Crop Production Losses Research Articles

Monitoring of fruit fly, Bactrocera zonata (Diptera: Tephritidae) population by installing traps in mango orchard Bahawalnagar, Pakistan.

Mango is king of fruits and grown in various countries including Pakistan. The production of mango fruits in Pakistan is low due to attack of various insect pests especially fruit flies. Fruit flies are the major issue in the mango production. Fruit flies are considered as quarantine and key pests of Agricultural and horticultural crops causing huge losses in crop production. The current study was conducted at a farmer field in Bahawalnagar, to monitor the population of fruit flies installing different traps like bottle, jar, and pheromones traps (methyl eugenol) in mango orchards. Data were recorded from January, 2020-December, 2020. The results showed that maximum population of male fruit flies was captured in pheromones traps while minimum in jar traps. The population of fruit flies was emerged in month of April and no population was recorded in first three month and last one month of study. The June and July were the most favorable months for fruit fly attack on mango while December was least. Pheromones traps which are installed at 12 feet height from the ground surface captured 99% fruit fly population while lowest 30% population were captured at 4 feet height. Methyl eugenol trap were more effective to capture maximum population of fruit flies among tested traps. It can prove effective trap in controlling pest population in the mango orchards globally especially in Pakistan.

Effect of decision variables on yield and water productivity of onion under conventional furrow irrigation system in bako woreda, Ethiopia

Flow rate and furrow length are the main irrigation decision variables currently affecting yield and water productivity at farm level. Improper selection of these variables produces an over use of water and loss in crop production. The general objective was to investigate the effect of decision variables on yield and water productivity of onion under conventional furrow irrigation system, with specific objective to analyze the effect of flow rate, furrow length and their interaction on yield and water productivity of onion. The field experiment was laid out in randomized complete block design with factorial arrangement of three levels of flow rate (0.7, 0.98 and 1.3 L/S) and three levels of furrow length (25, 35 and 50 m) with three replications. Inflow out flow method was used to determine the infiltration characteristics of the soil and Irrigation depth was controlled by using 3-inch Parshall flume. The maximum non-erosive flow rate to the experimental site was fixed through design equation considering soil textural class and furrow bed slope. Effect of furrow length and flow rate on yield and water productivity of the onion were used for evaluation. Their analyses indicated that effect of furrow length and their interaction with flow rate on yield were not significant (p<0.05). However, the flow rate showed highly significant (p<0.01) effect on yield of onion. The ranges of mean yield gained from furrow length and flow rate were F1 (14.75 ton ha-1) to F3 (15.96 ton ha-1) and Q1 (13.59 ton ha-1) to Q3 (19.69 ton ha-1), respectively. The effect of furrow length on crop water use efficiency and field water use efficiency was not significant (p<0.05). However, the flow rate has showed highly significant (p<0.01) effect on crop water use efficiency and field water use efficiency. The range of mean crop water use efficiency and field water use efficiency from furrow length and flow rate were F1 (33.65 kg/ha/mm) to F3 (36.41 kg/ha/mm) and Q1 (30.99 kg/ha/mm) to Q3 (38.65kg/ha/mm) and F1 (2.06 kg/m3) to F3 (2.23 kg/m3) and Q1 (1.89 kg/m3) to Q3 (2.36 kg/m3), respectively. Therefore, it can be concluded that a furrow length of 50 m is suitable to use 1.3 L/S of flow rate for better onion yield and water productivity under similar soil type of study area.
 Int. J. Agril. Res. Innov. Tech. 11(1): 92-100, June 2021

Impacts of droughts and floods on croplands and crop production in Southeast Asia – An application of Google Earth Engine

While droughts and floods have intensified in recent years, only a handful of studies have assessed their impacts on croplands and production in Southeast Asia. Here, we used the Google Earth Engine to assess the droughts and floods and their impacts on croplands and crop production over 40 years from 1980 to 2019. Using the Palmer Drought Severity Index (PDSI) as the basis for determining the drought and flood levels, and crop damage levels, crop production loss in both the Monsoon Climate Region (MCR) and the Equatorial Climate Region (ECR) of Southeast Asia was assessed over 47,192 grid points with 10 × 10-kilometer resolution. We found that rainfed crops were severely affected by droughts in the MCR and floods in the ECR. About 9.42 million ha and 3.72 million ha of cropland was damaged by droughts and floods, respectively. We estimated a total loss of 20.64 million tons of crop production between 2015 and 2019. Rainfed crops in Thailand, Cambodia, and Myanmar were strongly affected by droughts, whereas Indonesia, the Philippines, and Malaysia were more affected by floods over the same period. Accordingly, four levels of policy interventions were prioritized by considering the geolocated crop damage levels.

Computer Vision Framework for Wheat Disease Identification and Classification Using Jetson GPU Infrastructure

Diseases have adverse effects on crop production and yield loss. Various diseases such as leaf rust, stem rust, and strip rust can affect yield quality and quantity for a studied area. In addition, manual wheat disease identification and interpretation is time-consuming and cumbersome. Currently, decisions related to plants mainly rely on the level of expertise in the domain. To resolve these challenges and to identify wheat disease as early as possible, we implemented different deep learning models such as Inceptionv3, Resnet50, and VGG16/19. This research was conducted in collaboration with Bishoftu Agricultural Research Institute, Ethiopia. Our main objective was to automate plant-disease identification using advanced deep learning approaches and image data. For the experiment, RGB image data were collected from the Bishoftu area. From the experimental results, the VGG19 model classified wheat disease with 99.38% accuracy.

ROS and Oxidative Response Systems in Plants Under Biotic and Abiotic Stresses: Revisiting the Crucial Role of Phosphite Triggered Plants Defense Response.

Phosphite (Phi) is a chemical analog of orthophosphate [HPO43−]. It is a systemic pesticide generally known to control the prevalence of oomycetes and soil-borne diseases such as Phytophthora, Pythium, and Plasmopora species. Phi can also control disease symptoms and the spread of pathogenic bacteria, fungi, and nematodes. Phi plays critical roles as a fungicide, pesticide, fertilizer, or biostimulator. Overall, Phi can alleviate the severity of the disease caused by oomycete, fungi, pathogenic bacteria, and nematodes (leave, stem, fruit, tuber, and root) in various plants (vegetables, fruits, crops, root/tuber crops, ornamental plants, and forests). Advance research in molecular, physiological, and biochemical approaches has approved the key role of Phi in enhancing crop growth, quantity, and quality of several plant species. Phi is chemically similar to orthophosphate, and inside the cells, it is likely to get involved in different features of phosphate metabolism in both plants and pathogens. In plants, a range of physiobiochemical alterations are induced by plant pathogen stress, which causes lowered photosynthesis activities, enzymatic activities, increased accumulation of reactive oxygen species (ROS), and modification in a large group of genes. To date, several attempts have been made to study plant-pathogen interactions with the intent to minimize the loss of crop productivity. Phi’s emerging function as a biostimulant in plants has boost plant yield and tolerance against various stress factors. This review discusses Phi-mediated biostimulant effects against biotic and abiotic stresses.

English

The fruit fly is an invasive pest that causes significant loss in horticultural crop production by damaging the fruit quality. For the integrated management of fruit fly, monitoring is a major component. Automated pest monitoring is most promising and advanced for monitoring and accurate assessment of the status of the fruit fly. The need of the hour is to automate and monitor pest populations using advanced technologies to save time and to examine the real-time situation of the field. The purpose of this paper is to discuss the approaches and sensors which detect and monitor fruit flies automatically.The review focuses on image-based sensors for identifying fruit flies, analyzing the wing beat biometric signature with optoelectronics sensors, direct counting with photo interruption sensors and E-Traps that can provide real-time field information, and presenting the various integrated systems available.With a real-time imaging system, all information about the fruit fly population and infection rate is recorded and maintained in a database. All of this has resulted in enhanced monitoring systems for fruit fly integrated pest management.

Food Security Under The Era Of Climate Change Threat

Food Security Under The Era Of Climate Change Threat

Exogenous Stilbenes Improved Tolerance of Arabidopsis thaliana to a Shock of Ultraviolet B Radiation.

Excessive ultraviolet B (UV-B) irradiation is one of the most serious threats leading to severe crop production losses. It is known that secondary metabolite biosynthesis plays an important role in plant defense and forms a protective shield against excessive UV-B irradiation. The contents of stilbenes and other plant phenolics are known to sharply increase after UV-B irradiation, but there is little direct evidence for the involvement of stilbenes and other plant phenolics in plant UV-B protection. This study showed that foliar application of trans-resveratrol (1 and 5 mM) and trans-piceid (5 mM) considerably increased tolerance to a shock of UV-B (10 min at 1800 µW cm−2 of irradiation intensity) of four-week-old Arabidopsis thaliana plants that are naturally incapable of stilbene production. Application of trans-resveratrol and trans-piceid increased the leaf survival rates by 1–2%. This stilbene-induced improvement in UV-B tolerance was higher than after foliar application of the stilbene precursors, p-coumaric and trans-cinnamic acids (only 1–3%), but less than that after treatment with octocrylene (19–24%), a widely used UV-B absorber. Plant treatment with trans-resveratrol increased expression of antioxidant and stress-inducible genes in A. thaliana plants and decreased expression of DNA repair genes. This study directly demonstrates an important positive role of stilbenes in plant tolerance to excessive UV-B irradiation, and offers a new approach for plant UV-B protection.

Management zone delineation and spatial distribution of micronutrients in cold-arid region of India.

The plant health is governed by many factors: soil playing a central role which exhibits huge variability in its characteristics. Micronutrients even though needed in small quantities by plants play an indispensable role in affecting the crop growth and development. The assessment of spatial variability of different soil parameters is incumbent for tackling the loss of crop productivity on account of non-receipt of desirable inputs. The present investigation centered on the spatial distribution of soil parameters and micronutrients was conducted to delineate management zones (MZs) in cold arid region (Kargil) of India for proficient micronutrient management. Overall 454 georeferenced representative soil samples at the depths of 0-15 cm were garnered. The soil samples were processed and analyzed for different soil parameters encompassing pH, EC (electrical conductivity), SOC (soil organic carbon), and available micro-nutrient (iron, manganese, zinc, copper and nickel) concentrations. The distinct variation in the soil properties including micronutrients was identified with coefficient of variation ranging as low as 5.62% to moderate (21.16 to 42.49%) and as high as 159.63%. Semivariogram analysis and ordinary kriging of soil variables under study revealed diverse spatial distribution exhibiting medium to high spatial dependence in the region. PCA (principal component analysis) and K-means clustering were expended for the delineation of MZs. Four principal components (PCs) having eigen values > 1 and accounting for 70% of the total variation were subjected to further analysis. The five potential MZs were demarcated on the basis of K-means cluster performance index, and heterogeneity in parameters was discerned. The results of study corroborate that the spatial variability analysis of different soil parameters for delineation/identification of MZs might be effectually employed for site-specific micronutrient management.

Rewilding crops for climate resilience: economic analysis and de novo domestication strategies.

To match predicted population growth, annual food production should be doubled by 2050. This is not achievable by current agronomical and breeding practices, due to the impact of climate changes and associated abiotic stresses on agricultural production systems. Here, we analyze the impact of global climate trends on crop productivity and show that the overall loss in crop production from climate-driven abiotic stresses may exceed US$170 billion year-1 and represents a major threat to global food security. We also show that abiotic stress tolerance had been present in wild progenitors of modern crops but was lost during their domestication. We argue for a major shift in our paradigm of crop breeding, focusing on climate resilience, and call for a broader use of wild relatives as a major tool in this process. We argue that, while molecular tools are currently in place to harness the potential of climate-resilient genes present in wild relatives, the complex polygenic nature of tolerance traits remains a major bottleneck in this process. Future research efforts should be focused not only on finding appropriate wild relatives but also on development of efficient cell-based high-throughput phenotyping platforms allowing assessment of the in planta operation of key genes.

Rapid and simple detection of Phytophthora cactorum in strawberry using a coupled recombinase polymerase amplification\u2013lateral flow strip assay

Phytophthora cactorum is a devastating pathogen that infects a wide range of plants and causes Phytophthora rot disease, which has resulted in great economic losses in crop production. Therefore, the rapid and practicable detection of P. cactorum is important for disease monitoring and forecasting. In this study, we developed a lateral flow recombinase polymerase amplification (LF-RPA) assay for the sensitive visual detection of P. cactorum. Specific primers for P. cactorum were designed based on the ras-related protein gene Ypt1; all 10 P. cactorum isolates yielded positive detection results, whereas no cross-reaction occurred in related oomycete or fungal species. The detection limit for the LF-RPA assay was 100 fg of genomic DNA under optimized conditions. Combined with a simplified alkaline lysis method for plant DNA extraction, the LF-RPA assay successfully detected P. cactorum in naturally diseased strawberry samples without specialized equipment within 40 min. Thus, the LF-RPA assay developed in this study is a rapid, simple, and accurate method for the detection of P. cactorum, with the potential for further application in resource-limited laboratories.

Assessment of O3-induced crop yield losses in northern China during 2013–2018 using high-resolution air quality reanalysis data

Long-term exposure of food crops to high concentrations of ambient ozone (O3) can cause significant yield reductions. O3-induced crop loss studies are limited in China, especially in the North China Plain (NCP) where agricultural resources are abundant and O3 concentrations are high. In this study, we quantify the O3-induced adverse impacts on wheat and maize over the NCP and Shanxi province during 2013–2018 through the use of high-resolution air quality reanalysis dataset and land-use dataset. Results show that the accumulated hourly O3 concentration above 40 ppb (AOT40) in croplands experienced an upward trend, with an annual increase of 2.2 ppm h (18.1%) during the wheat growing period and 1.5 ppm h (10.9%) during the maize growing period from 2013 to 2018. O3-induced relative yield losses grew from 17.9% in 2013 to 38.6% in 2018 for wheat, and ranged from 7.5 to 11.9% for maize. The estimated crop production losses also increased over time. Shandong and Hebei provinces are the hot spots of crop losses and priorities should be given to them for O3 pollution prevention. Comparison with previous studies shows that uncertainties still exist in crop loss estimations. More rural O3 measurements and localized crop exposure-response experiments should be performed for better assessments.

Biotechnological Resources to Increase Disease-Resistance by Improving Plant Immunity: A Sustainable Approach to Save Cereal Crop Production.

Plant diseases are globally causing substantial losses in staple crop production, undermining the urgent goal of a 60% increase needed to meet the food demand, a task made more challenging by the climate changes. Main consequences concern the reduction of food amount and quality. Crop diseases also compromise food safety due to the presence of pesticides and/or toxins. Nowadays, biotechnology represents our best resource both for protecting crop yield and for a science-based increased sustainability in agriculture. Over the last decades, agricultural biotechnologies have made important progress based on the diffusion of new, fast and efficient technologies, offering a broad spectrum of options for understanding plant molecular mechanisms and breeding. This knowledge is accelerating the identification of key resistance traits to be rapidly and efficiently transferred and applied in crop breeding programs. This review gathers examples of how disease resistance may be implemented in cereals by exploiting a combination of basic research derived knowledge with fast and precise genetic engineering techniques. Priming and/or boosting the immune system in crops represent a sustainable, rapid and effective way to save part of the global harvest currently lost to diseases and to prevent food contamination.

Role of Exopolygalacturonase-Related Genes in Potato-Verticillium dahliae Interaction.

Verticillium dahliae is a hemibiotrophic pathogen responsible for great losses in dicot crop production. An ExoPG gene (VDAG_03463,) identified using subtractive hybridization/cDNA-AFLP, showed higher expression levels in highly aggressive than in weakly aggressive V. dahliae isolates. We used a vector-free split-marker recombination method with PEG-mediated protoplast to delete the ExoPG gene in V. dahliae. This is the first instance of using this method for V. dahliae transformation. Only two PCR steps and one transformation step were required, markedly reducing the necessary time for gene deletion. Six mutants were identified. ExoPG expressed more in the highly aggressive than in the weakly aggressive isolate in response to potato leaf and stem extracts. Its expression increased in both isolates during infection, with higher levels in the highly aggressive isolate at early infection stages. The disruption of ExoPG did not influence virulence, nor did it affect total exopolygalacturonase activity in V. dahliae. Full genome analysis showed 8 more genes related to polygalacturonase/pectinase activity in V. dahliae. Transcripts of PGA increased in the △exopg mutant in response to potato leaf extracts, compared to the wild type. The expression pattern of those eight genes showed similar trends in the △exopg mutant and in the weakly aggressive isolate in response to potato extracts, but without the increase of PGA in the weakly aggressive isolate to leaf extracts. This indicated that the △exopg mutant of V. dahliae compensated by the suppression of ExoPG by activating other related gene.

Waterlogging tolerance: A review on regulative morpho-physiological homeostasis of crop plants

The natural environment is being drastically affected by climate change. Under these severe environmental conditions, the growth and productivity of agricultural crops have reduced. Due to unpredictable rainfall, crops growing in the field are often exposed to waterlogging. This leads to significant crop damage and production losses. In this review paper, the mor-phological and physiological adaptations such as development of aerenchyma, adventitious roots, radial root oxygen loss barrier, and changes in chlorophyll fluorescence parameters of crops under waterlogging are discussed. This will help to understand the effects of waterlogging on various crops and their adaptation that promotes crop growth and productivity. To meet the food requirements of a growing population, the development of waterlogging tolerant crops by screening and plant breeding methods is necessary for plant breeders. Better knowledge of physiological mechanisms in response to waterlogging will facilitate the development of techniques and methods to improve tolerance in crops.

Light Intensity Adjustment and Severity Estimation for the Generic Disease of Tomato Plants

Many research has been done on detecting the type of disease that affects the crops. Because of this the farmers use pesticides to reduce the loss of crop production, since they don’t know how much pesticides to spray or use, they tend to overuse them which eventually leads to further destruction of crops. For disease classification Convolutional Neural Network (CNN) is being used which lets you know what kind of disease has affected the crop. In this paper we have worked on self attention networks to calculate the severity of the disease on the leaf . Self Attention Network introduced in the architecture lets the model learn the feature more efficiently and focus more on the affected region of the leaf. The model was trained and tested on the standard dataset (Plant Village) . The core processes comprises image capturing, image processing and testing on Self Attention Convolutional Neural Network architecture.. All of the key steps required to implement the model are detailed throughout the document.

Long-term non-sustainable soil erosion rates and soil compaction in drip-irrigated citrus plantation in Eastern Iberian Peninsula

Agriculture is known to commonly cause soil degradation. In the Mediterranean, soil erosion is widespread due to the millennia-old farming, and new drip-irrigated plantations on slopes, such as the citrus ones, accelerate the process of soil degradation. Until now, the published data about soil erosion in citrus orchards is based on short-term measurements. Long-term soil erosion measurements are needed to assess the sustainability of drip-irrigated citrus production and to design new strategies to control high soil erosion rates. The objective of this study is to assess long-term soil erosion rates in citrus plantations and report the changes in soil bulk density as indicators of land degradation. We applied ISUM (Improved Stock-Unearthing Method) to 67 paired trees in an inter-row of 134 m (802 m2 plot) with 4080 measurements to determine the changes in soil topography from the plantation (2007) till 2020. Soil core samples (469) were collected (0–6 cm depth) to determine the soil bulk density at the time of plantation (2007) and in 2020. The results demonstrate an increase in soil bulk density from 1.05 g cm−3 to 1.33 g cm−3. Changes in soil bulk density were higher in the center of the row as a result of compaction due to passing machinery. Soil erosion was calculated to be 180 Mg ha−1 y−1 due to a mean soil lowering of 1.5 cm yearly. The highest soil losses were found in the center of the inter-row and the lowest underneath the trees. The extreme soil erosion rates measured in new drip-irrigated citrus plantations are due to soil lowering in the center of the inter-row and in the lower inter-row position where the incision reached 80 cm in 13 years. The whole field showed a lowering of the soil topography due to extreme soil erosion and no net sedimentation within the plantation. The results show the urgent need for soil erosion control strategies to avoid soil degradation, loss of crop production, and damages to off-site infrastructures.

Reconciling food production and environmental boundaries for nitrogen in the European Union

Meeting European policy targets for reducing nitrogen (N) pollution while maintaining crop production is a large challenge. Strategies to tackle this dual challenge should assess where reducing N losses is most needed while accounting for variation in agricultural systems and ecosystems' vulnerability to N loading. We used a spatially explicit N balance model (INTEGRATOR) to assess whether crop production targets and thresholds for N impacts on biodiversity and water quality in the EU can be reconciled by (i) redistributing N inputs from excess regions to regions where environmental thresholds are not exceeded and (ii) improving N management to reduce ammonia (NH3) emissions from manure and enhance field-level N use efficiency (NUE). At current NUE, reducing N inputs to comply with three environmental thresholds (critical N deposition on terrestrial ecosystems and critical N concentrations in surface water and groundwater) would reduce European crop production by 50%. The widespread exceedance of thresholds does not provide much room for redistribution: increasing inputs to close yield gaps on land where N thresholds are not exceeded can only increase crop production by 3%. To achieve surface water quality targets without crop production losses, average NUE needs to increase from 0.64 to 0.78, whereas achieving groundwater targets only requires a modest increase from 0.64 to 0.67. In hotspot areas, however, crop production and N thresholds can only be reconciled at NUEs of >0.90, which is not feasible. Reducing manure NH3 emission fractions to 0.10 by adopting best-management practices reconciles current crop production and thresholds for agricultural NH3 emission (in view of critical deposition) on only half of the agricultural area. In some regions, technologically feasible improvements in N management are thus insufficient to both maintain current crop production and respect environmental boundaries. Overall, the evaluated measures could reconcile ~80% of current EU crop production with N thresholds.

Estimating the impact of ground ozone concentrations on crop yields across China from 2014 to 2018: A multi-model comparison

Ground level ozone exerts a strong impact on crop yields, yet how to properly quantify ozone induced yield losses in China remains challenging. To this end, we employed a series of O3-crop models to estimate ozone induced yield losses in China from 2014 to 2018. The outputs from all models suggested that the total Relative Yield Losses (RYL) of wheat in China from 2014 to 2018 was 18.4%–49.3% and the total RYL of rice was 6.2%–52.9%. Consequently, the total Crop Production Losses (CPL) of wheat and rice could reach 63.9–130.4 and 28.3–35.4 million tons, and the corresponding Total Economic Losses (TEL) could reach 20.5–44.7 and 11.0–15.3 billion dollars, stressing the great importance and urgency of national ozone management. Meanwhile, the estimation outputs highlighted the large variations between different regional O3-crop models when applying to large scales.Instead of applying one unified O3-crop models to all regions across China, we also explored the strategy of employing specific O3-crop models in corresponding (and neighboring) regions to estimate ozone induced yield loss in China. The comparison of two strategies suggested that the mean value from multiple models may still present an inconsistent over/underestimation trend for different crops. Therefore, it is a preferable strategy to employ corresponding O3-crop models in different regions for estimating the national crop losses caused by ozone pollution. However, the severe lack of regional O3-crop models in most regions across China makes a robust estimation of national yield losses highly challenging. Given the large variations between O3-crop interactions across regions, a systematic framework with massive regional O3-crop models should be properly designed and implemented.

A new mobile application of agricultural pests recognition using deep learning in cloud computing system

Agricultural pests cause between 20 and 40 percent loss of global crop production every year as reported by the Food and Agriculture Organization (FAO). Therefore, smart agriculture presents the best option for farmers to apply artificial intelligence techniques integrated with modern information and communication technology to eliminate these harmful insect pests. Consequently, the productivity of their crops can be increased. Hence, this article introduces a new mobile application to automatically classify pests using a deep-learning solution for supporting specialists and farmers. The developed application utilizes faster region-based convolutional neural network (Faster R-CNN) to accomplish the recognition task of insect pests based on cloud computing. Furthermore, a database of recommended pesticides is linked with the detected crop pests to guide the farmers. This study has been successfully validated on five groups of pests; called Aphids, Cicadellidae, Flax Budworm, Flea Beetles, and Red Spider. The proposed Faster R-CNN showed highest accurate recognition results of 99.0% for all tested pest images. Moreover, our deep learning method outperforms other previous recognition methods, i.e., Single Shot Multi-Box Detector (SSD) MobileNet and traditional back propagation (BP) neural networks. The main prospect of this study is to realize our developed application for on-line recognition of agricultural pests in both the open field such as large farms and greenhouses for specific crops.