Agricultural Suitability Research Articles

Determining agricultural suitable land in peri-urban geography using GIS and Multi Criteria Decision Analysis (MCDA) techniques

Land-use suitability analysis is of significance for the planning and management of cities. The main objective of this study was to develop a spatial model for land suitability assessment for peri-urban agricultural development with the use of geographic information systems (GIS) integrated with Multi Criteria Decision Analysis (MCDA) techniques. Physical attributes, land use, natural resources, accessibility, geology and soil properties were recognised as the key factors influencing land suitability for peri-urban agriculture in the study area. The study follows an integrated approach for the suitability assessment of agricultural development in Pendik District of Istanbul (Turkey) through integrating fuzzy logic model, Analytical Hierarchy Process (AHP) and the technique for order preference by similarity to ideal solution (TOPSIS) methods. Suitability classification of land was done in GIS by using the specified fuzzy membership functions. The weights of the main criteria and their sub-criteria were determined through the AHP method. Eight different zones were ranked according to agricultural development priority by the TOPSIS method. The area classified as suitable amounts to 27% of the total area with 0.07% classified as unsuitable. The highly suitable areas are mainly located in the north where there are villages specialised in agricultural production. The prioritisation of the suitable agricultural areas would assist in land use planning and urban management.

Integrating farmers’ decisions on the assessment of forest regeneration drivers in a rural landscape of Southeastern Brazil

Forest regeneration at large-scales is one of the main paths to achieving the ongoing ambitious restoration commitments. Thus, the identification of the main drivers of this process in agricultural landscapes is critical to understand the drivers determining restoration success. A growing number of studies have explored the biophysical and, less often, the socioeconomic drivers of forest regeneration using remote sensing approaches, but have not directly considered the influence of farmers’ decisions in spatial prediction models of forest regeneration. We explored the influence of biophysical and socioeconomic drivers on forest regeneration in a rural landscape of Southeastern Brazil, where native forest cover increased by 7.7%. We evaluated forest regeneration through the analysis of time series of multi-temporal satellite images and socioeconomic information obtained through a Social Survey with 47 landowners, using each farm as the study unit. Natural forest regeneration was mainly favored by lower land suitability for agriculture and higher proximity (lower distances) to forest remnants, as well by higher numbers of land-uses types in the farm and lower economic dependence of landowners from the farm income. Our results emphasize the importance of considering farmers’ decisions on predictive models of natural forest regeneration, which are critically needed to guide the implementation of large-scale forest restoration initiatives in agricultural landscapes.

Groundwater suitability for irrigated agriculture in Alluvial Bengal delta plain: Areview

<p>Groundwater is a vital source of irrigation and domestic purposes in Bangladesh, and hence, it must satisfy the water quality guidelines. The study has selected eleven (11) Districts of Bangladesh and collect secondary data regarding the irrigation water quality of groundwater. Several irrigation water indices, such as soluble sodium percentage (SSP), sodium adsorption ratio (SAR), magnesium adsorption ratio (MAR), residual sodium carbonate (RSC), Kelly’s ratio (KR), permeability index (PI), and irrigation water quality (IWQ) index are applied to evaluate irrigation water quality. According to IWQindex, the results showed 25.5% of water samples fell in highly suitability water type, whereas 33.5% exhibited low suitability type, and the remaining 41% were showed medium suitability for irrigation purposes. The values of SAR, SSP, RSBC, and MAR indices showed that about 31-64% of water samples were very good, whereas 5-20% were very poor for the same purpose. The results of TDS, EC, and total hardness showed good results as 88-93% of water samples fell in fair to excellent quality. The salinity hazard was found in the groundwater of coastal areas and completely unfit for irrigation. The study findings would help for improving the management of the groundwater resources for agricultural purposes in Bangladesh.</p>

Land suitability analysis using geographic information system (GIS): a case study in Soppeng district

The agricultural sector is a sector of the economy that is still the flagship in various regions in Indonesia, especially Soppeng regency. The area of Soppeng hills is approximately 800 km2 and is at an average altitude of 200 m above sea level, and has no coastal area. The utilization of Geographic Information Systems (GIS) promises resource management and modeling, especially quantitative models, to be more accessible and simpler. GIS is an efficient and effective way to know the characteristics of a region’s land and its development potential. Land suitability is the suitability of land for a particular purpose of use through the determination of land value (class) and land use patterns connected to the territory’s potential. It can be attempted more targeted land use along with its sustainability maintenance efforts. The spatial analysis involves modeling, testing, and interpreting model results to extract or form new information from a collection of geographic elements. This research will be carried out for 12 Months (1 year) through 4 stages. The research aims to identify land use’s suitability in the analysis of agricultural land suitability using geographical information systems in Soppeng regency area.

The economic and institutional determinants of trade expansion in Bronze Age Greater Mesopotamia

The focus of archaeologists on reconstructing exchange and communication networks in the past resulted in the enormous improvement of methods for analyzing material flows and detecting trade routes. However, our understanding of the determinants of trade patterns over time and space is still limited. To help tackle this issue, we study through regression analysis the rich economic and institutional experience of Bronze Age Greater Mesopotamia. Our testable predictions originate from three main economic theories of trade expansion. First, because of trade costs, mutually beneficial exchanges are discouraged by distance and encouraged by the relative size of markets. Second, trade expands when more suitable farming conditions in neighboring polities allow consumption risk-sharing. Finally, trade develops when interlocking exchange circuits ease the canalization of goods from the outside by providing secure routes, a more certain resolution of legal disputes and credit provision. Ordinary Least Squares—OLS—estimates based on data on 44 major Mesopotamian polities observed for each half-century between 3050 and 1750 BCE are consistent with these predictions. Our approach provides a robust theory-based empirical strategy for integrating archaeological, environmental, and historical data and calls for a tighter interdisciplinary cooperation.

Distinguishing anthropogenic and natural contributions to coproduction of national crop yields globally

Crop production is a crucial ecosystem service that requires a combination of natural and anthropogenic contributions to high and stable yields, which is a coproduction process. We analysed this coproduction based on nationally aggregated data for 15 major crops for 67 countries and the European Union with data for four time steps (2000, 2006, 2010, 2014). We found strong increases in fertilizer use, net capital stock and manure use intensity for lower-middle-income countries and stagnation or decrease of these for high-income countries. We used a multiple linear regression model predicting yield to distinguish the effect of anthropogenic contributions (crop-specific fertilizer use intensity, net capital stock intensity, manure use intensity) and natural contributions (crop-specific agricultural suitability, including soil characteristics, topography and climate). We found that in particular fertilizer use intensity, manure use intensity and agricultural suitability explained variation in yields to a considerable degree (R2 = 0.62).

Socioecological Dynamics Structuring the Spread of Farming in the North American Basin-Plateau Region

ABSTRACT The spread of agriculture is a major driver of social and environmental change throughout the Holocene, yet experimental and ethnographic data indicate that farming is less profitable than foraging, so why would individuals choose to adopt agriculture leading to its expansion? Ideal distribution models offer one framework to answer this question: Individuals should adopt less profitable subsistence strategies and occupy more marginal environments when local population density increases competition to the point where the suitability of the best strategies and habitats becomes equal to what can be gained in poorer strategies and habitats. Coupling radiocarbon-dated archaeological sites with a validated measure of agricultural suitability, we evaluate the emergence of farming in the Basin-Plateau region of North America. Our results show that farming first occurs in the more suitable Colorado Plateau physiographic region, and only spreads into the less suitable Great Basin after population density on the Plateau increases. This produces an approximate 300- to 400-year lag between the onset of farming on the Plateau and in the Basin. These findings support the ideal distribution hypothesis for the spread of farming, and suggest a general socioecological process that may help explain global patterns in the timing and tempo of agricultural expansions.

Projecting the urbanization effect on soil organic carbon stocks in polar and steppe areas of European Russia by remote sensing

This paper describes the effect of urbanization on soil organic carbon (SOC) stocks by projecting the main urban land cover classes over the initial pre-urban soil maps. Two cities different in climate and soil conditions as well as in availability of SOC data were chosen as the case studies. Rostov-on-Don is the center of Russian South, where croplands and natural steppes have been conventionally thoroughly studied by soil scientists. In contrast, soils of Murmansk located in Russian Arctics have always been overlooked due to low suitability for agriculture. Global, national and regional soil maps and databases were used to estimate pre-urban SOC stocks in the areas. The outcomes based on Harmonized World Soil Database were highly uncertain, underestimating 0-100 cm SOC stocks in the polar region and overestimating them in the steppe region, whereas the results based on Digital Soil Map of Russia and regional maps were comparable. Land cover structures of Rostov-on-Don and Murmansk were mapped based on the stepwise per-pixel and sub-pixel classification algorithms applied to Sentinel-2 and included the following classes: sealed soils, green lawns, trees and shrubs, bare soils and water. Murmansk was dominated by trees and shrubs (58.1%) with the proportion of area 17.5% covered by sealed soils. In Rostov-on-Don, less than 30% of the total area was covered by trees and shrubs which is also comparable with bare soils (19.6%)_and lawns (23.4%), whereas almost one third of the territory was sealed (27.6%). These land cover structures had a different impact on the topsoil SOC stocks: a 30-50% increase in Murmansk compared to the 18% decrease in Rostov-on-Don. An increase of the 0-100 cm SOC stocks was shown for both regions, however in the polar conditions it was two times higher compared to the steppe. In polar conditions, conversion of natural soils into urban non-sealed soils increased SOC stocks from 30% to more than 4 times in 0-10 cm layer and from 47% to almost 3 times in the 0-100 cm layer. The highest increase was reported for the lawns, whereas SOC under trees and shrublands were considerably lower. In Rostov-on-Don , sealed and bare soils stored less SOC compared to the initial natural soils. The conversion of natural areas into urban green infrastructure increased SOC up to 50-70%. Although the absolute SOC values based on the global and national legacy data are highly uncertain, especially for the polar areas, the research outcomes clearly reveal possible patterns in SOC changes induced by different urbanization pathways in contrast climatic conditions and highlight the complexity of the urbanization effect on soils

Applying the Best–Worst Method for land evaluation: a case study for paddy cultivation in northwest Turkey

Several multi-criteria decision-making methods are used in land suitability analyses. The main objective of this study is to present the potential use of the Best–Worst Method to determine agricultural land suitability. Study was conducted in 6837.26 ha land in Canakkale northwest Turkey. Nine land characteristics (texture, electrical conductivity, drainage, pH, depth, cation exchange capacity, organic matter content, soil fertility index (N, P, K and Zn content) and CaCO3%) were used. Data obtained from the Best–Worst Method were compared with the results of Analytical Hierarchy Process, and Storie Index method. According to the Best–Worst Method, 5.76% of the land was highly suitable, 58.37% were moderately suitable, 31.93% were marginally suitable, and 3.94% were not suitable for paddy cultivation. To Analytical Hierarchy Process, 5.76% of the land was highly suitable, 61.42% were moderately suitable, 29.01% were marginally suitable, and 3.94% were not suitable and with respect to Storie Index method, 5.76% were highly suitable, 0.20% were moderately suitable, 57.78% were marginally suitable, and 36.26% were not suitable for paddy cultivation. There was a statistically positive correlation between the Best–Worst Method and Analytical Hierarchy Process (r = .997) and negative correlation between Storie Index. So, results showed that the data generated with the use of Best–Worst Method were consistent, reliable and complied with the data of Analytical Hierarchy Process. The advantage of the Best–Worst Method to other methods is to conduct less pairwise comparisons and has more practical and fast algorithm. So, the Best–Worst Method can reliably use in crop-based land suitability analyses.

Development of an Australian Bread Wheat Nested Association Mapping Population, a New Genetic Diversity Resource for Breeding under Dry and Hot Climates.

Genetic diversity, knowledge of the genetic architecture of the traits of interest and efficient means of transferring the desired genetic diversity into the relevant genetic background are prerequisites for plant breeding. Exotic germplasm is a rich source of genetic diversity; however, they harbor undesirable traits that limit their suitability for modern agriculture. Nested association mapping (NAM) populations are valuable genetic resources that enable incorporation of genetic diversity, dissection of complex traits and providing germplasm to breeding programs. We developed the OzNAM by crossing and backcrossing 73 diverse exotic parents to two Australian elite varieties Gladius and Scout. The NAM parents were genotyped using the iSelect wheat 90K Infinium SNP array, and the progeny were genotyped using a custom targeted genotyping-by-sequencing assay based on molecular inversion probes designed to target 12,179 SNPs chosen from the iSelect wheat 90K Infinium SNP array of the parents. In total, 3535 BC1F4:6 RILs from 125 families with 21 to 76 lines per family were genotyped and we found 4964 polymorphic and multi-allelic haplotype markers that spanned the whole genome. A subset of 530 lines from 28 families were evaluated in multi-environment trials over three years. To demonstrate the utility of the population in QTL mapping, we chose to map QTL for maturity and plant height using the RTM-GWAS approach and we identified novel and known QTL for maturity and plant height.

Evaluation of Agricultural Land Suitability Based on RS, AHP, and MEA: A Case Study in Jilin Province, China

The suitability evaluation of agricultural land at the regional scale is of great significance for protecting land and water resources and building sustainable agricultural systems. Based on climate, soil, topographical, and surface water resources, land suitability index (LSI) data for maize, rice, and soybeans are established using an analytical hierarchy process and matter element analysis (AHP–MEA) model in Jilin Province, China. The results show that there is a significant positive linear correlation between the LSI and the measured yield, which indicates that the model has an ideal effect and certain reference and extension significance. The main limiting factors for maize and soybean planting are pH, total nitrogen (TN), available phosphorus (AP), and soil texture, while water shortage limits rice planting. Different spatial structure optimization schemes for planting are established using the LSI and measured yield, along with economic indices. This study shows that the scheme that integrates policy and cost can make full use of land and water resources and promote the economic growth of agriculture. After optimization, the planting areas of maize, rice, and soybeans were 7.22, 2.44, and 0.71 million ha, respectively, representing an increase of 15.71 billion yuan over the agricultural GDP for the existing planting structure. It is expected that this study will provide a basis for follow-up studies on crop cultivation suitability.

Can We Use Machine Learning for Agricultural Land Suitability Assessment?

It is vital for farmers to know if their land is suitable for the crops that they plan to grow. An increasing number of studies have used machine learning models based on land use data as an efficient means for mapping land suitability. This approach relies on the assumption that farmers grow their crops in the best-suited areas, but no studies have systematically tested this assumption. We aimed to test the assumption for specialty crops in Denmark. First, we mapped suitability for 41 specialty crops using machine learning. Then, we compared the predicted land suitabilities with the mechanistic model ECOCROP (Ecological Crop Requirements). The results showed that there was little agreement between the suitabilities based on machine learning and ECOCROP. Therefore, we argue that the methods represent different phenomena, which we label as socioeconomic suitability and ecological suitability, respectively. In most cases, machine learning predicts socioeconomic suitability, but the ambiguity of the term land suitability can lead to misinterpretation. Therefore, we highlight the need for increasing awareness of this distinction as a way forward for agricultural land suitability assessment.

Evaluation for Some Soils of Al-Bahariya Oases and The Optimal Planning for Their Agricultural Exploitation تقييم بعض أراضي الواحات البحرية والتخطيط الأمثل لاستغلالها زراعيا

The aim of this investigation is to study the characteristics of Al-Bawoiti soils for evaluating their capability and suitability for growing main crops using Remote Sensing (RS), Geographic Information Systems (GIS), and Sys, Model. Thirty-one representative soil profiles were selected. The profiles were morphological described and samples were collected representing the vertical variation for different laboratory analysis. According to the RS and GIS works three geomorphic units are recognized. These units are depression plain (18.06%), Aeolian plain (28.9%), and Pediplain (53.04%). The correlation between geomorphic unit and soils was carried out and then the soil maps where created using the ArcGIS 10.4.1 software. Based on the soil characteristics, the studied soil were evaluated according to their suitability for agriculture. In the current situation, they categorized into their capability classes namely, moderately suitable (S2=1.88%), marginally suitable (S3=85.87%), and not suitable (N=12.24%). These soils are suffering from limitations of texture class, salinity and alkalinity, topography and soil depth with different intensity degrees (slight, moderate, and severe). The severity of these limitations could be corrected by future land improvement according the potential suitability of the most studied soils could be improved to highly suitable (S1=0.2%), moderately suitable (S2=25.97%), marginally suitable (S3=73.53%), and not suitable (N2= 0.3%).Moreover, the suitability of 18 main crops in these soils was evaluated in the current and potential situation. The potential suitability of the soils for these crops could be improved according to the satisfaction conditions between soil properties and crops requirements. Keywords: Remote Sensing (RS), Geographic Information Systems (GIS), Land Evaluation.

Agricultural Land Suitability Analysis for Maize Crop by Using GIS Technology in Case of Debub Gondar Zone, Ethiopia

Food security is the current problem that affects the socio economic standard and living condition of the society. Land suitability analysis is a method of land evaluation which measures the degree of appropriateness of land for a certain use. The aim of this study was to identify and delineate the land that can best support maize production by using GIS technology. The study was carried out in Debub Gondar zone in Ethiopia. Even though agriculture is the main economic activity in the study area, its productivity is insufficient because some of the crops being introduced are not doing well. For this study, three specific objectives were mentioned. These are; identify the suitable area of maize in the study area, identify the unsuitable area of maize in the study area and compare current maize production with suitable and unsuitable area. To accomplish these study four parameters were used such as soil, slope, temperature and rainfall. The parameters were analyzed by ArcGIS 10.3 software. As result the largest portion of the study area was unsuitable for maize production and some part of northern, north western and eastern part of study area was partially suitable for maize production. Finally, we recommended that the concerned bodies should cultivate maize around the suitable part of the study area. Keywords: Debub Gondar, GIS, Maize, Suitability DOI: 10.7176/JEES/11-4-01 Publication date: April 30 th 2021

An attention U-Net model for detection of fine-scale hydrologic streamlines

Surface water is an irreplaceable resource for human survival and environmental sustainability. Accurate, finely detailed cartographic representations of hydrologic streamlines are critically important in various scientific domains, such as assessing the quantity and quality of present and future water resources, modeling climate changes, evaluating agricultural suitability, mapping flood inundation, and monitoring environmental changes. Conventional approaches to detecting such streamlines cannot adequately incorporate information from the complex three-dimensional (3D) environment of streams and land surface features. Such information is vital to accurately delineate streamlines. In recent years, high accuracy lidar data has become increasingly available for deriving both 3D information and terrestrial surface reflectance. This study develops an attention U-net model to take advantage of high-accuracy lidar data for finely detailed streamline detection and evaluates model results against a baseline of multiple traditional machine learning methods. The evaluation shows that the attention U-net model outperforms the best baseline machine learning method by an average F1 score of 11.25% and achieves significantly better smoothness and connectivity between classified streamline channels. These findings suggest that our deep learning approach can harness high-accuracy lidar data for fine-scale hydrologic streamline detection, and in turn produce desirable benefits for many scientific domains.

Challenges of soybean self-sufficiency policy in Indonesia

Indonesia deals with adverse soybean trade balance deficit as its domestic production is far below its demand. The study aims to analyze the challenges of achieving soybean self-sufficiency based on a survey in Lampung, West Java, Central Java, and West Nusa Tenggara Provinces in 2018. The special program (UPSUS) of soybean distinctively designed to achieve soybean self-sufficiency is characterized by heavily subsidized inputs, intensive practice on existing farm land area, as well as newly-planted area enhancement (PATB) on fragmented, less fertile and unsuitable land areas. Nevertheless, it results in unsatisfactory soybean production enhancement. Soybean farming is not competitive in term of farm business due to its relatively low yield and unfeasible farm-gate price. To enhance national soybean production, this crop should be grown on the suitable farm land at appropriate scale. A soybean variety resistant to insect-pests attack is mandatory to be adopted by farmers. In addition, the government needs to restructure the marketing aspect consisting of an effective floor price establishment through Bulog purchase with sufficient state budget allocation. It is also urgent to regulate soybean trade such as soybean import during off season as well as reasonable import tariff.

The Economic and Institutional Determinants of Trade Expansion in Bronze Age Mesopotamia

The focus of archaeologists on reconstructing exchange and communication networks in the past resulted in the enormous improvement of methods for analyzing material flows and detecting trade routes. However, our understanding of the determinants of trade patterns over time and space is still limited. To help tackle this issue, we study through regression analysis the rich economic and institutional experience of Bronze Age Mesopotamia. Our testable predictions originate from three main economic theories of trade expansion. First, because of trade costs, mutually beneficial exchanges are discouraged by distance and encouraged by the relative size of markets. Second, trade expands when more suitable farming conditions in neighboring polities allow consumption risk-sharing. Finally, trade develops when interlocking exchange circuits ease the canalization of goods from the outside by providing secure routes, a more certain resolution of legal disputes and credit provision. Ordinary Least Squares—OLS—estimates based on data on 44 major Mesopotamian polities observed for each half-century between 3050 and 1750 BCE are consistent with these predictions. Our approach provides a credible theory-based empirical strategy for integrating archaeological, environmental, and historical data and calls for a tighter interdisciplinary cooperation.

Pervasive cropland in protected areas highlight trade-offs between conservation and food security.

Global cropland expansion over the last century caused widespread habitat loss and degradation. Establishment of protected areas aims to counteract the loss of habitats and to slow species extinctions. However, many protected areas also include high levels of habitat disturbance and conversion for uses such as cropland. Understanding where and why this occurs may realign conservation priorities and inform protected area policy in light of competing priorities such as food security. Here, we use our global synthesis cropland dataset to quantify cropland in protected areas globally and assess their relationship to conservation aims and socio-environmental context. We estimate that cropland occupies 1.4 million km2 or 6% of global protected area. Cropland occurs across all protected area management types, with 22% occurring in strictly protected areas. Cropland inside protected areas is more prevalent in countries with higher population density, lower income inequality, and with higher agricultural suitability of protected lands. While this phenomenon is dominant in midnorthern latitudes, areas of cropland in protected areas of the tropics and subtropics may present greater trade-offs due to higher levels of both biodiversity and food insecurity. Although area-based targets are prominent in biodiversity goal-setting, our results show that they can mask persistent anthropogenic land uses detrimental to native ecosystem conservation. To ensure the long-term efficacy of protected areas, post-2020 goal setting must link aims for biodiversity and human health and improve monitoring of conservation outcomes in cropland-impacted protected areas.

Spatially Explicit Evaluation and Driving Factor Identification of Land Use Conflict in Yangtze River Economic Belt

Regional land use transitions driven by the adaptive reconciliation of existing land use conflict with socioeconomic development can lead to positive economic effects as well as new land use conflict. Although research on land use transition has progressed considerably, limited studies have explored the spatiotemporal dynamic pattern of land use conflict during the land use transition period. Previous evaluation approaches on land use conflict that mainly focus on status or potential conflict lack conflict intensity evaluation during the land use transition process. A new spatially explicit evaluation framework of land use conflict that directly examines three aspects of conflict, namely, ecological and agricultural (EAC), agricultural and construction (ACC), and ecological and construction (ECC) land conflicts based on ecological quality and agricultural suitability, is proposed in this study. The spatiotemporal dynamic pattern and driving factors of land use conflict in the Yangtze River Economic Belt of China in the period of 2000–2018 are evaluated. The results indicated that comprehensive land use conflict (CLUC) intensity slightly decreased by 9.91% and its barycenter showed a trend toward the west during 2000–2018. ACC is the most drastic conflict among the three aspects of conflict. The mean intensity of ACC reduced remarkably by 38.26%, while EAC increased by 33.15% and ECC increased by 28.28% during the research periods. The barycenter of EAC moved toward the east while the barycenter of ACC and ECC moved toward the west. The changes in the intensity and spreading pattern of land use conflict indices demonstrated the changes in the pattern of territorial space development. Total population, population density, per capita GDP, number of mobile phone users, and road density were strong drivers that influenced the land use conflict of territorial space. Multiple policy recommendations including improving territorial space planning and governance ability, and improving land use efficiency, were proposed to manage and resolve the land use conflict of territorial space. The results and conclusions of this study will help improve future regional land use policies and reduce land use conflict.

3D city models for urban farming site identification in buildings

Studies have suggested that there is farming potential in urban residential buildings. However, these studies are limited in scope, require field visits and time-consuming measurements. Furthermore, they have not suggested ways to identify suitable sites on a larger scale let alone means of surveying numerous micro-locations across the same building. Using a case study area focused on high-rise buildings in Singapore, this paper examines a novel application of three-dimensional (3D) city models to identify suitable farming micro-locations (level and orientation) in residential buildings. We specifically investigate whether the vertical spaces of these buildings comprising outdoor corridors, façades and windows receive sufficient photosynthetically active radiation (PAR) for growing food crops and do so at a high resolution. We also analyze the spatio-temporal characteristics of PAR, and the impact of shadows and different weather conditions on PAR in the building. Environmental simulations on the 3D model of the study area indicated that the cumulative daily PAR or Daily Light Integral (DLI) at a location in the building was dependent on its orientation and shape, sun's diurnal and annual motion, weather conditions, and shadowing effects of the building's own façades and surrounding buildings. The DLI in the study area generally increased with building's levels and, depending on the particular micro-location, was found suitable for growing moderately light-demanding crops such as lettuce and sweet pepper. These variations in DLI at different locations of the same building affirmed the need for such simulations. The simulations were validated with field measurements of PAR, and correlation coefficients between them exceeded 0.5 in most cases thus, making a case that 3D city models offer a promising practical solution to identifying suitable farming locations in residential buildings, and have the potential for urban-scale applications.