimilated into enhancing policymaking was not mentioned. In yet another
meta-review, Weiss et al. [12] research highlighted the emerging development in RS that
strengthens the specific application of RS in crop breeding, agricultural land use monitoring,
crop yield forecasting, and biodiversity loss. Sharma, Kamble, and Gunasekaran [23] focused on
how GIS data applications have assisted in the development of precision agriculture. The authors
proposed a framework, “Big GIS Analytic”, to guide how big GIS data should be applied in the
agriculture supply chain. Their framework also lays a foundation for a theoretical structure for
improving the quality of GIS data application in agriculture to elevate productivity. These studies
help us to understand how GIS and RS applications in agricultural production systems have
advanced. However, the available systematic reviews seem not to explicitly provide how GIS
and RS technologies could enhance the integration of the spatial dimension of agriculture into
policy frameworks and interventions.
There is an increasing demand for evidence-supported decision making to assist
policymakers in assessing the local needs of farmers, improving production and supply value
chains, and developing spatial based-interventions. In this regard, this review aimed at
synthesizing existing evidence on GIS and RS application in agriculture in enhancing evidence-
informed policies for improving agriculture sustainability and identifying obstacles for their
application, particularly in LMICs. The review draws on a decade of literature, from 2011 to
2021, to examine the current and future perspectives on integrating GIS in policies that support
agriculture sustainability.

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Open AccessReview

Application of GIS in Agriculture in Promoting Evidence-Informed Decision Making for

Improving Agriculture Sustainability: A Systematic Review
by

Mwehe Mathenge

1,*
,

Ben G. J. S. Sonneveld

2
and

Jacqueline E. W. Broerse

Department of Urban Management, School of Planning and Architecture, Maseno University,

Maseno 40105, Kenya

Athena Institute, Faculty of Science, Vrije Universiteit Amsterdam, De Boelelaan 1085, 1081
HV Amsterdam, The Netherlands

Author to whom correspondence should be addressed.

Sustainability 2022, 14(16), 9974; https://doi.org/10.3390/su14169974

Submission received: 22 June 2022 / Revised: 18 July 2022 / Accepted: 25 July

2022 / Published: 12 August 2022
(This article belongs to the Special Issue Advanced Technologies, Techniques and Process for
the Sustainable Precision Agriculture)

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Abstract

The objective of this review was to synthesize existing evidence on GIS and RS application in
agriculture in enhancing evidence-informed policy and practice for improving agriculture
sustainability and identifying obstacles to their application, particularly in low- and middle-
income countries. Systematic searches were conducted in the databases SCOPUS, Web of
Science, Bielefeld Academic Search Engine, COnnecting REpositories (CORE), and Google
Scholar. We identified 2113 articles published between 2010–2021, out of which 40 articles met
the inclusion criteria. The results show that GIS technology application in agriculture has gained
prominence in the last decade, with 66% of selected papers being published in the last six years.
The main GIS application areas identified included: crop yield estimation, soil fertility
assessment, cropping patterns monitoring, drought assessment, pest and crop disease detection
and management, precision agriculture, and fertilizer and weed management. GIS technology has
the potential to enhance agriculture sustainability through integrating the spatial dimension of
agriculture into agriculture policies. In addition, GIS potential in promoting evidenced informed
decision making is growing. There is, however, a big gap in GIS application in sub-Saharan
Africa, with only one paper originating from this region. With the growing threat of climate
change to agriculture and food security, there is an increased need for the integration of GIS in
policy and decision making in improving agriculture sustainability.

Keywords:
GIS; RS; spatial autocorrelation; policy integration; agri-spatial policy
integration; spatial; sustainable agri-food systems

1. Introduction

The demand for food globally has risen tremendously and is expected to increase to 59–
98% by the year 2050 [1]. However, the growing concerns are that the agricultural food
production systems are unable to match the high demand, especially in poor nations, causing an
intensifying level of food insecurities [2]. The inefficiency of the food production systems is also
one of the reasons for food insecurity [3]. How best to facilitate increased food production
without jeopardizing land and water resources, energy, and the environment is a momentous task
that government and policymakers have to address [4].

In many low- and middle-income countries (LMICs), most of the food production is rural-
based, dominated by smallholder and subsistence farmers. Enhancing smallholders’
sustainability requires that farmers are empowered with practicable information that enables
them not only to make evidence-informed decisions but also to implement them in activities that
could increase their farm productivity and sustainability. In efforts toward transforming the weak
and often inefficient traditional subsistence production practices, sustainable production
approaches [5,6] that support production-efficiency-enhancement and better agronomic practices
are needed. These include planting climate-resilient crops, high-yielding crop varieties, crop
yield forecasting, integrated pest management, as well as integrating biodiversity solutions in
sustainable food production systems [7,8] Ultimately, these novel interventions would require
comprehensive, up-to-date datasets (spatial and non-spatial) and the adoption of advanced GIS
technologies that can synthesize and integrate social, spatial, economic, demographic, and
environmental data in agriculture. The output of this synthesis would be evidence-based spatial
knowledge that improves our understanding of agriculture sustainability and in supporting better
policies and decision-making processes.

Contemporary advances in Geographic Information Systems (GIS), Remote Sensing (RS),

and Geographic Positioning Systems (GPS) technologies present an opportunity to acquire and
operationalize high-resolution satellite imagery and digital spatial data [9]. In the agriculture
sector, these spatial data have aided in the investigation of the spatial linkages of social, physical,
agroecological, and environmental complexities and how they affect agriculture sustainability.
GIS technology provides users with a mixture of geo-spatial information management tools and
methods that allow users to collect, store, integrate, query, display, and analyze geospatial data at
various scales [10]. Remote sensing technology acquire images and other information about
crops and soil from sensors mounted on different platforms including satellites, airborne remote
sensing (manned drones and unmanned aerial vehicles), and ground-based equipment that is then
processed by computers to aid agricultural decision-making systems [11,12].

The spatial context of agriculture can be viewed from the perspective of farmers’
differentiated access to livelihood capitals, local resources, and access to essential infrastructure
and services existing in a locality. In a GIS system, the data containing each of these aspects can
be deconstructed as nested spatial layers, each rooted in local geography by geographic
coordinates captured using GPS [13]. These spatial layers can then be processed and analyzed in
a GIS system in multiple ways to reveal crop and soil conditions and spatial interactions, predict
crop trends, monitor land-use change, monitor pests, and in biodiversity conservation
[14,15,16,17]. They can also be used to map and reveal spatial impediments to agricultural
production, or even new information for improving agricultural sustainability.

In recent times, the increasing complexity associated with agriculture production systems
has aroused policymakers’ interest in investigating how the spatial aspect “dimension” of
agriculture can be exploited using advanced GIS, RS, and GPS technologies to improve
agricultural productivity and better production practices [18,19]. The integration of GIS
technologies in agriculture has increased the opportunities for the development of even better
spatial explicit frameworks that support the creation of dynamic agriculture databases and
interactive systems [20]. Such database systems allow users to interact with spatially referenced
agriculture data in real-time, while accurately providing precise positional data, thus providing
enhanced frameworks for decision making. New fields that apply GIS in agriculture have
emerged as a result. These include precision agriculture, automated farm systems, crop yield
forecasting, climate change detections, and the real-time monitoring of crop production
[11,12,21]. These have the capability of improving agricultural production and food security.

In this regard, several recent systematic literature reviews have been conducted to
illuminate and consolidate various ways GIS, RS, and GPS technologies have been applied in the
agriculture sector. García-Berná et al. [11] used a systematic mapping study to focus on the
current trend and what new opportunities in remote sensing techniques offer in agriculture. Their
study found increased uptake of RS technologies in the acquisition and extracting of
georeferenced data from satellite imagery and unmanned aerial vehicles. Spatial data from these
technologies have been applied in several areas including crop growth and yield estimation,
cropland parameter extraction, weed and disease detection, and the monitoring of water and
nutrients in plants. How this application could be integrated to improve spatial-based agriculture
policymaking was not elaborated by the authors. The Al-Ismaili [22] review highlighted the
integrated application of RS and GIS techniques in precision agriculture and in the mapping,
detection, and classification of the greenhouse through aerial images and satellites. How such a
technique could be assimilated into enhancing policymaking was not mentioned. In yet another
meta-review, Weiss et al. [12] research highlighted the emerging development in RS that
strengthens the specific application of RS in crop breeding, agricultural land use monitoring,
crop yield forecasting, and biodiversity loss. Sharma, Kamble, and Gunasekaran [23] focused on
how GIS data applications have assisted in the development of precision agriculture. The authors
proposed a framework, “Big GIS Analytic”, to guide how big GIS data should be applied in the
agriculture supply chain. Their framework also lays a foundation for a theoretical structure for
improving the quality of GIS data application in agriculture to elevate productivity. These studies
help us to understand how GIS and RS applications in agricultural production systems have
advanced. However, the available systematic reviews seem not to explicitly provide how GIS
and RS technologies could enhance the integration of the spatial dimension of agriculture into
policy frameworks and interventions.

There is an increasing demand for evidence-supported decision making to assist

policymakers in assessing the local needs of farmers, improving production and supply value
chains, and developing spatial based-interventions. In this regard, this review aimed at
synthesizing existing evidence on GIS and RS application in agriculture in enhancing evidence-
informed policies for improving agriculture sustainability and identifying obstacles for their
application, particularly in LMICs. The review draws on a decade of literature, from 2011 to
2021, to examine the current and future perspectives on integrating GIS in policies that support
agriculture sustainability. The main contributions of the study are to provide readers and
policymakers with evidence on how GIS technology has been used in the agriculture sector to
improve agricultural production practices and inform how the technology can be adopted to
improve evidence-based decision making and policies. This paper is structured as follows: after
the introduction, we describe the methodology applied to select and review the articles; then, we
detail the findings in Section 3. In Section 4, we highlight obstacles to applying GIS in
agriculture policy and practice. Lastly, Section 5 and Section 6 give the conclusion and the
limitations of the study.

2. Review Methodology

2.1. Process of Screening

The search used the bibliographic databases SCOPUS, Web of Science/Clarivate, Bielefeld
Academic Search Engine (BASE), and COnnecting REpositories (CORE), as well as Google
Scholar. The following inclusion criteria were employed to screen for titles and abstracts: (1) full
articles in peer-reviewed journals; (2) articles published between January 2010 and October
2021; (3) written in the English language; and (4) those associated with the application of GIS or
RS in agriculture. The following search string was applied as index terms to search: “TITLE,
ABSTRACT (Agriculture* OR Plant OR Crop*) AND (GIS OR Geographic OR Information
OR Systems) AND (Remote OR Sensing OR RS)”. The full search syntax is found in Table
A1 in the Appendix. Following the eligibility criteria, a total of 2113 articles were found; 701
articles were identified from SCOPUS, 104 from Web of Science, 468 from Bielefeld Academic
Search Engine (BASE), 68 from CORE, and 238 records from Google Scholar. After excluding
duplicates and studies for which no full text or access was available (988 articles), 1223 articles
were eligible for further screening.

The flow of the screening process is shown in Figure A1 in the Appendix A. The first
screening was based on the title and abstract checking for relevance to the purpose of this article,
based on which a substantial number of articles (n = 554) were excluded. Further exclusion
criteria were based on (1) articles focusing on the general application of GIS, i.e., suitability
analysis and site selection analysis (n = 81) and (2) irrelevant topics or focus (n = 171). After the
exclusion of these articles, 97 articles were subjected to secondary screening through full article
reading, which resulted in the exclusion of 57 articles. After the final screening, 40 articles were
selected for the analysis.
2.2. Data Extraction and Analysis
Full reference records for selected articles were exported to the Mendeley reference
manager and Microsoft Excel to enable coding and analysis. We extracted data using a
standardized form and included the following descriptive data: author(s); year of study; journal;
location; research objectives/questions; and main methods, findings, and conclusions. The
included articles were analyzed through thematic analysis, combining both deductive and
inductive coding.

3. Results

3.1. Characterization of the Selected Papers

In total, 20 journals published the selected papers (Figure 1), with the top four journals
being Elsevier (26% of the articles), Springer (21%), MDPI (9%), and PLOS ONE (9%).
Affiliate journals of Elsevier where the papers were published included Agricultural Systems,
Chemosphere, Science of the Total Environment, Agricultural Water Management, Field Crops
Research, Computers and Electronics in Agriculture, Applied Geography, Computers and
Electronics in Agriculture, and Catena. The Springer journal affiliates included Nature, Earth
Systems and Environment, Nutrient Cycle Agroecosystem, Precision Agriculture, and
Environment Monitoring Assess, while the MDPI affiliate journals included Sustainability and
Agriculture.
Figure 1. Publication sources of selected papers.

The selected articles covered diverse fields of GIS applications that were published in
various years and based in diverse regions as shown in Table 1.

Table 1. Characteristics of the included records.

The most frequent fields of application were crop yield estimation and forecasting (30%)
and soil fertility assessment (22.5%). Eighteen countries were identified in the selected papers
where the research was conducted. Grouped by region, we found that East Asia and Pacific
countries were the most frequent, accounting for 35% of the total, including Australia (n = 4);
Bangladesh (n = 1); Indonesia (n = 1); China (n = 7); and Russia (n = 1). South Asia accounted
for 27.5% including India, (n = 7); Pakistan, (n = 1); and Iran (n = 3). North America accounted
for 10% of the total, including the USA (n = 3) and Canada (n = 1). Middle East and North
Africa accounted for 17.5% including Saudi Arabia, (n = 2); UAE, (n = 1); Morocco, (n = 1);
and Egypt, (n = 3). GIS applications in Europe and Central Asia accounted for 7.5% of the total,
including Ireland, Ukraine, and Turkey, each with (n = 1). Sub-Saharan Africa had the least
articles, with only one (Ethiopia, n = 1) accounting for 2.5%.

The most frequent type of GIS application methodologies identified in the selected papers
are presented in Figure 2. More than half (27 papers) accounting for 67.5% of the selected
papers used GIS in their methodologies; 8 papers (20%) integrated both GIS and RS, while 5
papers (12.5%) used RS techniques.

Figure 2. Number of papers using GIS, RS, or a combination of the two in their methodology.

3.2. GIS Application in Agriculture and the Implication to Policy

The main field of study for the selected papers was categorized into seven application areas
(Table 2). These include crop yield estimation/forecasting (26% of the papers), soil fertility
assessment (18%), cropping patterns and agricultural monitoring (13%), drought assessment
(16%), pest and crop disease detection and management (11%), precision agriculture (8%), and
fertilizer and weed management (8%).
Table 2. Classification of main types of research topics addressed in the selected papers.

We expound on how GIS was applied in the selected papers according to the research topic
in the sections below.

3.2.1. Crop Yield Estimation/Forecasting

Monitoring crop growth and early crop yield forecasting over agricultural fields is an
important procedure for food security planning and agricultural economic return prediction. The
continued advancement in RS and GIS technologies has improved the process and techniques of
monitoring the development of crops and estimating their yields [26,29,31]. Several studies
demonstrate the application of integrated GIS and RS technologies in crop yield estimation.
Memon et al.’s [24] study demonstrated how integrating multispectral Landsat satellite imagery
and comparing different RS-based spectral indices were effective in measuring the percentage of
wheat straw cover and successively determining its effect on yields of rice crops. The knowledge
can inform long-term planning of agriculture sustainability in rice-wheat cropping systems. The
result of the research by Hassan and Goheer [27] showed that the accurate early estimation of
wheat crop yield before harvesting can be determined by using vegetation indices derived from
moderate resolution imaging spectroradiometer satellite imagery and crop yield data and the GIS
modelling approach. In yet another study, Hassan and Goheer [28] used a GIS-based
environment policy integrated climate model that provided a practical tool for simulating rice
crop yield. The model combined regional level crop level data, soil data, farm management data,
and climatic data to spatially estimate variations in crop yield. Likewise, Al-Gaadi et al. [29]
extracted the normalized difference vegetation index and soil-adjusted vegetation index from
Landsat satellite images acquired during the potato growth stages to predict potato tuber crop
yield. GIS- and RS-based crop yield forecasting models could have a wider application in
informing spatially based agriculture policies. For example, based on the output of these models,
policy intervention can be designed to manipulate the specific contributors to crop yields (which
include farm management techniques, weather conditions, water availability, altitude, terrain,
plant health, and policy intervention) [25,30]. Forecasting crop yields well before harvest is
crucial, especially in a region characterized by climatic uncertainties. Monitoring agricultural
crop growth conditions and the prediction of potential crop yield is important in planning and
policymaking for food security and agricultural economic return prediction [26,28,31]. This
could include developing policies for improving agriculture productivity and sustainability [28].
In feeding a growing population in LMICs, agricultural production systems must strive to reduce
the food production yield gap between current yields achieved by farmers and those potentially
attainable in rainfed subsistence farming systems. In addressing this mismatch, the study by
Hochman et al. [30] developed a model that integrated statistical yield and cropping area data,
remotely sensed data, cropping system simulation, and GIS mapping to assess and map wheat
yield gaps.

3.2.2. Soil Quality/Fertility Assessment

Soil quality assessment is critical for designing sustainable agricultural practices (optimal
agricultural use) that can help bridge the current food production and demand gap in overcoming
the food security problem. The availability of RS datasets and GIS spatial modelling techniques
provides new opportunities for measuring/evaluating soil quality at different spatial scales
[33,36]. Shokr et al. [35] developed a spatially-explicit soil quality model by combining soil’s
physical, chemical, and biological properties and integrating these with a digital elevation model
and Sentinel-2 satellite image to produce digital soil maps. Abdelfattah and Kumar [32] describe
the application GIS-enabled web-based soil information system that provides a descriptive,
quantitative, and geospatial soil database in a simple interface. The system was applied to
determine the sufficiency potential of soils for plant growing and management. Using GIS and
RS technologies, Abdellatif et al. [38] developed a spatial model for the assessment of soil
quality. His model combined four main soil quality indices (soil fertility index, soil physical
index, soil chemical index, and geomorphological properties Index) and GIS ordinary kriging
spatial interpolation to map the soil quality index. The application of these GIS-based models
provides evidence-based ways to improve soil quality management. This would enable decision
makers, policy formulators, land-use planners, and agriculturalists to efficiently manage soil
resources to ensure the sustainable use of agricultural lands according to their potential
[34,36,37]. Thus, assessing soil quality indicators would be important for sustainable agricultural
policies and practices and in achieving food security.
3.2.3. Crop Mapping and Monitoring Decision Support Systems
In an era of unpredictable climate changes, agricultural crop monitoring analysis could help
government policymakers and farmers plan and design cropping patterns that adapt to water
availability. Agricultural monitoring systems integrate multiple geospatial data sets and cropping
system models into computer algorithms to spatially compute and simulate optimum scenarios
for site-specific conditions for crop production [42]. A crop monitoring system is developed by
integrating geospatial data obtained by high-resolution remote sensing with a web GIS geoportal
interface [41]. Santosh and Suresh [39] demonstrated the uniqueness of combining GIS and RS
in a tool for crop selection and rotation analysis at the farm level to improve crop management
decisions. Cropping patterns simulation is determined by irrigation water availability, which in
turn is affected by changes in climate and irrigation water extraction policies. Wang et al. [16]
combined GIS and irrigation water availability simulation models to analyze the cropping
patterns based on the forecast of irrigation water availability. A GIS web-based crop mapping
and monitoring decision support system at the farm level could help farmers to access
information and take appropriate measures to improve crop production [39]. Such a system can
have a wider application in supporting agronomic decision making including optimizing land and
labor productivities, enhancing higher cropping intensities, and producing better crop yield [40].
This can increase crop production and ensure better crop management, in the long run, and
precision irrigation management.

3.2.4. Agricultural Drought Assessment

Using spatial datasets generated by satellite RS and GIS technologies offers very useful
information for assessing and modelling agricultural drought-risk patterns, monitoring drought
conditions, and producing drought vulnerability (risk) maps [48]. Hoque et al. [43] integrated
geospatial techniques with fuzzy logic to develop a comprehensive spatial drought risk inventory
model for operational drought management. This model successfully identified the spatial
extents and distribution of agricultural drought risk. Sehgal and Dhakar [44] used GIS and high
spatial resolution RS-derived indicators of crop sensitivity to develop a methodology that
assessed and mapped, at a local scale, key biophysical factors contributing to agricultural
drought vulnerability. The drought vulnerability maps could inform policymakers in formulating
spatially explicit policies for drought mitigation and intervention strategies [45,46]. In addition,
vulnerability maps could be used to indicate where socioeconomic development policy programs
should be given priority [47].

3.2.5. Pest and Crop Disease Detection and Management

Several geospatial tools and techniques continue to be developed to aid farmers in crop
disease control and management strategies. Several studies [49,50,51,52] provide practical
application of satellite RS data and Geospatial techniques for sustainable crop disease detection
and management. RS technology including Airborne and satellite imagery acquired during
growing seasons has been used for early- and within-season detection, mapping of some crop
diseases, the control of recurring diseases in future seasons, and assessing economic loss caused
by frost damage [49]. Santoso et al. [50] used high-resolution QuickBird satellite imagery to
effectively detect spatial patterns of oil palm plants infected by basal stem rot disease. They used
six vegetation indices derived from visible and near-infrared bands satellite imagery to
successfully discriminate between healthy and infected oil palms. Using precision agriculture
technologies and remote sensed imagery, Yang [52] showed how site-specific fungicide
application to disease-infested areas has been implemented for effective control of the disease. In
the future, new approaches that apply geoinformation technologies in monitoring and
management of pest and crop disease detection could reduce the effect of pesticides and
herbicide chemicals on the environment.

3.2.6. Precision Agriculture

In precision agriculture, automated geospatial analysis and decision support algorithms can
provide valuable scientific information to policymakers for better agriculture policy
development. Precision agriculture practices, which employ integrated GIS, RS, and GPS
technologies, have gained prominence in their ability to optimize crop production, facilitate site-
specific crop management, and reduce the application of agrochemicals. Toscano et al. [58]
demonstrated the usefulness of Sentinel-2 and Landsat-8 images to depict the within-field spatial
variability of wheat yield, which is key for adopting precision farming techniques. This provided
a potential alternative to traditional farming practices by improving site-specific management
and agricultural productivity. García et al. [59] tested the performance of remote sensing drones
as mobile gateways to provide a guide to the optimal drone parameters for successful Wi-Fi data
transmission between sensor nodes and the gateway in precision agriculture systems. The study
successfully demonstrated that drones (flying at the lowest velocity, at a height of 24 m, and with
an antenna with 25 m of coverage) can be used as a remote sensing tool to gather the data from
the nodes deployed on the fields for crop monitoring and management. This had the potential to
increase the adoption of precision agriculture by even smallholder farmers. Segarra et al.’s [60]
study specifically focused to understand the European Space Agency’s twin Sentinel-2 satellites’
features and their application in precision agriculture. Their study highlights that Sentinel-2 has
dramatically increased the capabilities for agricultural monitoring and crop management, abiotic
and biotic stresses detection, improved the estimation of crop yields, enhanced crop type
classifications, and provided a variety of other useful applications in agriculture. All of these
contribute to increasing the adoption of precision agriculture, which leads to more productive
and sustainable agriculture management and environmental sustainability [61,62]. In precision
agriculture, plantation-rows extraction using satellite image-based solutions is essential in crop
harvesting, pest management, and plant grow-rate predictions. The study of Fareed and Rehman
[55] used GIS and RS to design an automated method to extract plantation rows from a drone-
based image point clouds-based digital surface model. The automatic plantation rows extraction
can be used to quantify plantation-row damage assessment in precision agriculture.

3.2.7. Weed Management and Fertilizer Decision Support System

Accurate weed distribution mapping could greatly enhance efficiency in weed management
and reduce weed damage, overhead costs of herbicide application, and the rationalization of
fertilizers [57]. Dunaieva et al. [21] used GIS technologies to produce accurate weed distribution
maps in rice farms. This information improved the efficiency of input application, thus reducing
the consumption of inputs including herbicides, fungicides, and weeding labor costs. This in turn
reduced the weed damage and crop production overhead costs. Xie et al. [56] demonstrated the
application of GIS in the development of a GIS-based Fertilizer Decision Support System
(FDSS) by integrating RS data, field surveys, and expert knowledge to develop a soil spatial
database on the SuperMap platform for crop management systems. The application of FDSS in
agricultural production had benefits, such as increasing fertilizer utilization efficiency, thus
lowering production costs.

4. Obstacles to Applying GIS in Agriculture Policy and Practice

Generally, the use of GIS and RS technologies is not a panacea to successful evidence-based
policy and practice and has its downside. The success of the geospatial technology application
depends on its proper use, quality data, and considerable resources in its management. In
countries that suffer low resources, such as LMICs, the cost of the technology and lack of
appropriate skills jeopardize its wider use and adoption [63]. Simulating crop yield production is
always challenging due to the variety of cropping systems and levels of technology used.
Accurate crop yield gap assessment would require improvements in input data quality, including
accurate weather parameters, better soil characterization, and spatially distributed land use data
[28]. It would also demand the setting up of instrumented geo-referenced validation sites that
provide comprehensive survey data to inform a continuous improvement cycle for yield gap
assessment [30]. As such, future improvements in current remote sensing technology and the
development of better-integrated cropping systems models would provide more accurate inputs
for yield gap assessment.

In drought vulnerability assessment and mapping, most studies reported in the literature tended
to use aggregated spatial data at higher spatial scales (national or regional level), but not at a
finer scale (i.e., local level). Since the intensity of drought hazards is more felt and manifested at
the local level, a detailed drought-risk mapping at a finer scale would require high resolution
remote sensing and the use of locally contextual indicators to yield a full picture of vulnerability.
This would have more relevance to policymakers whose intent is to formulate and implement
mitigation interventions at the local level. With the prediction of more severe and frequent
drought uncertainties and increasingly threats from climate change, drought-risk mapping that
incorporates all the spatially explicit risk components would be a highly efficient contribution to
drought-mitigating strategies. More skills and knowledge on the use of geospatial techniques for
agricultural drought risk are needed.

In crop disease detection, challenges still exist in mapping them using airborne or satellite
imagery. Although many crop diseases can be successfully detected and mapped using satellite
imagery, each disease has its characteristics that would require different procedures for detection
and management. According to Yang, [52] “some diseases are difficult to detect, especially when
multiple biotic and abiotic conditions with similar spectral characteristics exist within the same
field” (pg. 531). Recurring diseases would require consistent historical imagery and spatial-
temporal data, while emerging diseases are more difficult to detect. Yang argues that more
advanced RS imaging sensors and image-processing techniques for differentiating diseases from
other confounding factors are needed. In less-developed countries, very few farmers have the
necessary skills required to use RS technologies in creating their prescription maps, in the
implementation of disease management and in the site-specific fungicide application. More
research is needed in the development of integrated geospatial analytical methodologies and
tools for aiding farmers in the detection of different crop diseases.

Although precision agriculture technologies can aid in optimizing crops and facilitating
agricultural management decisions in solving food insecurity challenges in LMICs, precision
farming requires the adoption of geospatial technology and a large amount of high-resolution
spatiotemporal data. A lack of skills to use GIS and RS in LMICs can be augmented by the
dissemination and the transfer of practical geospatial technologies from developed countries
[51]. However, considerable investments in ICT infrastructure are needed for the effective
adaptation of precision agricultural approaches in LMICs.

Soil fertility assessment is considered one of the most important indicators of precision
farming and for the sustainable use of agricultural lands according to their potential. This
requires a comprehensive soil information system. However, according to Abdelfattah and
Kumar [32], much of the world has very poor coverage of soil quality data. In LMICs, the
fragmentation of agricultural land into small uneconomical plots and unsustainable farming
practices is happening at a much higher rate. In such a rapidly changing environment, the
potential of active remote sensors to determine soil quality requires further research.

Other obstacles to the use and adoption of GIS and RS in agriculture include a lack of
commonly agreed data interoperability standards. Though there is increasing availability of
spatial data usage in LMICs, many of these data are prone to error and are often collected and
stored with different spatial units, formats, metadata, time, and space intervals. This makes some
data unusable, prevents spatial data integration, and hinders a unified analysis of data, especially
those collected from multiple sensors and platforms. A need exists on developing standardized
guidelines for agriculture spatial data. Training for researchers, practitioners, and farmers on how
to collect quality and accurate spatial data that can be usable in multi-platform systems is
paramount. Developing spatial data repositories with better interoperability can enable data
integration and improve the efficiency of data analyses. In this regard, crowdsourced data
collection would be a promising contribution to developing cost-effective agri-spatial data
repositories.

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