Crop Phenological Stages Research Articles

Phenological stages of wheat modulate effects of phosphorus fertilization in plant-soil microbial interactions

Abstract Aims Future phosphorus (P) fertilizer availability faces challenges due to limited phosphate rock mines and strict quality regulations regarding Cd contents in phosphate rock. In this study, conventional fertilization was partially substituted with meat bone meal (MBM), sludge (S), and the organo-mineral combination of S plus MBM (SMBM), in a wheat agroecosystem. Methods We investigated the impact of fertilization treatments and crop phenological stages on P availability, crop yield, and soil microbial responses. Analysis included enzyme activities, microbial biomass, and the composition of bacterial and fungal communities using metabarcoding. Additionally, we estimated functional genes related to the P cycle through qPCR. Crop yield and nutrient content in plants and soil were also determined. Results Replacing traditional fertilization with MBM and SMBM maintained crop yield at levels equivalent to conventional fertilization. S and SMBM produced 70% and 40% (respectively) more bioavailable P compared to conventional treatment (Trad). Significant differences between treatments in soil microbial biomass were observed in the flag leaf stage. S increased in 20% total soil microbial biomass compared to Trad. Crop phenology had a stronger impact on bacterial and fungal communities than fertilization treatments. The use of S enhanced microbial biomass and activity. Yield in both MBM and SMBM plots exhibited no statistically significant differences compared to traditional fertilization. Conclusion Organo-mineral fertilization emerges as a sustainable strategy for maintaining crop production while improving soil functionality. Our findings emphasize the primary influence of crop phenology on shaping soil microbial communities and influencing microbial biomass and functionality.

Crop yield estimation at different growing stages using a synergy of SAR and optical remote sensing data

Crop yield forecasting is an essential component of crop production assessment, impacting people at the global scale down to the level of individual farms. Until now, yield forecasting has predominantly relied on optical data, particularly the maximum value of vegetation indexes. However, this approach only presents a short forecasting window, and it is essential to obtain yield estimates as early as possible in the growing season and then further improve forecasting even after the vegetation index has reached its peak. So far, optical satellite data at high-temporal resolution (1–3 days) has been actively used for real time crop yield monitoring, whereas fewer operational models make a use of synthetic aperture radar (SAR). In this study, we explore whether SAR data can capture distinct aspects of crop dynamics, providing new insights for yield estimation depending on the crop's phenological stage. We assess the efficiency of dual- (Sentinel-1) and quad-polarimetric (UAVSAR, RADARSAT-2) data to explain inter-field crop yield variability for corn, soybean, and rice over a test area in Arkansas, US (258 fields, 2019). We used optical imagery acquired by Planet/Dove-Classic, Sentinel-2, and Landsat 8, to establish a baseline performance of satellite-based indicators to explain yield variability and assess dual- and quad-polarimetric SAR data for crop yield assessment. In terms of polarimetric indexes, the results showed that in general the results for rice were mostly stable and better than the other crops (R2adj ∼ 0.4 on average). The best results were obtained for the Sentinel-1 VHasc with R2adj = 0.47 and RADARSAT-2 phase difference with R2adj = 0.45. The results for corn performed the least with an R2adj <0.35 for all the indexes. The results for soybeans were more variable and were highly correlated with certain indicators such as RADARSAT-2 HV, RADARSAT-2 Volume, and RADARSAT-2 Pauli HV with R2adj>0.4. We also investigated the day of year (DOY) with the maximum correlation between optical and SAR-derived features and the final yields for corn, soybean, and rice. The maximum correlation for optical features occurs over a short time between DOY 155 (June 4) and 185 (July 5) for corn and rice, and DOY 190 (July 9) and DOY 211 (July 30) for soybean, with these results being consistent across various optical-based sensors. On the contrary, the maximum correlation for SAR-derived features varied significantly and was between DOY 120 (April 30) to DOY 225 (August 13). A study of the time series parameters cross-correlation showed that the optical parameters were highly correlated, but the SAR parameters showed strong temporal decorrelation. We conducted a comparison between C-band and L-band to assess their sensitivity at each stage of growth. In this experiment, we determined that for low vegetation, the C band will be more useful at the beginning of the growth cycle, while the L band provides more information in later stages of growth. Using a random forest regression model combining SAR parameters with common difference vegetation index (DVI), we improved the error by 50% in comparison to the error using the (DVI) for corn, soybean, and rice.

A Method for Estimating Soybean Sowing, Beginning Seed, and Harvesting Dates in Brazil Using NDVI-MODIS Data

Brazil, as a global player in soybean production, contributes about 35% to the world’s supply and over half of its agricultural exports. Therefore, reliable information about its development becomes imperative to those who follow the market. Thus, this study estimates three phenological stages of soybean crops (sowing, beginning seed, and harvesting dates), identifying spatial–temporal patterns of soybean phenology using phenological metric extraction techniques from Normalized Difference Vegetation Index (NDVI) time-series data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. Focused on the state of Paraná, this study validates the methodology using reference data from the Department of Rural Economics (DERAL). Subsequently, the model was applied to the major Brazilian soybean area cultivation. The results demonstrate strong agreement between the phenological estimates and reference data, showcasing the reliability of phenological metrics in capturing the stages of the soybean cycle. This study represents the first attempt, to the best of our knowledge, to correlate the vegetative peak of soybeans with the beginning seed stage at a large scale within Brazilian territory. Amidst the urgent need for the accurate estimation of agricultural crop phenological stages, particularly considering extreme weather events threatening global food security, this research emphasizes the continual importance of advancing techniques for soybean monitoring.

Seasonal Dynamic of NO3− and K+ in a Citrus Crop Irrigated by Different Water Qualities

This study evaluated the effect of (i) irrigation water source: transfer (TW) and reclaimed water (RW), and (ii) crop phenological stage: winter rest (WR), flowering-sprouting (FS), and fruit growth (FG), on NO3− and K+ dynamics in soil and leaf of a citrus crop. The experiment was carried out during the 2018 and 2019 growing seasons on adult ‘Star Ruby’ grapefruit trees (Citrus paradisi Macf.). The concentration of both nutrients was periodically measured in soil and leaf samples and continuously monitored in the soil soluble fraction with nutrient sensors. Moreover, soil NO3− leaching was indirectly estimated by the periodic measurement of the leaf enrichment in 15N isotope (15Nleaf). The two water sources showed a different nutrient loading. Thus, NO3− and K+, were approximately 5 and 7 times higher, respectively, in the RW. Furthermore, the average contents of NO3− and K+ in the soil samples from the RW treatment were 10.1 and 19.7%, respectively, higher than in TW, with the highest soil NO3− leaching observed in RW treatment. In line with this, the mean contents of NO3− and K+ in the leaves from the RW treatment were 106.9 and 30.4% higher than the TW ones. As for the different phenological stages, in the FG stage, the lowest concentrations of NO3− in the soil samples and the highest in the leaf tissue were observed after a high soil leaching event. In this study the nutrient sensors measurements varied according to the dynamic of NO3− and K+ in the soil samples. The use of RW promoted an accumulation of NO3− and K+ in the soil and leaves of grapefruit trees, but also enhanced soil NO3− leaching, indicating that the proper management of this water source is necessary to avoid soil contamination. The mobilization of NO3− and K+ from soil to leaf was the highest in the FG stage, to ensure fruit development and vegetative growth.

New three red-edge vegetation index (VI3RE) for crop seasonal LAI prediction using Sentinel-2 data

Leaf area index (LAI) serves as a pivotal parameter in crop monitoring, significantly impacting agricultural applications. Empirical models are one of the commonly used methods for estimating LAI, they are often dependent on vegetation indices (VIs), predominantly derived from low-to-moderate spatial resolution satellite sensors. A critical limitation of these VIs is their tendency to saturate at elevated LAI values. Additionally, the interplay between chlorophyll content (Cab), LAI, as well as average leaf inclination angle (ALA), particularly in reflectance spectra from red to red-edge regions, has been underexplored in past research. Based on Sentinel-2 satellite, with three red-edge bands and enhanced spatio-temporal resolution, this study introduced a new three red-edge vegetation index (VI3RE), comprising NDVI3RE and CI3RE, which leverages the unique spectral characteristics of these bands and related differential response to LAI and Cab variations. We investigated VI3RE’s efficacy through a threefold approach: firstly, by conducting sensitivity analyses using noise equivalent (NE)ΔLAI ((NE) ΔLAI=1.37 and 1.59 for NDVI3RE and CI3RE; (NE)ΔLAI = 1.75 – 3.25 for other VIs) and extended Fourier amplitude sensitivity test (EFAST) (the contribution of LAI: FOI > 30 % and TOI > 40 % for VI3RE; FOI ∼ 17 % − 28 % and TOI ∼ 25 % − 34 % for other VIs, except for NDVI with TOI ∼ 50 %), we demonstrated VI3RE’s heightened sensitivity to LAI and its improved capability for seasonal LAI estimation compared to other VIs. Secondly, by comprehensively considering the simplicity, interpretability, AIC and R2 values of various models, the linear regression model was selected for analyzing the relationships between LAI and various VIs. We established that VI3RE, particularly NDVI3RE, exhibits a robust correlation with LAI, thereby enhancing LAI estimation accuracy (R2 = 0.72, RMSE = 1.39). Lastly, we applied the LAI-VI regression models to generate crop LAI imageries throughout the growing season, subsequently validating by field measured LAI data. The results affirmed VI3RE’s superior performance in seasonal LAI estimation of crops, notably during peak growth and sowing phases. We conclude that the newly formulated VI3RE offers a universal, highly precise, and reliable model for LAI prediction across various crop types and phenological stages. However, the broader application of VI3RE may be limited, due to the three red-edge bands are not commonly found in most satellite platforms. In addition, some other aspects are not considered in this study, such as phenological information, meteorological factors, sampling strategies, etc., which should be meticulously considered in the future studies.

Optimizing the Release Pattern of Telenomus podisi for Effective Biological Control of Euschistus heros in Soybean.

An augmentative biological control program using the egg parasitoid Telenomus podisi Ashmead (Hymenoptera: Platygastridae) is a promising tool for the management of the brown stink bug, Euschistus heros (Fabricius) (Hemiptera: Pentatomidae) in soybeans. The T. podisi are released as adults or pupae within lab-reared E. heros eggs. Because of the small size of the parasitoid and potentially limited dispersal ability, determining the optimal release pattern is critical for biological control of the target pest. This study used sentinel E. heros eggs to investigate T. podisi dispersal within soybean crops during two distinct phenological stages: the beginning of flowering (Vn-R1) and the grain-filling phase (R5-R6). Data were analyzed using semi-variograms and kriging maps. The results indicate significant differences in parasitism rates between the two plant growth stages and among different matrices. The maximum dispersal range for T. podisi was calculated at 39.0 m in the Vn-R1 stage with a maximum parasitism rate of 42%, while in the R5-R6 stage, the maximum dispersal range was calculated to be 30.9 m with a maximum parasitism of 73%. Therefore, it is recommended that release points for T. podisi be spaced no further than 30 m apart. These results provide valuable insights for future research and applications in biological control strategies, including adjustment of the logistics and release technique depending upon the crop phenological stage.

Crop phenology reshapes the food‐safety landscape for roe deer in an agroecosystem

Abstract Understanding the behavioural adjustments of wildlife in anthropized landscapes is key for promoting sustainable human–wildlife coexistence. Little is known, however, about how synanthropic species navigate spatio‐temporal variation in the availability of food and cover that are shaped by human practices such as agriculture. Animal habitat use is predominantly driven by spatial and temporal variations in food and cover, as individuals respond to fluctuations in the trade‐off between food acquisition and risk avoidance. In agroecosystems, the availability of high‐quality forage and cover is dependent on agricultural practices (e.g., harvesting) and crop phenology, providing an ideal opportunity to evaluate how wildlife adjust their behaviour in a heterogeneous human‐dominated landscape. We investigated the influence of crop phenology on the behaviour of European roe deer (Capreolus capreolus) to infer the functional roles of crop types in the food‐cover landscape. We analysed the habitat selection and activity patterns of 105 GPS‐collared adult female roe deer using a unique data set combining field‐specific land cover data, region‐specific estimates of crop phenology and weekly harvesting data for three common crops in a French agroecosystem. We found very distinct habitat selection and activity patterns according to crop type, phenological stage and time of day. Wheat and artificial meadows were strongly selected at night‐time during the early and post‐harvest stages only, when roe deer were highly active, suggestive of feeding activity. On the contrary, roe deer strongly selected maize during the day when it was high enough to provide cover, when they were less active, indicating that it was primarily used for refuge. These patterns depended on the availability of more ‘natural’ cover, suggesting that mature maize may substitute for ‘natural’ cover when the latter is scarce. Synthesis and application: Our work highlights the importance of behavioural plasticity and habitat complementation in the persistence of this synanthropic species in agroecosystems. This behavioural adjustment may buffer the consequences of the reduction in natural habitats that accompanies intensification of agricultural production and has implications for understanding how agricultural practices shape the food‐safety trade‐off of wildlife living in these highly modified landscapes.

Onfield estimation of quality parameters in alfalfa through hyperspectral spectrometer data

Alfalfa is a forage of vast importance around the world. In the past, near-infrared spectroscopy (NIRS) technique have been explored in the lab to determine quality traits such as fibre content in dried and ground material. During the last decade, portable hyperspectral devices have emerged as a tools for in-field prediction, of not only crop yield but also a large range of quality and physiological markers. The objective of this study was to estimate quality parameters in an alfalfa crop using hyperspectral data acquired from a full-range (350–2500 nm) spectrometer under field conditions. Reflected spectra were measured in single leaves as well as at the canopy level, then reflectance was related to target parameters such as biomass, leaf pigments, sugars, protein, and mineral contents. Due to their large effect on crop quality parameters, meteorological conditions and phenological stages were included as predictors in the models. We found that meteorological and phenological variables improved the accuracies and percentage of variance explained (R2) for most of the parameters evaluated. Based on R2 values, the best prediction models were obtained for biomass (0.71), sucrose (0.65), flavonoids (Flav) (0.56) and nitrogen (0.70) with normalized root mean squared errors of 0.196, 0.32, 0.087 and 0.08, respectively. These parameters were associated mainly with visible (VIS) (approx. 350–700 nm) and near infrared (NIR) (700–1250 nm) regions of the spectrum. Regarding mineral composition, the best prediction models were developed for P (0.51), B (0.50) and Zn (0.44), associated with the short-wave infra-red (SWIR) region (1250–2500 nm). The results of this study demonstrated the potential of hyperspectral techniques to be used as a base for performing initial evaluations in the field of quality traits in alfalfa crops.

Within-Season Crop Identification by the Fusion of Spectral Time-Series Data and Historical Crop Planting Data

Crop mapping at an earlier time within the growing season benefits agricultural management. However, crop spectral information is very limited at the early crop phenological stages, leading to difficulties for within-season crop identification. In this study, we proposed a deep learning-based fusion method for crop mapping within the growing season, which first learned a priori information (i.e., pre-season crop types) from historical crop planting data and then integrated the a priori information with the satellite-derived crop types estimated from spectral times-series data. We expect that preseason crop types provided by crop rotation patterns is an effective supplement to spectral information to generate reliable crop maps in the early growing season. We tested the proposed fusion method at three representative sites in the U.S. with different crop rotation intensities and one site with cloudy weather conditions in the Sichuan Province of China. The experimental results showed that the fusion method incorporated the strengths of pre-season crop type estimates and the spectral-based crop type estimates and thus achieved higher crop classification accuracy than the two estimates throughout the growing season. We found that pre-season crop estimates had a higher accuracy in the scenarios with either nearly continuous planting or half-time planting of the same crop. In addition, the historical crop type data strongly affected the performance of pre-season crop estimates, suggesting that high-quality historical crop planting data are particularly important for crop identification at earlier times in the growing season. Our study highlighted the great potential for near real-time crop mapping through the fusion of spectral information and crop rotation patterns.

A Phenology-guided Bayesian-CNN (PB-CNN) framework for soybean yield estimation and uncertainty analysis

Large-scale crop yield estimation is important for understanding the response of agriculture production to environmental forces and management practices, and plays a critical role in insurance designing, trade decision making, and economic planning. The empirical models (e.g., deep learning models) have been increasingly utilized for estimating crop yields with the ability to take into account a range of yield predictors and complex modeling relationships. Yet empirical estimation of crop yields still faces important challenges, particularly in accommodating spatio-temporal crop phenological development patterns as well as tackling the heterogeneity of a diversity of yield predictors. The different types of uncertainties associated with empirical yield estimations have seldom been explored. The objective of this study is to develop a Phenology-guided Bayesian-Convolutional Neural Network (PB-CNN) framework for county-level crop yield estimation and uncertainty quantification, with soybean in the US Corn Belt as a case study. The PB-CNN framework comprises three key components: Phenology Imagery construction, multi-stream Bayesian-CNN modeling, as well as feature importance (i.e., yield predictor and phenological stage) and predictive uncertainty analysis (i.e., aleatoric and epistemic uncertainty). With the innovative integration of critical crop phenological stages in modeling the crop yield response to a heterogeneous set of yield predictors (i.e., satellite-based, heat-related, water-related, and soil predictors) as well as the associated uncertainties, the developed PB-CNN framework outperforms three advanced benchmark models, achieving an average RMSE of 4.622 bu/ac, an average R2 of 0.709, and an average bias of −2.057 bu/ac in estimating the county-level soybean yield of the US Corn Belt in testing years 2014–2018. Among the yield predictor groups, the satellite-based predictor group is the most critical in soybean yield estimation, followed by the water- and heat-related predictor groups. Throughout the growing season, the soybean blooming to dropping leaves phenological stages play a more crucial role in modeling the soybean yield. The soil predictor group as well as the early growing stages can improve the model estimation accuracy yet potentially brings more uncertainties into the yield estimation. The further uncertainty disentanglement indicates that the dominant uncertainty in yield estimation is the aleatoric uncertainty, mainly stemming from the fluctuations and variations inherent in the modeling input observations. The PB-CNN framework largely enhances our understanding of the complex soybean yield response to varying environmental conditions across crop phenological stages as well as associated uncertainties for more sustainable agricultural development.

Deep reinforcement learning for irrigation scheduling using high-dimensional sensor feedback

Deep reinforcement learning has considerable potential to improve irrigation scheduling in many cropping systems by applying adaptive amounts of water based on various measurements over time. The goal is to discover an intelligent decision rule that processes information available to growers and prescribes sensible irrigation amounts for the time steps considered. Due to the technical novelty, however, the research on the technique remains sparse and impractical. To accelerate the progress, the paper proposes a principled framework and actionable procedure that allow researchers to formulate their own optimisation problems and implement solution algorithms based on deep reinforcement learning. The effectiveness of the framework was demonstrated using a case study of irrigated wheat grown in a productive region of Australia where profits were maximised. Specifically, the decision rule takes nine state variable inputs: crop phenological stage, leaf area index, extractable soil water for each of the five top layers, cumulative rainfall and cumulative irrigation. It returns a probabilistic prescription over five candidate irrigation amounts (0, 10, 20, 30 and 40 mm) every day. The production system was simulated at Goondiwindi using the APSIM-Wheat crop model. After training in the learning environment using 1981–2010 weather data, the learned decision rule was tested individually for each year of 2011–2020. The results were compared against the benchmark profits obtained by a conventional rule common in the region. The discovered decision rule prescribed daily irrigation amounts that uniformly improved on the conventional rule for all the testing years, and the largest improvement reached 17% in 2018. The framework is general and applicable to a wide range of cropping systems with realistic optimisation problems.

Deep-Learning-Based Rice Phenological Stage Recognition

Crop phenology is an important attribute of crops, not only reflecting the growth and development of crops, but also affecting crop yield. By observing the phenological stages, agricultural production losses can be reduced and corresponding systems and plans can be formulated according to their changes, having guiding significance for agricultural production activities. Traditionally, crop phenological stages are determined mainly by manual analysis of remote sensing data collected by UAVs, which is time-consuming, labor-intensive, and may lead to data loss. To cope with this problem, this paper proposes a deep-learning-based method for rice phenological stage recognition. Firstly, we use a weather station equipped with RGB cameras to collect image data of the whole life cycle of rice and build a dataset. Secondly, we use object detection technology to clean the dataset and divide it into six subsets. Finally, we use ResNet-50 as the backbone network to extract spatial feature information from image data and achieve accurate recognition of six rice phenological stages, including seedling, tillering, booting jointing, heading flowering, grain filling, and maturity. Compared with the existing solutions, our method guarantees long-term, continuous, and accurate phenology monitoring. The experimental results show that our method can achieve an accuracy of around 87.33%, providing a new research direction for crop phenological stage recognition.

Crop Phenology Modelling Using Proximal and Satellite Sensor Data

Understanding crop phenology is crucial for predicting crop yields and identifying potential risks to food security. The objective was to investigate the effectiveness of satellite sensor data, compared to field observations and proximal sensing, in detecting crop phenological stages. Time series data from 122 winter wheat, 99 silage maize, and 77 late potato fields were analyzed during 2015–2017. The spectral signals derived from Digital Hemispherical Photographs (DHP), Disaster Monitoring Constellation (DMC), and Sentinel-2 (S2) were crop-specific and sensor-independent. Models fitted to sensor-derived fAPAR (fraction of absorbed photosynthetically active radiation) demonstrated a higher goodness of fit as compared to fCover (fraction of vegetation cover), with the best model fits obtained for maize, followed by wheat and potato. S2-derived fAPAR showed decreasing variability as the growing season progressed. The use of a double sigmoid model fit allowed defining inflection points corresponding to stem elongation (upward sigmoid) and senescence (downward sigmoid), while the upward endpoint corresponded to canopy closure and the maximum values to flowering and fruit development. Furthermore, increasing the frequency of sensor revisits is beneficial for detecting short-duration crop phenological stages. The results have implications for data assimilation to improve crop yield forecasting and agri-environmental modeling.

A real-time decision-making tool based on dynamic thresholds for Phthorimaea absoluta management in greenhouse tomato

The onset of digital information and computing power brings the opportunity to optimize the use of agricultural inputs and favors the transit to an information-based agriculture. The tomato leafminer (TLM), Phthorimaea absoluta, is a major pest of greenhouse tomatoes, and its control relies heavily on chemical pesticides. This study presents a decision-making system for TLM that dynamically adjusts decision thresholds throughout the crop cycle. Through a controlled experiment, we obtained pest damage curves for three crop phenological stages, which were used to derive a set of non-linear functions that estimates a decision threshold expressed as the maximum tolerable number of larvae/plant for any given crop age. We show that produce losses due to TLM are far from constant across the crop cycle, and that such variation can be modeled as a function of the effect of TLM on yield, cumulative control costs, potential yield, and produce prices. The decision threshold function was linked to a previously validated sampling plan, which was adjusted to work in serial bouts in frequencies that vary according to sampling results and pest growth rates. The decision-making system is presented as a web-based app that delivers a management recommendation, the timing for the next sampling bout, and an indication of how close the population is from the threshold. This study presents a step forward towards a modern application of the economic injury level concept by adapting pest management decision criteria to the changing ecological and economic context of each production system.

Soil Moisture Retrieval During Crop Growth Cycle Using Satellite SAR Time Series

Satellite SAR-based soil moisture retrieval over agricultural fields, under crop overlain conditions, is a challenging exercise. This is so since the overlying crop volume interacts with both the incoming and the backscattered radar signal. Therefore, the soil moisture linked solely to the top layer ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$0-5$</tex-math></inline-formula> <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$cm$</tex-math></inline-formula> ) of the soil cannot be reliably retrieved under such conditions without avoiding the obscuring effect of growing crop volume. In this investigation, we demonstrated a proof of concept for a time-series approach to retrieve soil moisture during crop growth cycle. Contrary to the use of the single-scene approach, the novelty of the proposed approach lies in exploiting the satellite SAR time-series acquired during a cropping cycle. The proposed time-series approach is effective for capturing the nuances in the crop phenological stages while calibrating the Dubois-WCM soil moisture retrieval model. By employing this approach, we achieved the 0.04 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$m^{3} m^{-3}$</tex-math></inline-formula> soil moisture retrieval RMSE benchmark at a high spatial resolution and addressed the issue of solving for the Dubois-WCM model constants under data constrained conditions. Furthermore, we observed that combination of temporally non-overlapping vegetation descriptors (optical and SAR) resulted in degradation in the performance of the retrievals and under such circumstances single polarimetric descriptors performed better.

Effect of weather variabilities on dispersion pattern of Nilaparvata lugens (Stål) (Homoptera: Delphacidae) in paddy field

Field experiments were conducted to find out the impact of weather factors and crop phenological stages on the dispersion pattern of brown planthopper (BPH), Nilaparvata lugens (Stål) in paddy during kharif 2019 and 2020 at three paddy crop phenological stages like the active tillering stage (IV week of July to III week of August), panicle bearing and grain filling stage (IV week of August to III week of October) and grain maturity stage (IV week of October to IV week of November). Weather factors in relation with mean BPH population were subjected to multiple regression and correlation analysis. The dispersion patterns were studied by regression models using Index of dispersion, Elliott’s law, Taylor’s Power Law and Iwao’s patchiness regression model. Maximum population of BPH was observed during panicle bearing and grain filling stage at 108 days after transplanting. The BPH counts were fitted to negative binomial which indicated aggregated distribution in the field and most of the weather parameters like temperature, evening relative humidity and wind speed is negatively correlated during this stage. During active tillering and grain maturity stage, Poisson distribution pattern of BPH population in the field was observed which showed random distribution.

Potential of Time-Series Sentinel 2 Data for Monitoring Avocado Crop Phenology

The ability to accurately and systematically monitor avocado crop phenology offers significant benefits for the optimization of farm management activities, improvement of crop productivity, yield estimation, and evaluation crops’ resilience to extreme weather conditions and future climate change. In this study, Sentinel-2-derived enhanced vegetation indices (EVIs) from 2017 to 2021 were used to retrieve canopy reflectance information that coincided with crop phenological stages, such as flowering (F), vegetative growth (V), fruit maturity (M), and harvest (H), in commercial avocado orchards in Bundaberg, Queensland and Renmark, South Australia. Tukey’s honestly significant difference (Tukey-HSD) test after one-way analysis of variance (ANOVA) with EVI metrics (EVImean and EVIslope) showed statistically significant differences between the four phenological stages. From a Pearson correlation analysis, a distinctive seasonal trend of EVIs was observed (R = 0.68 to 0.95 for Bundaberg and R = 0.8 to 0.96 for Renmark) in all 5 years, with the peak EVIs being observed at the M stage and the trough being observed at the F stage. However, a Tukey-HSD test showed significant variability in mean EVI values between seasons for both the Bundaberg and Renmark farms. The variability of the mean EVIs between the two farms was also evident with a p-value &lt; 0.001. This novel study highlights the applicability of remote sensing for the monitoring of avocado phenological stages retrospectively and near-real time. This information not only supports the ‘benchmarking’ of seasonal orchard performance to identify potential impacts of seasonal weather variation and pest and disease incursions, but when seasonal growth profiles are aligned with the corresponding annual production, it can also be used to develop phenology-based yield prediction models.

Remote Sensing Monitoring of Durum Wheat under No Tillage Practices by Means of Spectral Indices Interpretation: A Preliminary Study

Due to its advantages, remote sensing monitoring has been used in various applications and made noteworthy contributions to understanding soil and plant processes, as well as in the agriculture sector. The aim of the work is to compare the return of durum wheat crops in conservative agricultural practices in Mediterranean climate conditions by analysing the data from the Sentinel2 satellite through three spectral indices. The analysed spectral indices have different interpretations and therefore have been studied in different periods: (i) NDVI (normalized difference vegetation index) for the evaluation of the vegetative vigour from January to June; (ii) NDWI (normalized difference water index) for the moisture of covered soil from January to June and of bare soil after harvesting from June to August; and (iii) NMDI (normalized multi-band drought index) for the variability of bare soil moisture from June to August. With reference to the machines used in cultivation practices, a further purpose of the study is to investigate the effects of automatic guidance versus manual guidance on production yields and on the spectral indices considered. The first results show that the NDVI follows crop phenological stages by reaching the maximum values in correspondence with the stem elongation and booting stages. Additionally, the NDWI showed the same trend as the NDVI during the current crop. After harvesting, the NDWI showed higher values in the plots cultivated under conservation tillage practices. In the same period, the NMDI showed the same results as the NDWI and a positive correlation, confirming that tillage practices could imply a lower ability to retain water in drought time.

Soil moisture retrieval over crop fields based on two-component polarimetric decomposition: a comparison of generalized volume scattering models

Model-based polarimetric decomposition can separate to some extent the backscattered radar signals from the vegetation canopy and the underlying ground, hence enabling a strategy for soil moisture retrieval in vegetated agricultural fields. However, the volume scattering models used in previous studies are only applicable to specific cases, making it difficult to completely remove the volume component induced by the vegetation layer. In this paper, three generalized volume scattering models (i.e., generalized volume scattering model (GVSM), simplified adaptive volume scattering model (SAVSM), and simplified Neumann volume scattering model (SNVSM)) are incorporated in the decomposition and evaluated for soil moisture retrieval. Considering the complexity and descriptive ability of the available physical models, a modified two-component model-based decomposition is proposed as the basic decomposition framework. This decomposition is also based on the physical constraint of the dielectric constants included in the model. The employed models combine an X-Bragg surface scattering model with three continuous generalized volume scattering models. The analytic solution of the parameters is obtained, and the minimum residual power criterion is used to determine the optimal solution to fit the model. By using the proposed model-based decomposition framework, the performance of the three models to simulate the canopy scattering and, as a result, to later estimate soil moisture under agricultural vegetation is evaluated. Fully polarimetric RADARSAT-2 C-band images acquired on eight dates in 2013 and 2015 over fields covered by two crops (wheat and soybean) were employed for validation. Results show that the proposed decomposition method, using any of the three volume scattering models, can provide promising inversion results of soil moisture, with RMSEs ranging from 2.89 to 7.43 [vol.%]. Compared with the other two models, the SNVSM simulates the vegetation contribution more accurately in this framework, and it provides a stable soil moisture inversion performance at different crop phenological stages, with an optimal overall accuracy of RMSE = 4.99 [vol.%] and a correlation coefficient of R = 0.78.

Selectivity of 2,4-D salts to barley plants applied at distinct phenological crop stages

The herbicide 2,4-D is available in distinct formulations in terms of selectivity to non-target species, and in terms of drift and volatilization potentials. Thus, the objective of this work was to evaluate the selectivity and effect of 2,4-D formulations (amine salt and choline) applied at different developmental stages of barley. The experiment was installed in the field in randomized blocks, in a 2 × 4 + 1 factorial scheme, with four replications. Factor A comprised the formulations of 2,4-D (amine salt and choline), and factor B the crop phenological stage (before tillering start, at full tillering, first node and booting), plus a weeded control treatment. The 2,4-D amine showed higher phytotoxicity when applied before tillering and at the first node. On the other hand, 2,4-D choline, when used prior to tillering start, showed the highest phytotoxicities. The yield components showed positive results when using the amine salt before and at tillering, while in the first node stage, and in booting, the choline salt stood out. The use of 2,4-D amine in tillering and choline in the first node provided the highest grain yield of barley.