Different Types Of Crops Research Articles

Date Palm Crop Yield Estimation – A Framework

Saudi Arabia is the home land of the date palm tree and the dates are considered to be one of the most important national products. As the dates are part of their heritage, therefore, Saudi Arabia is the largest consumer of dates. Saudi Arabia has more than fifty date-processing facilities, which process large amounts of these products. Right now, Saudi Arabia ranks second in the production of dates. There are more than twenty-five million date palm trees that cover more than 150,000 hectares of land in Saudi Arabia. Date production is estimated to be more than 1.1 million tons each year which accounts for around seventy two percent of the total agricultural output of Saudi Arabia. It is very vital to predict the yield so that stakeholders will be prepared to market their product in a better way. Crop yield estimation can be done either through conventional method or through image processing methods. The former are often costly, complex, time consuming methods, that cannot be applied on a large scale. So, it is essential to employ those methods for crop yield estimation that are time saving as well as cost effective and image processing method fulfils these conditions. Since image processing extracts different features from an image that can be used not only in recognizing different types of crops but also estimating their yield. Recently, crop yield estimation have been developed using Artificial Neural Networks (ANN) have exhibited improved performance and self-adaptability as compared to traditional statistical methods. Keeping in view the importance of this topic, this paper presents a framework for crop yield estimation through image processing by using the ANN.

Assessing smallholder preferences for incentivised climate-smart agriculture using a discrete choice experiment

The promotion of climate smart agriculture (CSA) techniques to increase farmer resilience against climate change and improve their livelihoods is high on the international development agenda and aims to help achieve Sustainable Development Goals of food security (SDG 2), climate resilience and mitigation (SDG 13). We present the results of a discrete choice experiment (DCE) conducted in face-to-face interviews. In a study in Malawi, farmers responded to a series of questions about different cropping techniques and tree planting options to improve soil fertility and climate change resilience. A combination of financial and non-financial incentives was proposed to increase adoption and success rates. The results show that for different policy objectives, different climate smart packages are suitable. Our results demonstrate that farmers prefer options that secure the production of maize and include crops with both domestic use and local markets. The drought-resistant crop sorghum was unpopular among respondents; achieving SDG 13 through this CSA approach would therefore require high incentive payments. If CSA is to help achieve multiple goals e.g. poverty and inequality reduction (SDGs 1 and 10) as well as SDGs 2 and 13, a range of CSA packages, with different types of crops, rotation versus intercropping techniques and incentive levels, should be offered to smallholders.

CROP DATA RETRIEVAL USING EARTH OBSERVATION DATA TO SUPPORT AGRICULTURAL WATER MANAGEMENT

Irrigation water requirements (IWR) estimation requires the availability of accurate crop data, which can be acquired, along the crop growth season for a given region, through the use of Earth Observation (EO) time series. Besides, a relationship between the crop´s coefficients and the normalized difference vegetation index (NDVI) can be established allowing the crop´s evapotranspiration estimation. The main objective of the present work is to develop a methodology, and its implementation in a computational application, to extract crop’s parameters from EO images time series for a set of parcels of different types of crops, to be used as input data for a soil water balance model to compute IWR. The methodology was tested in two distinct test sites, Vila Franca de Xira (site I) and Vila Velha de Rodao (site II) municipalities, Portugal. Landsat 7 and 8 images acquired from April to October 2013 were used for site I, while SPOT-5 Take-5 images from April to September 2015 were considered for site II. EO data was used to estimate the basal crop’s coefficients, the planting dates and the lengths of crop’s growth stages. Based on crop, soil and meteorological data, IWR for the main crops of both test regions were estimated using the IrrigRotation model. The crop coefficients values obtained from EO data proved to be reliable for the IWR estimation.

Spatial and seasonal variation in occupation and abundance of common vole burrows in highly disturbed agricultural ecosystems

Understanding how species respond to disturbance in human-modified ecosytems is critical for management and conservation of biodiversity in the Anthropocene. In agroecosystems, human disturbances severely modify the habitat of species, particularly for those that live in burrows. The common vole Microtus arvalis (Pallas, 1778) is a semi-fossorial microtine, which often exhibits large abundance fluctuations, becoming an agricultural pest in peak years. We evaluated how both agrarian disturbances (via types of crop and their management) and landscape heterogeneity influenced the abundance of common vole burrow systems along a yearly cycle, at the field and landscape scales. We seasonally recorded the number of burrows and their recent occupation in circular plots of 200-m radius including different types of crops in intensified agrarian landscapes in NW Spain. Our results showed a marked seasonal and spatial pattern in both total abundance and abundance of occupied burrows. After a population peak year, only 31% of burrows were occupied across the year (from 41% in spring–summer to 12% in autumn). The crop type and its management in relation to soil disturbance were the main factors driving seasonal and spatial dynamics of burrow abundance at the field and landscape scale. Alfalfa fields held the highest abundance of both total and occupied burrow systems across the year, while fields of traditional-tilled cereal retained the lowest. As a result, at the landscape scale, plots with a greater surface devoted to traditional cereal crops maintained a lower relative number of burrow systems. Regarding the landscape structural heterogeneity, plots with longer length of field margins and lower area of watercourses maintained higher abundance of burrow systems. An adequate landscape-scale planning of crop types, agricultural practices, and distribution of non-crop habitats could be a promising sustainable method to reduce the risk of crop-damaging vole plagues.

Sistema Experto para la Rotación de Cultivos en un Invernadero

The production of greenhouse crops is one of the most modern techniques currently used in agricultural production. Crop rotation is an agricultural practice to increase the productivity of the land and optimize the use of resources by alternating different types of crops in the same soil. The project called "Expert System for the rotation of crops in a greenhouse" was developed with the aim of making the decision of the type of suitable crop to be planted in the "San Sebastian" greenhouse. Since the risk of production loss is considerably reduced , so that it helps the producer to know which is the suitable crop to sow according to the experience of an agricultural expert; using physical sensors, the registration of ambient and humidity temperatures is obtained in real time; the software was developed by the prototype model.

Deep blade loosening increases root growth, organic carbon, aeration, drainage, lateral infiltration and productivity

All cultivated soils are prone to rapid reconsolidation when wet, particularly structurally unstable soils. This predisposes them to waterlogging, slow and limited infiltration, rapid drying, and poor establishment, growth and production of crops. Field-scale research was undertaken to develop soil management and/or engineering practices that will maintain a stable, loose tilth with aeration, drainage and infiltration properties that prevent waterlogging, enhance infiltration and increase crop production.The effects of deep ripping plus gypsum, permanent raised beds (PRB), deep blade loosening at 250 mm depth (DBL) and no-tillage crop establishment (NT) were studied in controlled traffic regimes on rainfed and irrigated crops. The DBL treatment severs roots at about 250 mm depth and loosens without inverting the overburden. Experiments were undertaken from 1997 to 2011 on winter waterlog-prone unstable sandy loam over clay soils in Western Australia, on an unstable fine sandy clay loam in Pakistan and on a self-mulching clay in Queensland. All sites were operated on a field scale as part of the commercial operations of collaborating farmers. Root zone soil conditions were monitored with measurements (0–400 mm) of bulk density, tilth porosity, hydraulic conductivity, perched water table depth, root mass, organic carbon, total nitrogen and moisture profiles. Irrigation applications and crop yield were also recorded.Bulk density data on all experimental sites showed reconsolidation of the tilth created by deep ripping with or without gypsum in the NT treatment occurred substantially within one growing season. The reconsolidated NT treatment in Western Australia also had low hydraulic conductivity, air-filled porosity values of only two to 4% at a soil moisture potential of −250 mm (PRB height) and suffered long duration waterlogging events. The DBL-PRB treatment on these soils remained largely unconsolidated, had larger hydraulic conductivity, an air-filled porosity at −250 mm moisture potential of ≥10%, and did not suffer waterlogging. Modelling using drainage theory showed that DBL-PRB could be as wide as three metres and easily drain saturated beds within two days, thus precluding waterlogging.Root mass in the 0–400 mm in DBL-PRB treatment was, on average, seasonally 31% greater than the NT treatment. This increase in root mass is shown to have produced increases of 48% in soil organic carbon and 34% in total soil nitrogen content, and, over 19 cropping seasons with six different types of crop, the DBL-PRB treatment produced an annual average increase in grain yield of 23% over that of the NT treatment.The DBL-PRB treatment also substantially increased the amount and rate of lateral infiltration. Irrigation wetting fronts were both measured and predicted by infiltration theory to reach the centre of 2 m wide DBL-PRB in one third to one half of the time taken in NT-PRB.DBL practice has the potential to stabilise a deepened tilth in all soil types through the conservation of the structure and mass of enhanced root growth, which increases soil organic carbon, total soil nitrogen and crop productivity, as well as preventing waterlogging and improving irrigation efficiency.

Evaluation of Effect of Different Crop Types On Soil Water Erosion: Case Study of the Myjava Hill Land, Slovakia

The main objective of the study is to model and evaluate the effect of different types of crops on soil water erosion by using the physically-based EROSION-3D model. The EROSION-3D model is based on physical principles and simulates surface runoff, erosion, the deposition of material, and the detachment of soil particles for a single rainfall event. The erosion model was applied to a small catchment of Svacenicky Creek (6.86 km2), which is situated in the Myjava Hill Land in the western part of Slovakia; it is known for its quick runoff responses and related erosion processes. The parts most prone to erosion processes are located in the northern part of the Svacenicky Creek catchment on slopes with a more than 20% angle. Three crop types (winter wheat, silage corn and sugar beets) were chosen in consideration of their growing periods in order to assess the dynamic character of the erosion processes, especially during the growing period of each single crop. The modelling was performed for design rainfall events that were derived separately for each month and which were inputs to the model according to the growing period of the selected crops; six scenarios of the initial soil moisture in a range of 10-40% were designed. The results demonstrated the differences between the erosive effects of the crops selected during their growing periods. Finally, the results were statistically evaluated to determine the differences between the various scenarios of the initial moisture, different rainfall events, and selected crop types.

Image processing-based intelligent robotic system for assistance of agricultural crops

Agriculture has been practiced in conventional ways for centuries and supported with mechanical systems in the last few decades. With the evolution of robotic equipment and sensors, the researchers are focusing on introducing smart farming. In this paper, we propose improved algorithms for infection detection in leaves and field classification targeting a heterogeneous robotic system. Image processing methods have been used on the leaves of the plants to calculate the infection percentage in crops and elementary machine learning algorithm k-means clustering for classifying the field. Classification of the agricultural field has been done for growing different types of crops in a mixed cropping technique which has an advantage over other farming procedures. Early detection of diseases can help in better preventive measures in the early stages. We have used 3,150 images of crop diseases for three different types of crops and by smartly incorporating some previously established techniques. The primary objective of this paper includes the qualitative analysis of infection detection algorithms and further elaboration for the possible application of the suggested work in smart farming.

Image processing-based intelligent robotic system for assistance of agricultural crops

Agriculture has been practiced in conventional ways for centuries and supported with mechanical systems in the last few decades. With the evolution of robotic equipment and sensors, the researchers are focusing on introducing smart farming. In this paper, we propose improved algorithms for infection detection in leaves and field classification targeting a heterogeneous robotic system. Image processing methods have been used on the leaves of the plants to calculate the infection percentage in crops and elementary machine learning algorithm k-means clustering for classifying the field. Classification of the agricultural field has been done for growing different types of crops in a mixed cropping technique which has an advantage over other farming procedures. Early detection of diseases can help in better preventive measures in the early stages. We have used 3,150 images of crop diseases for three different types of crops and by smartly incorporating some previously established techniques. The primary objective of this paper includes the qualitative analysis of infection detection algorithms and further elaboration for the possible application of the suggested work in smart farming.

Date Palm Crop Yield Estimation a Framework

Saudi Arabia is the home land of the date palm tree and the dates are considered to be one of the most important national products. As the dates are part of their heritage, therefore, Saudi Arabia is the largest consumer of dates. Saudi Arabia has more than fifty date-processing facilities, which process large amounts of these products. Right now, Saudi Arabia ranks second in the production of dates. There are more than twenty-five million date palm trees that cover more than 150,000 hectares of land in Saudi Arabia. Date production is estimated to be more than 1.1 million tons each year which accounts for around seventy two percent of the total agricultural output of Saudi Arabia. It is very vital to predict the yield so that stakeholders will be prepared to market their product in a better way. Crop yield estimation can be done either through conventional method or through image processing methods. The former are often costly, complex, time consuming methods, that cannot be applied on a large scale. So, it is essential to employ those methods for crop yield estimation that are time saving as well as cost effective and image processing method fulfils these conditions. Since image processing extracts different features from an image that can be used not only in recognizing different types of crops but also estimating their yield. Recently, crop yield estimation have been developed using Artificial Neural Networks (ANN) have exhibited improved performance and self-adaptability as compared to traditional statistical methods. Keeping in view the importance of this topic, this paper presents a framework for crop yield estimation through image processing by using the ANN.

Smart Sustainable Agriculture (SSA) Solution Underpinned by Internet of Things (IoT) and Artificial Intelligence (AI)

The Internet of Things (IoT) and Artificial Intelligence (AI) have been employed in agriculture over a long period of time, alongside other advanced computing technologies. However, increased attention is currently being paid to the use of such smart technologies. Agriculture has provided an important source of food for human beings over many thousands of years, including the development of appropriate farming methods for different types of crops. The emergence of new advanced IoT technologies has the potential to monitor the agricultural environment to ensure high-quality products. However, there remains a lack of research and development in relation to Smart Sustainable Agriculture (SSA), accompanied by complex obstacles arising from the fragmentation of agricultural processes, i.e. the control and operation of IoT/AI machines; data sharing and management; interoperability; and large amounts of data analysis and storage. This study firstly, explores existing IoT/AI technologies adopted for SSA and secondly, identifies IoT/AI technical architecture capable of underpinning the development of SSA platforms. As well as contributing to the current body of knowledge, this research reviews research and development within SSA and provides an IoT/AI architecture to establish a Smart, Sustainable Agriculture platform as a solution.

OTIMALISASI PENGGUNAAN SUMBERDAYA LAHAN SAWAH TADAH HUJAN MELALUI PENERAPAN POLA USAHATANI DI DESA REMBITAN KECAMATAN PUJUT KABUPATEN LOMBOK TENGAH

ABSTRAK ABSTRACT In order to obtain maximum results in a farming system, especially farming in rainfed areas, where both planting and harvesting failure often occur, the determination or selection of crop and cropping patterns becomes important. Likewise, combining different types of crop and livestock farming in a particular farming pattern is an effort in order to increase the farmer's total income.This study aims to: (1) Identify the farming pattern or mixed farm applied by rainfed farmers; (2) Calculating the income of each farming pattern; (3) Optimalizing the rainfed farming in Rembitan Village, Pujut Sub-district. This research uses descriptive method. The analysis used are: income analysis, linear programming, and sensitivity analysis.The results showed that: (1) There are 10 farming patterns conducted by farmers in the rainfed areas of Rembitan Village, Pujut Sub-district, Central Lombok Regency; (2) The average total farming pattern income in the rainfed areas of Rembitan Village, Pujut Sub-district, Central Lombok Regency, which combines crop and livestock farming is Rp 12.263.596,00/year; (3) There are three recommended farming petterns activity in rainfed areas of 882 ha maximizing Rembitan Village revenue of Pujut Sub-district, Central Lombok Regency, Rp 20,649,455,129 per year with distribution, namely: X4 activity of 177,71 hectares (20,15 %); X7 of 145,633 hectares (16,51%); and X10 of 558,657 hectares (63,34%).

Urbanization and cattle density are determinants in the exposure to anticoagulant rodenticides of non-target wildlife

The persistence and toxicity of second generation anticoagulant rodenticides (SGARs) in animal tissues make these compounds dangerous by biomagnification in predatory species. Here we studied the levels of SGARs in non-target species of wildlife and the environmental factors that influence such exposure. Liver samples of terrestrial vertebrates (n = 244) found dead between 2007 and 2016 in the region of Aragón (NE Spain) were analysed. The presence of SGARs was statistically analysed with binary or ordinal logistic models to study the effect of habitat characteristics including human population density, percentage of urban surface, livestock densities and surface of different types of crops. SGARs residues were detected in 83 (34%) of the animals and levels >200 ng/g were found in common raven (67%), red fox (50%), red kite (38%), Eurasian eagle-owl (25%), stone marten (23%), Eurasian buzzard (17%), northern marsh harrier (17%), and Eurasian badger (14%). The spatial analysis revealed that the presence of SGARs residues in wildlife was more associated with the use of these products as biocides in urban areas and cattle farms rather than as plant protection products in agricultural fields. This information permits to identify potential habitats where SGARs may pose a risk for predatory birds and mammals.

Azole-resistant Aspergillus fumigatus in the Italian environment.

Azole resistance in Aspergillus fumigatus environmental and clinical isolates is recognised as an emerging problem worldwide. Development of azole resistance may be environmentally driven because of the massive use of azole fungicides in agriculture. The mechanism of azole resistance is mostly related to mutations in the cyp51A gene. A. fumigatus azole resistance in the environment was previously documented in northern Italy. This study extended the research in the agricultural environment also in central and southern Italy and investigated differences in the Italian geographical areas and in the different types of crops. A total of 177 samples (173 soil samples and 4 Dutch bulbs) collected in the period 2014-20117 in 14 Italian regions were analysed. Itraconazole-resistant A. fumigatus isolates grew in 16.9% of the screened samples. Differences were observed in soil samples from the three Italian geographic areas: 12.5% in the north, 15.2% in the centre and 24.1% in the south. Resistant isolates were from different cultivations, treated or officially not treated with azole fungicides. Sequencing of the cyp51A gene confirmed that resistance was mainly associated with the TR34/L98H mutation (29/30 isolates); 1 isolate showed the G54E mutation. The risk for patients to acquire multi-azole-resistant strains from the environment could have a serious impact on the management of life-threatening invasive infections. The azole resistance rate of 16.9% found in Italy requires suitable monitoring of antifungal susceptibility of clinical isolates.

INFLUÊNCIA DE DIFERENTES SUBSTRATOS NA PRODUÇÃO DE FITOCONSTITUINTES DE <i>Moringa oleifera </i>Lam. CULTIVADA A PLENO SOL

A análise de extratos de folhas de Moringa oleifera cultivadas em diferentes substratos oferece a oportunidade de se examinar um conjunto de compostos únicos, que podem ser utilizados para diversos fins, em nutrientes e na medicina. Levando-se esses fatores em consideração, foi montado um experimento em delineamento experimental, conduzido com blocos inteiramente casualizados, com sete tratamentos: (1) 100% neossolo quartzarênico; (2) neossolo quartzarênico 60% x 40% vermicomposto; (3) neossolo quartzarênico 80% x 20% vermicomposto; (4) neossolo quartzarênico 60% x 40% casca de arroz carbonizada; (5) neossolo quartzarênico 80% x 20% casca de arroz carbonizada; (6) neossolo quartzarênico 60% x 40% solo argiloso; e, (7) neossolo quartzarênico 40% x 60% solo argiloso, a pleno sol, quatro repetições por tratamento. A avaliação de crescimento da parte aérea foi realizada aos 105 dias, com a coleta das folhas, secas em estufa de ventilação forçada e posteriormente trituradas e pesadas. Com o material obtido foram preparados extratos metanólicos (20%) e aquosos (20%), submetidos à análise fitoquímica. Foi detectada a presença de compostos fenólicos, taninos, flavonoides, cumarinas, esteroides, heterosídeos cardioativos, alcaloides e açúcares redutores. Estes compostos são provavelmente os responsáveis em conferir amplo espectro de atividades biológicas atribuídas às folhas da espécie. Porém, o presente estudo sugere cautela em seu uso indiscriminado, devido à grande presença de heterosídeos cardioativos. A maior ou menor quantidade de metabólitos foi influenciada pelos diferentes substratos e, na dependência do tipo de utilização da espécie diferentes tipos de cultivos seriam necessários para aumentar ou diminuir determinados compostos presentes em suas folhas.

Optimization Model for Agricultural Reclaimed Water Allocation Using Mixed-Integer Nonlinear Programming

Reclaimed water (RW) is a reliable alternative water supply for irrigation in the agricultural sector, which is the predominant consumer of water in Iraq. A mixed-integer nonlinear programming reclaimed water allocation optimization model was developed to maximize the net benefit generated from the cultivation of different types of crops, comparing the use of reclaimed water type A (tertiary treated water), and reclaimed water type B (secondary treated water). The model was solved using the Algorithms for coNTinuous/Integer Global Optimization of Nonlinear Equations (ANTIGONE) optimizer in the general algebraic modeling system (GAMS). A total of 84 agricultural farms located on 5300 ha to the south of Baghdad, Iraq were available for irrigation with reclaimed water. Analysis considered varying quantities of available reclaimed water and different irrigation efficiencies (45–85%). The net benefits from using lower quantities of reclaimed water were similar for both types of reclaimed water, and the highest net benefit crop was cultivated on 384 ha. As the quantities of water increased, the amount of cultivated land increased and the net benefit per hectare decreased as the model required the cultivation of more crops with lower economic value. Irrigation with reclaimed water has potential to increase agricultural and economic activity adjacent to Baghdad.

Potential Applications of GNSS-R Observations over Agricultural Areas: Results from the GLORI Airborne Campaign

The aim of this study is to analyze the sensitivity of airborne Global Navigation Satellite System Reflectometry (GNSS-R) on soil surface and vegetation cover characteristics in agricultural areas. Airborne polarimetric GNSS-R data were acquired in the context of the GLORI’2015 campaign over two study sites in Southwest France in June and July of 2015. Ground measurements of soil surface parameters (moisture content) and vegetation characteristics (leaf area index (LAI), and vegetation height) were recorded for different types of crops (corn, sunflower, wheat, soybean, vegetable) simultaneously with the airborne GNSS-R measurements. Three GNSS-R observables (apparent reflectivity, the reflected signal-to-noise-ratio (SNR), and the polarimetric ratio (PR)) were found to be well correlated with soil moisture and a major vegetation characteristic (LAI). A tau-omega model was used to explain the dependence of the GNSS-R reflectivity on both the soil moisture and vegetation parameters.

Atrazine promotes immunomodulation by melanomacrophage centre alterations in spleen and vascular disorders in gills from Oreochromis niloticus

Atrazine is a herbicide that is banned in Europe but remains widely used on different types of crops in several countries in the American continent. Atrazine is known to be an endocrine disruptor and its effects on gonads have been extensively reported, but the toxic action on other organs is poorly documented. In this paper, we investigated the toxicity of atrazine on the gills and spleens of Nile tilapia (Oreochromis niloticus). The median lethal concentration (LC50), capable of killing one-half of the test animals was calculated, and sublethal concentrations of atrazine were used in a semistatic and subchronic assay. The following four experimental groups were formed: control not exposed to atrazine, a group exposed to 1 ppm atrazine for 15 days, a group exposed to 2 ppm for 7 days, and a group exposed to 2 ppm for 15 days. The concentrations were verified during the study by high performance liquid chromatography. The gills and spleens were stained with hematoxylin and eosin and histopathological findings were made. The Perls technique was used on the spleens to identify hemosiderin, lipofuscin, and melanin pigments in the cells from melanomacrophage centres (MMCs). The spleens were submitted to proliferating cell nuclear antigen (PCNA) and inducible nitric oxide synthase (iNOS) immunohistochemistry, and morphometry was used to assess splenocyte proliferation and melanomacrophage iNOS expression. Finally, a colorimetric assay for caspase-3 was performed on the spleens to identify apoptosis. Vascular and structural alterations, such as venous sinus congestion, aneurysm, hemorrhage, pillar cell hypertrophy, disarrangement of secondary lamellae, and epithelial lifting were observed in the gills. The frequency of individuals with aneurysms was higher in the groups treated with 2 ppm than in other groups. Atrazine had an immunomodulatory effect on the spleen, observed by the alteration in the percentage of red and white pulp, alteration of the MMC area, changes in the melanomacrophage pigment content, slight iNOS suppression, decrease in splenocyte proliferation under 1 ppm atrazine, and increased caspase 3 activity under 2 ppm atrazine after 7 and 15 d. Such effects could compromise oxygenation and the immune response and, ultimately, the survival and fitness of the fish.

Application of Doppler Radar for Wildlife Detection in Vegetation

Abstract The objective of this paper was to test the suitability of Doppler microwave radar for the detection of wild animals hidden in grassland in front of the harvester. The ability of Doppler radar HB100 sensor to detect a dog or a human person hidden behind different types of crops was tested in laboratory conditions. Relative movement between the radar and the observed object was secured by the assembly acting as mathematical pendulum. The radar always moved in front of different crop samples (arranged in two or one line). The dog or human person was situated behind the crop. In five out of seven cases, the sensor was able to detect the human person. Only in two out of seven cases, the sensor was able to detect the dog. Nevertheless, it can be concluded that microwave radar sensor can be a useful device for detection of wild animals in the crop. Next research is needed in order to better explain the influence of disturbing factors on the measurements.

Multi-Criteria Evaluation of Irrigated Agriculture Suitability to Achieve Food Security in an Arid Environment

This research aims at assessing land suitability for large-scale agriculture using multiple spatial datasets which include climate conditions, water potential, soil capabilities, topography and land management. The study case is in the Emirate of Abu Dhabi, in the UAE. The aridity of climate in the region requires accounting for non-renewable sources like desalination and treated sewage effluent (TSE) for an accurate and realistic assessment of irrigated agriculture suitability. All datasets were systematically aggregated using an analytical hierarchical process (AHP) in a GIS model. A hierarchal structure is built and pairwise comparisons matrices are used to calculate weights of the criteria. All spatial processes were integrated to model land suitability and different types of crops are considered in the analysis. Results show that jojoba and sorghum show the best capabilities to survive under the current conditions, followed by date palm, fruits and forage. Vegetables and cereals proved to be the least preferable options. Introducing desalinated water and TSE enhanced land suitability for irrigated agriculture. These findings have positive implications for national planning, the decision-making process of land alteration for agricultural use and addressing sustainable land management and food security issues.