Crop Growth Stages Research Articles

Adaptive Month Matching: A Phenological Alignment Method for Transfer Learning in Cropland Segmentation

The increasing demand for food and rapid population growth have made advanced crop monitoring essential for sustainable agriculture. Deep learning models leveraging multispectral satellite imagery, like Sentinel-2, provide valuable solutions. However, transferring these models to diverse regions is challenging due to phenological differences in crop growth stages between training and target areas. This study proposes the Adaptive Month Matching (AMM) method to align the phenological stages of crops between training and target areas for enhanced transfer learning in cropland segmentation. In the AMM method, an optimal Sentinel-2 monthly time series is identified in the training area based on deep learning model performance for major crops common to both areas. A month-matching process then selects the optimal Sentinel-2 time series for the target area by aligning the phenological stages between the training and target areas. In this study, the training area covered part of the Mississippi River Delta, while the target areas included diverse regions across the US and Canada. The evaluation focused on major crops, including corn, soybeans, rice, and double-cropped winter wheat/soybeans. The trained deep learning model was transferred to the target areas, and accuracy metrics were compared across different time series chosen by various phenological alignment methods. The AMM method consistently demonstrated strong performance, particularly in transferring to rice-growing regions, achieving an overall accuracy of 98%. It often matched or exceeded other phenological matching techniques in corn segmentation, with an average overall accuracy across all target areas exceeding 79% for cropland segmentation.

Enhancing Paddy Yields with Advanced Nitrogen Fertilizers: A Study of Neem-coated Urea, Agrotain Urea and Nano Nitrogen

Aims: To evaluate and compare the performance of nano nitrogen, agrotain urea, and neem-coated urea in improving nitrogen use efficiency (NUE), crop yield, and sustainability in kharif (Vanakalam) and rabi (Yasangi) rice crops in the Southern Telangana Zone of India. Study Design: A field experiment was conducted in a randomized block design with 11 treatments and three replications to assess the effects of various nitrogen sources and application on rice productivity. Place and Duration of Study: The field study was planned from 2021 to 2023 at Institute of Soil Health Management, Agricultural Research Institute, PJTSAU, Rajendranagar, Hyderabad, from 2021 to 2023. Methodology: The experiment included treatments with varying nitrogen levels applied through nano nitrogen, agrotain urea, and neem-coated urea, including 100% Recommended Dose of Nitrogen (RDN), STCR (Soil Test Crop Response) based nitrogen, and reduced nitrogen doses (50% and 33% RDN). Dry matter yield at tillering, panicle initiation and harvest stages, grain and straw yield were measured at key growth stages (tillering, panicle initiation, and harvest) of rice crop. Results: In the kharif season, the highest dry matter yield at panicle initiation was recorded with 100% RDN using STCR based neem coated urea (7007 kg ha⁻¹), followed by agrotain urea (6199 kg ha⁻¹) and Neem coated urea (6019 kg ha⁻¹). Nano-ntrogen sprays with reduced nitrogen doses (50% and 33% RDN) showed significantly lower yields, with 50% RDN-STCR-based NCU yielding 5826 kg ha⁻¹ and 33% RDN-STCR-based NCU yielding 4899 kg ha⁻¹. In the rabi season, grain yields were highest with 100% RDN from STCR based N-NCU (7382 kg ha⁻¹), Agrotain urea (6741 kg ha⁻¹) and NCU (6561 kg ha⁻¹), while reduced nitrogen doses resulted in lower yields, ranging from 5886 kg ha⁻¹ (50% RDN) to 5142 kg ha⁻¹ (33% RDN) with STCR based N-NCU. Straw yield during kharif was highest with 100% RDN treatments (6540–7422 kg ha⁻¹), with reduced nitrogen doses and Nano-N sprays resulting in lower yields. The system straw yield showed the highest values with 100% RDN-STCR-based NCU (16277 kg ha⁻¹), followed by agrotain urea (14888 kg ha⁻¹) and NCU (14284 kg ha⁻¹). Nano-N sprays alone produced a system straw yield of 8119 kg ha⁻¹, higher than the control (7732 kg ha⁻¹), but significantly lower than 100% nitrogen treatments. Conclusion: Full nitrogen applications (100% RDN) through innovative fertilizers like agrotain urea, and neem coated urea resulted in significantly higher crop yields and nutrient uptake compared to reduced nitrogen doses. The study highlights the critical role of adequate nitrogen in improving nitrogen use efficiency and sustaining rice production systems, with substantial yield gains observed at 100% RDN (up to 44% for grain yield and 37% for straw yield).

Environmental and management determinants of blackleg crown canker disease (Leptosphaeria maculans) of canola (Brassica napus)

AbstractBlackleg disease (Leptosphaeria maculans) impacts canola (Brassica napus) productivity worldwide, but the factors driving severity are not well defined. This study characterised blackleg disease progression from leaf lesion appearance to crown canker development. Leaf lesion severity at four crop growth stages and crown canker severity were empirically associated in crops planted at different times. The biotrophic growth rate of L. maculans in planta was determined at temperatures between 12 and 28 °C. Data from 24 Australian field experiments on spring B. napus cultivars were analysed to relate infection timing relative to crop growth stage and site conditions to crown canker severity at maturity. Despite variations in lesions across seasons and planting times, no significant relationship was found between lesion severity or incidence and crown canker severity. Crown canker severity increased significantly when younger plants were infected. The average in planta hyphal growth rate of L. maculans was 0.13 mm/°C-day. The analyses of biotrophic hyphal growth alongside the timing of infection were used to identify the onset of crown canker development. Simulations showed that thermal time and plant available water accounted for 61% and 36% of the variance in crown canker severity for moderately susceptible and moderately resistant genotypes, respectively. Blackleg crown canker severity is a nuanced interplay between time of infection relative to crop growth stage, genetic resistance, temperature, and moisture. Disease-crop models incorporating these factors could be used to explore how, for example, sowing time, level of host genetic resistance or extreme weather conditions affect disease infection and crop yield.

Energy-Efficient Wireless Multimedia Sensor Nodes for Plant Proximal Monitoring.

The paper presents a double-radio wireless multimedia sensor node (WMSN) with a camera on board, designed for plant proximal monitoring. Camera sensor nodes represent an effective solution to monitor the crop at the leaf or fruit scale, with details that cannot be retrieved with the same precision through satellites or unnamed aerial vehicles (UAVs). From the technological point of view, WMSNs are characterized by very different requirements, compared to standard wireless sensor nodes; in particular, the network data rate results in higher energy consumption and incompatibility with the usage of battery-powered devices. Avoiding energy harvesters allows for device miniaturization and, consequently, application flexibility, even for small plants. To do this, the proposed node has been implemented with two radios, with different roles. A GPRS modem has been exclusively implemented for image transmission, while all other tasks, including node monitoring and camera control, are performed by a LoRaWAN class A end-node that connects every 10 min. Via the LoRaWAN downlink, it is possible to efficiently control the camera settings; the shooting times and periodicity, according to weather conditions; the eventual farming operations; the crop growth stages and the season. The node energy consumption has been verified in the laboratory and in the field, showing that it is possible to acquire one picture per day for more than eight months without any energy harvester, opening up further possible implementations for disease detection and production optimization.

Nutrient Uptake of the Individual Crops in Maize and Groundnut Cropping System Grown under Different Irrigation Systems with Varied Irrigation and Nitrogen Levels

An experiment was conducted to assess the effects of irrigation systems, irrigation and N levels on nutrient uptake of rabi maize and summer groundnut during 2021-22 and 2022-23 at the College Farm, College of Agriculture, Rajendranagar, PJTAU, Hyderabad. The experiment composed of 18 treatment combinations (3 irrigation systems as main plot, 3 irrigation levels as sub-plot and 2 nitrogen levels as sub-sub-plot) in a split plot design replicated thrice. The study revealed that irrigation systems, irrigation and N levels and the interaction of irrigation systems and irrigation levels influenced the nutrient uptake of maize and groundnut crops during both the years of experimentation. Significantly higher NPK uptake was observed with sub-surface drip irrigation (M2), irrigation level at 1.2 Epan;IW/CPE (S1) and N level at 100 per cent RDN (N1). The interaction of irrigation systems and irrigation levels (M x S) was significant while the interaction of N levels with irrigation systems (M x N) and irrigation levels (N x S) was not significant for both rabi maize and summer groundnut. The interaction of irrigation systems and irrigation levels revealed that in both maize and groundnut crops at earlier crop growth period the M3S1 (surface irrigation with 1.2 IW/CPE) resulted in significantly higher NPK uptake followed by M2S1 and M2S2 (sub-surface drip irrigation with 1.2 and 0.9 Epan). However at later crop growth stages due to prolonged fertigation schedules in micro irrigation systems, the higher NPK uptake was observed with M2S1 and M2S2 followed by surface drip irrigation system with 1.2 Epan (M1S1) and surface irrigation system with 1.2 IW/CPE (M3S1) while M3S3 resulted in lowest NPK uptake at all crop stages and in grains & stover/haulm during both the consecutive years (2021-22 and 2022-23).

Physiological impact of water deficit during growth stages of the wheat crop

Physiological impact of water deficit during growth stages of the wheat crop

The Effect of Spray Regimes on the Population Dynamics of Selected Field Pests and Their Effect on Grain Yield and Yield Components of Common Bean in Uganda.

In Uganda, the common bean (Phaseolus vulgaris) is often infested by a complex of insect pests, but bean flies, aphids, bean leaf beetles, and flower thrips are the most important. Whereas yield losses due to these pests have been established, there is limited information on their population dynamics at different stages of crop growth and their effect on yield and yield components. In order to describe the population dynamics of selected common bean pests at various phases of bean crop growth, and their impact on yield and yield components, a study was carried out in Uganda during the 2016 second rains and the 2017 first rains in three agro-ecological zones. Bean flies, bean aphids, bean leaf beetles, whitefly, striped bean weevil, leafhoppers, and caterpillars were the main insects observed. Pesticide spray schedules were imposed to generate different populations of insect pests whose effects on yield and its components were determined. The findings indicate that spray regimes significantly influenced the abundance of bean flies and leafhoppers, but not the other insect pests. Additionally, except for caterpillars, insect pests were significantly influenced by crop growth stages, but only leafhoppers exhibited a significant negative relationship with grain yield. Furthermore, yield and yield components varied significantly between spray regimes, and there was a significant positive relationship between grain yield and yield components. Our study is important for informing growers on the stage of crop growth at which management tactics such as use of insecticides can be applied for different insect pests.

Development of multistage crop yield estimation model using machine learning and deep learning techniques.

In this research paper, machine learning techniques were applied to a multivariate meteorological time series data for estimating the wheat yield of five districts of Punjab. Wheat yield data and weather parameters over 34years were collected from the study area and the model was developed using stepwise multi-linear regression (SMLR), artificial neural network (ANN), support vector regression (SVR), random forest (RF) and deep neural network (DNN) techniques. Wheat yield estimation was done at the tillering, flowering, and grain-filling stage of the crop by considering weather variables from 46 to 4th, 46 to 8th, and 46 to 11th standard meteorological week. Weighted and unweighted Meteorological variables and yield data were used to train, test, and validate the models in R software. The evaluation results showed a consistent and promising performance of RF, SVR, and DNN models for all five districts with an overall MAPE and nRMSE value of less than 6% during validation at all three growth stages. These models exhibited outstanding performance during validation for the Faridkot, Ferozpur, and Gurdaspur districts. Based on accuracy parameters MAPE, RMSE, nRMSE, and percentage deviation, the RF model was found better followed by SVR and DNN models and, hence can be used for district-level wheat crop yield estimation at different crop growth stages.

Survey and Monitoring of Onion Thrips (Thrips tabaci) Lindeman

Thrips is a most important global pest of onion crop they are direct impact of onion yield and quality. The nymph and adult of thrips feed on leaves by piercing and rasping the leaf tissues and causes lengthwise, silvery stippling or blotching on the onion leaves, resulting in the loss of chlorophyll and reduced photosynthetic efficiency. Thrips attack onion at all the stages of crop growth stages but their count increases from bulb initiation and remains high up to bulb development and maturity. Onion thrips can also transmit several plant pathogens in onion crop that reduced onion bulb size and quality. The above surveys objective to evaluate the damage percentage of thrips in onion crops. Survey was conducted during Kharif 2022 and Rabi 2022-23. The maximum thrips damage rating scale of 3(45%) was recorded at 75 DAT in the Kharif season and Rabi season at 75 DAT thrips damage rating scale was recorded at 4 (65%).The survey result show that the thrips population and damage percentage increased due to increased temperature and relative humidity.

Effect of stage‐wise deficit irrigation on crop water productivity of different varieties of teff (Eragrostis tef)

AbstractThe increasing demand for water for various purposes is causing the availability of water for irrigation to decrease daily. Consequently, it is necessary to implement deficit irrigation. Research on the different teff crop varieties and the level of deficit irrigation with crop growth stages at semi‐arid agroecological zones remains inadequate. Therefore, the objective of this research was to evaluate the effect of stage‐wise deficit irrigation and crop variety on crop water productivity for the teff crop. Two varieties of teff and three irrigation levels were selected and applied for each growth stage. Crop water productivity was determined by using crop water consumed with their yield production and analyzed using Statistix 10.0 software. The results showed that crop varieties had a significant effect on crop water productivity. The maximum crop water use efficiency (0.92 kg m−3) and field water use efficiency (1.387 kg m−3) were obtained for a 30% deficit at the ripening growth stage for the Tseday teff variety. Contrarily, the minimum CWUE and FWUE of 0.617 kg m−3 and 0.959 kg m−3 were obtained to 30% deficit irrigation at the yield formation growth stage of the Quncho teff variety. Therefore, deficit irrigation during the yield formation growth stage was not recommended, but it was possible to apply deficit irrigation during the ripening growth stage to improve crop water productivity in areas of water scarcity.

Influence of tillage, residue and nitrogen placement on maize growth and yield under conservation agriculture

Conservation agriculture (CA) is changing the paradigm in production and productivity of maize-wheat system, but proper residue management and quantification, and application of nitrogen (N) are the main bottlenecks. In this context a field experiment was conducted in split plot design consisting of three crop establishment practices (CEPs) in main plots i.e., ZT with residue retention (ZT + R), zero tillage (ZT), conventional tillage (CT) and 4 N placement methods (NPMs) i.e., Control (only P and K applied), RDN: Recommended Dose of N (Band placement of 1/3rd N at sowing followed by surface placement of 1/3rd N during each at V6 (emerged six leaves with the complete collar visible) and tasselling stage, improved RDN (band placement of 1/3rd N as basal dose followed by 1/3rd nitrogen as subsurface placement at V6 stage followed by 1/3rd nitrogen as surface band placement at tasselling stage) and improved 80 % RDN (Band placement of 30 % N as basal followed by subsurface placement of 30 % N in maize (at V6 stage) and surface band placement of 20 % N in maize (at tasselling stage). The ZT + R treatment resulted in significantly higher plant height (6.61-7.02 %) and dry matter accumulation (DMA) (7.50-7.73 %) during different crop growth stages in maize compared to CT. The NPM involving subsurface placement of N at the V6 stage, that is, improved RDN, significantly increased plant height (2.37-2.73 %) and DMA (1.85-4.13 %) as compared to RDN during different crop growth stages. Significantly higher NDVI and lower CTD values were reported under ZT+R across crop growth stages over the years. ZT + R in combination with improved RDN resulted in significantly higher stover and biological yield by 10.40 and 10.10 %, respectively, as compared to CT with RDN. The improved 80 % RDN saved 20 % N to achieve the same level of productivity as the RDN, emphasising the role of the subsurface placement of nitrogen. Therefore, residue retention in ZT with improved RDN can enhance maize productivity in the Indo-Gangetic Plain and similar agro-ecologies. This research contributes to bridging the gap in nitrogen management under conservation agriculture.

Effect of Nitrogen Management and Silicon Fertilization on Crops

Nitrogen management has a substantial impact on crop growth stages and yields. Split nitrogen administration at specific growth stages improves nutrient utilization, reduce losses and increase yield. Several studies have shown that applying nitrogen in fractions throughout the life cycle improves nutrient uptake efficiency while minimizing environmental contamination. Proper judicious nitrogen application impacts soil properties by affecting nutrient availability. Nitrogen management based on the Leaf Color Chart (LCC) is successful in terms of optimizing nitrogen delivery and enhancing yield characteristics. Silicon fertilization has shown encouraging outcomes in terms of plant growth, yield, and stress tolerance across a variety of crops. Silicon also improves the soil features such as nutrient availability and pH regulation, resulting in increased plant nutrient uptake. The interaction effects of silicon with other nutrients show synergistic effects that improve nutrient absorption and yield. Silicon interacts with nitrogen, phosphorus, potassium, and other nutrients, which promotes crop performance and nutrient efficiency. Application of nitrogen and silicon management strategies can boost crop yield, nutrient efficiency, and sustainability in agricultural systems. However, additional research is needed to understand the intricate mechanisms behind the interactions of nitrogen, silicon, and other nutrients in crop-soil systems to develop more effective nutrient management strategies. This review deals with different aspects of nitrogen fertilization in crops and simultaneous silicon application and interaction among both. The aspects of growth parameters, yield attributes, yield, economics, soil characteristics and efficiency studies have been assessed.

A high-precision automatic diagnosis method of maize developmental stage based on ensemble deep learning with IoT devices

Accurately determining the stage of crop development holds significant importance for field crop management. With the advancement of smart agriculture, an increasing number of Internet of Things (IoT) devices are being integrated into agricultural production, enabling more efficient acquisition of high-precision crop images. Currently, research on detecting crop growth stages based on IoT device images remains relatively scarce. Most existing studies rely on a single network model for detection, often encountering issues such as low accuracy and overfitting. Therefore, in this study, we collected maize images using IoT devices and constructed an integrated deep learning model by utilizing four convolutional neural networks (CNNs) to detect the growth period of maize in real time. Additionally, we implemented several improvements on these four CNNs and subsequently tested the performance of the ensemble model on the maize dataset. Regarding the ensemble strategy for the ensemble model, we proposed a dynamic weighted voting method, building upon the original voting approach, which can mitigate model training fluctuations and expedite model convergence. Ultimately, we manually simulated various lighting conditions to assess their impact on the ensemble model. Experimental results demonstrate that the ensemble deep model proposed in this paper represents a robust method for detecting maize growth stages, achieving an accuracy rate of 0.976 on the maize dataset, effectively facilitating high-precision detection of maize growth stages in complex backgrounds.

Water is the Most Essential Component for the Agriculture and Family Environment

Abstract: Agriculture makes a pre-eminent contribution in the Indian economy. About seventy per cent of the rural families depend on it. According to the reports of Indian economic survey 2017-2018 more than 50 per cent of the work force in India is involved in agriculture and has a share of 17 to 18 per cent in the total GDP. (India economic survey 2018). Many factors influences on agricultural production like soil, climate, genetic diversity, abundance of micro-organisms, water etc, among which water play an important role because it is required from seed germination to other physiological stages of crop growth.

The dynamic effects of weather shocks on agricultural production

This paper proposes a new methodological approach using high-frequency data and local projections to assess the impact of weather on agricultural production. Local projections capture both immediate and delayed effects across crop types and growth stages, while providing early warnings for food shortages. Adverse weather shocks, such as excess heat or rain, consistently lead to delayed downturns in production, with heterogeneous effects across time, crops, and seasons. We build a new index of aggregate weather shocks that accounts for the typical delay between event occurrence and economic recognition, finding that these shocks are recessionary at the macroeconomic level, reducing inflation, production, exports and exchange rates.

Sensitivity and uncertainty analysis of a surface runoff model using ensemble of artificial rainfall experiments

Abstract Surface runoff models are essential for designing water and soil protection measures. However, they often exhibit uncertainty in both parameterization and results. Typically, uncertainty is evaluated by comparing model realizations with measured data. However, this approach is constrained by limited data availability, preventing comprehensive uncertainty assessment. To overcome this limitation, we employed the generalized likelihood uncertainty estimation (GLUE) methodology to conduct sensitivity and uncertainty analyses on a series of surface runoff models. These models were based on an ensemble of artificial rainfall experiments comprising 77 scenarios with similar settings. We utilized the rainfall-runoff-erosion model SMODERP2D to simulate the experiments and employed Differential Evolution, a heuristic optimization method, to generate sets of behavioural models for each experiment. Additionally, we evaluated the sensitivity and uncertainty with respect to two variables; water level and surface runoff. Our results indicate similar sensitivity of water level and surface runoff to most parameters, with a generally high equifinality. The ensemble of models revealed high uncertainty in bare soil models, especially under dry initial soil water conditions where the lag time for runoff onset was the largest (e.g. runoff coefficient ranged between 0–0.8). Conversely, models with wet initial soil water conditions exhibited lower uncertainty compared to those with dry initial soil water content (e.g. runoff coefficient ranged between 0.6 – 1). Models with crop cover showed a multimodal distribution in water flow and volume, possibly due to variations in crop type and growth stages. Therefore, distinguishing these crop properties could reduce uncertainty. Utilizing an ensemble of models for sensitivity and uncertainty analysis demonstrated its potential in identifying sources of uncertainty, thereby enhancing the robustness and generalizability of such analyses.

Large Scale Farm Scene Modeling from Remote Sensing Imagery

In this paper we propose a scalable framework for large-scale farm scene modeling that utilizes remote sensing data, specifically satellite images. Our approach begins by accurately extracting and categorizing the distributions of various scene elements from satellite images into four distinct layers: fields, trees, roads, and grasslands. For each layer, we introduce a set of controllable Parametric Layout Models (PLMs). These models are capable of learning layout parameters from satellite images, enabling them to generate complex, large-scale farm scenes that closely reproduce reality across multiple scales. Additionally, our framework provides intuitive control for users to adjust layout parameters to simulate different stages of crop growth and planting patterns. This adaptability makes our model an excellent tool for graphics and virtual reality applications. Experimental results demonstrate that our approach can rapidly generate a variety of realistic and highly detailed farm scenes with minimal inputs.

Canopy structure dynamics constraints and time sequence alignment for improving retrieval of rice leaf area index from multi-temporal Sentinel-1 imagery

Due to the limited availability of in-situ observation data, most existing leaf area index (LAI) inversion models do not fully leverage temporal information. Furthermore, the phenological evolution of crops can result in unstable and inaccurate retrieval outcomes. To address these challenges, this study proposes a novel framework for LAI inversion based on Sentinel-1. First, the constrained canopy structure dynamic hierarchical linear model (CSDHLM) is constructed, which integrates canopy dynamics information and temporal constraints. Second, the microwave scattering characteristics at various crop growth stages used to develop the phenological segment dynamic time warping (PSDTW). The PSDTW aims to address the challenges posed by inconsistent phenological dynamics across different plots. The quantitative evaluation results indicate that CSDHLM more accurately captures the temporal changes of LAI (R2 = 0.7688, RMSE = 0.8742) compared to hierarchical linear model (R2 = 0.7234, RMSE = 0.9561) and gaussian process regression (R2 = 0.7143, RMSE = 0.9717). Additionally, the LAI inversion results obtained by combining CSDHLM and PSDTW have greater robustness (R2 = 0.7332, RMSE = 1.4032) across diverse agricultural scenarios. This study emphasizes the importance of phenological information in estimating rice LAI, and the proposed framework is capable of generating long-term rice LAI maps with high resolution, demonstrating significant potential for agricultural applications at the regional scale.

Wheat (Triticum aestivum) response under soil moisture and crop water stress based irrigation scheduling at variable nitrogen regimes

The field experiment was conducted during winter (rabi) seasons of 2021–22 and 2022–23 at research farm of ICAR-Indian Agricultural Research Institute, New Delhi to examine the water productivity and crop response under soil moisture and crop water stress based irrigation scheduling at variable nitrogen regimes in wheat (Triticum aestivum L.). Experiment was conducted in a split-plot design (SPD) design comprised of 3 irrigation regimes in main plots and 5 graded nitrogen (N) levels in sub-plots, replicated thrice. Irrigation scheduling regimes included I1 (50% available soil moisture depletion-ASMD); I2 (CWSI i.e. crop water stress index based); and I3 (Conventional crop growth stage based). The 5 graded N levels included 0 (N0); 50 (N1); 100 (N2); 150 (N3); and 200 (N4) kg N/ha. Results showed that 50% DASM based irrigation significantly increased grain yield (11.28 and 6.30%), straw yield (5.33 and 5.70%), dry matter accumulation (5.65 and 5.44%), water productivity (11.37 and 6.19%), root length (15.89 and 44.48%), root weight (11.63 and 12.77%) and grain N uptake (20.88 and 14.52%) compared to conventional crop stage based irrigation during 2021–22 and 2022–23, respectively. Among the graded N application, maximum grain yield (4.78 and 4.82 t/ha) and crop water productivity (13.91 and 15.09 kg/ha-mm) were recorded with treatment N4 (200 kg N/ha), but remained statistically at par with N3 (150 kg N/ha) due to the marginal increment beyond 150 kg N/ha. Overall, soil moisture based irrigation at 50% MAD with 150 kg/ha N application proved to be the most effective and economical approach to enhance dry-matter accumulation, yield and water productivity with saving from harmful environmental effects ascending from excessive nitrogen use.

Impact of climate change on vegetation growth trends in a citrus farmland in the south-eastern Sicily

Abstract. The environmental repercussions of climate change triggered substantial alterations in existing ecosystem balances leading to a gradual reduction in biodiversity and the increasing degradation of soil and the built environment. This has a direct effect on agricultural production in particular, leading to increased crop water requirements, the spread of new pests and pathogens that are difficult to manage, and, thus, a general reduction in productivity. Through the analysis of multispectral data remotely sensed with latest generation of optical sensors (RGB, thermal, NIR and multi-spectral cameras), it is possible to recognize the health status and growth stages of crops. In fact, in relation to the amount of radiation reflected in the different bands it is possible to calculate spectral indices, or vegetation indices. The main vegetative indices are NDVI, SAVI, MSAVI2, GNDVI and NDRE, which are calculated by differentially combining the ultraviolet spectrum, the visible range, and the near and mid-infrared band.In this paper the outcomes of a multi-year monitoring of a citrus grove field near Rosolini (Syracuse), in south-eastern Sicily-Italy, are presented. This monitoring activity was conducted to highlight areas of productive greenery under stress, on which to act as a priority with specific agronomic interventions. In parallel with the direct monitoring phases of the citrus grove, the trend of average monthly and annual temperature and precipitation values over the past 24 years was also studied to more specifically contextualize the possible presence of significant variations in climatic trends and relate them to the results returned by the calculation of the indices.