Early Stages Of Crop Growth Research Articles

Proline Priming Enhances Seed Vigour and Biochemical Attributes of Rice (Oryza sativa L.) during Germination.

Seed vigour is a desirable trait especially for direct seeded rice (DSR) cultivation. Seeds with high vigour could improve seed germination, support seedlings in competing with weeds for water and nutrients, and improving seedling establishment throughout the early stages of crop growth. The success of DSR system which account for more 25% of world cultivation areas is highly dependent on the seed vigour and seedling establishment. Seed priming is a promising technique to improve seed vigour. Proline is an amino acid that has been well studied for its roles in plants under different environmental stress conditions. Nevertheless, the effect of proline as a seed priming agent in improving seed vigour in rice remain elusive. In this research, the effect of 24 h of proline priming at various concentrations (0 mM, 1 mM, 2 mM, 10 mM and 20 mM) on rice seed vigour, amylase activity, and total soluble sugar (TSS) content of a Malaysia indica rice variety, MR269 was investigated. Results showed that seeds primed with lower concentration of proline (0 mM, 1 mM and 2 mM) had better germination responses while priming at high concentrations (10 mM and 20 mM) reduced seed germination. Among the concentration tested, priming with 1 mM proline enhanced seed vigour with significantly higher germination percentage (GP), germination rate index (GRI) and seedling vigour index (SVI). In addition, proline primed seeds also exhibited increased amylase activity and TSS content as compared to unprimed seeds. However, priming seed with 20 mM proline was detrimental to the seed vigour and seedling growth whereby lower GP, GRI and SVI and higher mean germination time (MGT) were observed. In short, this study shows that proline could be a potential seed priming agent to improve seed vigour in rice.

Ensembles of multiple models for soil moisture retrieval from remote sensing data over agricultural areas: A deep learning-based framework

In agricultural areas, most surface soil moisture (SM) retrieval models are unstable in terms of their accuracy and performance during crop growth. As a result, there is no consensus on which model performs optimally during the agricultural season. This is because of the uncertainties associated with model physics, initial conditions, model inversion processes, input data, vegetation attenuation and soil characteristics. To better deal with these practical concerns, we propose a simple, but robust SM retrieval method of using combination of multiple models based on deep learning and multi-model ensemble approach, called the DL-MME method, which makes use of the ‘collective intelligence’ and ‘wisdom of crowds’ concepts. The advantages of this method are: (1) robustness to model selection, and (2) robustness to model calibration during the growing season. In addition, this method is less dependent on one type of data across various agricultural areas compared to the single model approach. Firstly, the coupled water cloud model (WCM) and soil backscattering models (Oh model or advanced integral equation model (AIEM)) with different vegetation descriptors were calibrated and validated during the growing season in sugarcane and winter wheat fields for Sentinel-1 backscattering coefficients (VV and VH). SM was also retrieved by employing the trapezoid model (OPTRAM) with different parameters from Sentinel-2 images. To optimize SM retrieval computations, we used the outputs from optical and SAR models, auxiliary features, and reliable in situ SM measurements as inputs to a deep learning convolutional neural network (DL-CNN). For sugarcane and wheat fields in the early stages of crop growth, WCM models retrieved more accurate time-series SM than optical models. OPTRAM soil moisture retrievals showed greater accuracy in the late crop growing season. Time-series SM retrieval accuracy using DL-MME was higher than for the optical and semi-empirical SAR models. According to the results of the in situ validation for wheat (sugarcane) fields, the minimum MAE by an optimal combination of models was around 0.01 (0.02) m3m−3 (RMSE = 0.036 (0.074) m3m−3; R = 0.87 (0.71)). The findings demonstrate that our method is reliable and feasible for SM retrieval. Additionally, our method provides a way to select an optimal model for retrieving time-series SM during the crop growing season.

Energy Compensation for Crop Growth under Plastic Mulching: Theories, Models, and Limitations

Plastic film mulching (PM) is a useful agronomic means to adapt to the environmental conditions of dryland agriculture and improve crop production. To improve the theoretical framework of PM technology, this study focuses on the interaction between the soil temperature change caused by PM and crop growth. The definition, action mechanism, and simulation of the compensatory effect of PM on growing degree days are introduced to reveal the effect of soil temperature under PM on crop development and growth. Our summary shows that the strength of the warming effect changes with the growth and development of crops, strengthening during the early stage of crop growth and gradually weakening as a crop canopy develops. Generally, the warming effect has a good promotion effect on crop growth, but the crop growth is hampered even with a yield reduction when the increased soil temperature caused by PM exceeds the tolerant temperature for plant growth. Moreover, the compensatory effect of PM could be used to quantify the growth and development of crops under PM and has been widely applied to cotton, corn, winter wheat, and rice. The compensation coefficient is larger in the early stage of crop growth than in the later stage. The compensation coefficient has certain differences for the same crop because of the influence of climate factors, soil moisture content, and soil microtopography. In future research, the theoretical integration of the safety period of PM and the time threshold of the compensatory effect could be theoretically interpreted, and the construction of the compensatory effect module in the crop models will also be an important issue.

Optimizing Weed Control: A Review of Integrated Weed Management in Mustard (Brassica juncea L.)

Mustard (Brassica juncea L.), a species of the cruciferous family, is the world's third most significant oilseed crop, following soybean and palm. Mustard seeds range in oil content from 37 to 49%. India is the fourth-largest producer of mustard and rapeseed in the world. The most significant issue causing mustard's low yield is weed control. Crop yields are significantly reduced by weeds; in the case of mustard, this can mean a complete failure rate of 15–30%. Crop plants compete with weeds for nutrients, sunlight, space, and water. In addition to lowering crop quality, weeds pose a number of nutritional and environmental hazards. In mustard crops, weed competition is particularly important in the early stages since the crop grows slowly in the first 4–8 weeks after sowing. On the other hand, 15–40 days are the critical window for crop–weed competition. Weed control must be carried out appropriately and on time in order to maximize mustard growth and production. During the early stages of crop growth, 25–30 days after sowing, the traditional practice of hand weeding once is insufficient because weeds always reappear after manual weeding, irrigation, or winter rainfall. More importantly, though, is that these weeds drain a large amount of the soil's moisture and nutrients. Though hand weeding mustard is simple, the expense of doing so is significant due to the scarcity of labor at the appropriate time and the uncontrolled growth of various intra row weeds. Weeds may become tolerant of herbicides despite their regular and high dosage application. Therefore, combined approaches could be the best option to control the complex weed flora in mustard.

Modeling hydrological consequences of 21st-Century climate and land use/land cover changes in a mid-high latitude watershed

The Naoli River Basin (NRB), a pivotal agricultural production area in China, is poised to undergo substantial impacts on water resources due to projected climate and land use/cover (LULC) changes. Despite its significance in the context of China's expanding farmland construction in the NRB, there exists limited research on the potential repercussions of future shifts in runoff, soil water content (SWC), and evapotranspiration (ET) on crop productivity and water availability (both in terms of quantity and timing). This study employs future LULC maps and an ensemble of ten CMIP6 Global Climate Models (GCMs) across three scenarios to drive the well-calibrated distributed hydrological model, ESSI-3. The objective of present study is aimed on projecting hydrological consequences under climate and land use/land cover changes in near-term (2026–2050), middle-term (2051–2075), and far-term (2076–2100) future in comparison to the baseline period of 1990–2014. Results consistently indicate an increase trend in annual average ET, runoff, and SWC in the NRB across all three future periods under the three SSP scenarios. LULC changes emerge as the primary driver influencing regional hydrological processes in the near future. Notably, under high-emission scenarios, monthly runoff and SWC are projected to significantly increase in March but decrease in April during the middle and far future periods compared to the baseline. This shift is attributed to the anticipated warming of winter and spring, leading to a transition in peak snowmelt from April to March. Concurrently, the expansion of cropland intensifies crop evapotranspiration demand, potentially exacerbating water stress during the early stages of crop growth in April. The findings underscore the importance of addressing the substantial impacts of climate change and land use planning on regional water cycling processes. Early planning to mitigate water shortages during the initial stage of future crop growth is crucial for ensuring food security and managing water-related challenges in the NRB and neighboring mid-high latitude regions.

System of Wheat Intensification: An Innovative and Futuristic Approach to Augment Yield of Wheat Crop

There is a new method of wheat production called the System of Wheat Intensification (SWI) that manages seed treatment, seed rate, spacing, weeding, and watering. The SWI and traditional methods of wheat sowing differ from each other in terms of potential yield. In comparison to the traditional method of wheat sowing, SWI allows seed treatment, which increases the number of tillers, the number of grains in spike, and the weight of the grain. Wheat seeding in the traditional approach is done at a much closer distance than in the SWI method, which results in faulty germination as a result of increased competition between the plants. In SWI, proper root formation in the early stages of crop growth can be encouraged by increasing the space between plants and rows, as well as increasing the density of plants. For small and medium-sized farmers, it is a great way to boost productivity and income while reducing food poverty at the same time. Using organic manure instead of chemical fertilizer is a new strategy that helps support sustainable agriculture. To help the poorest farmers and enhance their productivity and profit, SWI should be recommended.

Effect of Municipal Solid Wastes On Grain Yield of Three Maize Cultivars

The growth and yield of three maize cultivars were examined in relation to the impact of Municipal Solid Wastes (MSW) enriched soil. According to the results, plants grown on soil treated with MSW and inorganic fertilizer (NPK) produced significantly (P≤0.05) taller plants, larger leaves and higher number of leaves than plants grown on the control soil. On the other hand, plants grown on soils fertilized with MSW 1 showed significantly (P≤0.05) greater values of these indicators. The study also demonstrated that maize cultivar TZE yellow, EVDT 99 and Comp-1-synthetic showed no significant variation at the later growth stage but TZE yellow significantly (P≤0.05) had taller plants, larger leaves, and higher number of leaves at the early stage of crop growth. Comp-1-synthetic produced more grain and more grains per cob which was statistically at par with TZE yellow but differ significantly (P≤0.05) from EVDT 99. Crops grown on MSW and NPK treated soils had significantly (P≤0.05) higher grain yields and more grains per cob than those grown on the control soil. However, compared to NPK treated soils, MSW treated soils significantly (P≤0.05) produced better grain yield and number of grains per cob. The maize cultivars' differences in crude protein content were significant at P≤0.05. The crude protein content of TZE yellow was significantly (P≤0.05) higher than that of EVDT 99 and Comp-1-synthetic. Additionally, plants cultivated on MSW 1 treated soil had considerably higher crude protein content than plants grown on other soil types. Therefore, if municipal solid wastes are sorted and handled, MSW enriched soils could be employed to boost maize production.

Quantifying consistency of crop establishment using a lightweight U-Net deep learning architecture and image processing techniques

Consistency of crop establishment is a measure of uniformity of crop attributes, such as plant stand count, crop emergence rate, and plant spacing across the field. Quantifying consistency during the early crop growth stage is important for establishment decisions to use targeted nutrients and to facilitate timely replanting in inconsistent crop regions. Crop consistency can be analysed using two key parameters: plant stand count and spacing statistics since they provide insight into plant density and its emergence percentage. However, manual assessment of them is time-consuming, prone to errors, and labour-intensive in large fields. An alternative method is proposed to automate estimating these parameters using field imagery under uncontrolled settings. We use the YOLOv5-based object detection model for plant counting, which attains a mean average precision of 0.956 to detect Canola plants. A Lightweight U-Net model is proposed to segment rows, followed by Guo–Hall thinning and Probabilistic Hough Transform to determine inter-row and inter-plant spacing. Our proposed row segmentation model achieves a mean Intersection over Union (mIoU) of 0.8444 with class-wise IoU of 0.9925 and 0.6963 for background and crop using fewer parameters. The new architecture uses only 14M parameters and achieves performance comparable to the state-of-the-art U-Net (32.5M) and SegNet (29M).

Aerial imagery-based tobacco plant counting framework for efficient crop emergence estimation

Crop emergence estimation at early crop growth stages is becoming increasingly important for the long-term sustainability of natural resources. It helps farmers and agricultural stakeholders in the efficient allocation of resources like water, pesticides, and fertilizers. It can be used to estimate the yield and seed quality, identify the region of potential yield losses, and make future agriculture plans. These future agriculture plans can play a crucial role in ensuring maximum crop population and yield while utilizing the same limited land and natural resources. Most of the existing plant counting frameworks require offline processing of images with computationally expensive algorithms including the structure for motion and multiview stereo to develop an orthomosaic. This study proposed a tobacco plant counting framework that directly estimates counts from aerial images and has the potential for real-time applicability. It consists of three core modules: overlap detection, plant detection, and plant counting. The overlap detection module replaces the need for computationally expensive orthomosaic formation to avoid counting repetition by overlap masking based on only visual cues. Three different methods are evaluated as core modules for finding an optimal solution for plant counting based on time complexity and accuracy. In the first method after overlap detection, semantic segmentation with U-NET is employed as a plant detection module. For plant counting, we count the connected pixels classified as plants to estimate the crop count. In the second method after overlap detection, object detection using YOLOv7 is utilized as a plant detection module followed by simply counting each detected plant. In the third method, we utilize YOLOv7 for object detection, similar to the second method. However, we introduce the SORT (Simple Online and Realtime Tracking) algorithm for object tracking. This object tracking replaces the overlap detection module making it a real-time applicable method. For plant counting, we assess the number of tracked plants. The proposed algorithm is evaluated on two distinct tobacco fields. The high-resolution aerial data is collected from tobacco fields near Peshawar, Pakistan, and is human-labelled. The first and second methods show average F1 scores of 0.947 and 0.9667, respectively, whereas the third method has the potential for real-time applicability with an average F1 score of 0.967.

Електросапа – культиватор «Мрія»

In this article, we propose the design of a universal electric hoe - a cultivator with a balancing drive wheel for inter-row cultivation of row crops. Currently, chemical methods and mechanical loosening are widespread in modern agriculture, but concerns about the use of herbicides, especially in "organic farming," leave no choice. We propose a machine that allows for inter-row cultivation of any row crops, even by a child. The proposed device is to be equipped with an electric motor with an electric battery, which will allow for greater mobility, low noise and vibration levels, as well as a set of tools to perform all technological operations when growing row crops. Ninety percent of manual labor in the cultivation of garden crops is spent on sowing and subsequent cultivation of row spacing. Therefore, the device must have a certain set of tools, such as a "hiller paw" for cutting furrows and hilling; a "one-sided paw" for removing weeds from the row spacing; a "lancet paw" for deep loosening and crust destruction. As can be seen from the above, manual weeding is the most effective method of weed control for working on a personal plot. However, due to the high labor intensity of the work, the task of creating a device for processing both inter-row and intra-row weeding is now being addressed. Field tests have shown that this device performs all the tasks in full, and the battery capacity, subject to continuous processing, is enough for 400 m2. The next step in improving the functional characteristics of the proposed design is to equip the electric chopper-cultivator with a device for inter-row cultivation. Inter-row cultivation is a method of weed control that involves removing weeds from the internal space between plants in one row. This process can be an important step in crop care and is aimed at reducing competition between weeds and crops. It is important to perform inter-row weeding in a timely manner, especially in the early stages of crop growth when weeds are just beginning to gain weight. It is economically feasible for the operator to control the machine manually. After a short practice, the control accuracy can reach several centimeters.

旱作条件下大豆玉米复合种植系统水分利用特征和生产力的比较研究

Quantitative investigation of the utilization characteristics of precipitation resources and the production capacity of the intercropping production system under the soybean maize intercropping mode in northern China is of great significance for improving the resource utilization rate of dryland agriculture and regional sustainable development. This study adopted a completely randomized block design, and six planting modes were set up: soybean monoculture, maize monoculture, and four soybean maize intercropping modes. Plant height and grain yield data were observed, and precipitation resource utilization efficiency was calculated. Based on the FAO-56 recommended crop coefficients, the crop coefficients were corrected according to the actual growth of crops in the experimental area; combined with meteorological data, the crop water demand under different planting modes was estimated based on the Penman-Monteith principle. The results showed that crops' daily growth water demand under different planting modes was generally deficient in water in the early and middle stages of growth, and surplus in the later stages. Compared with the monoculture mode, there were significant differences in precipitation resource utilization efficiency under different intercropping modes (P < 0.05), with soybeans reduced by 67.50% ~ 89.56%, and corn reduced by 43.99% ~ 61.05%. Based on the semi-humid and semi-arid area of the eastern Loess Plateau, an appropriate irrigation system needs to be designated for soybean and corn intercropping systems in the early and middle stages of crop growth. Under a certain proportion of water resource supply, different modes have different water resource utilization efficiency and final production capacity for the intercropping system.

Combining biophysical parameters with thermal and RGB indices using machine learning models for predicting yield in yellow rust affected wheat crop.

Evaluating crop health and forecasting yields in the early stages are crucial for effective crop and market management during periods of biotic stress for both farmers and policymakers. Field experiments were conducted during 2017-18 and 2018-19 with objective to evaluate the effect of yellow rust on various biophysical parameters of 24 wheat cultivars, with varying levels of resistance to yellow rust and to develop machine learning(ML) models with improved accuracy for predicting yield by integrating thermal and RGB indices with crucial plant biophysical parameters. Results revealed that as the level of rust increased, so did the canopy temperature and there was a significant decrease in crop photosynthesis, transpiration, stomatal conductance, leaf area index, membrane stability index, relative leaf water content, and normalized difference vegetation index due to rust, and the reductions were directly correlated with levels of rust severity. The yield reduction in moderate resistant, low resistant and susceptible cultivars as compared to resistant cultivars, varied from 15.9-16.9%, 28.6-34.4% and 59-61.1%, respectively. The ML models were able to provide relatively accurate early yield estimates, with the accuracy increasing as the harvest approached. The yield prediction performance of the different ML models varied with the stage of the crop growth. Based on the validation output of different ML models, Cubist, PLS, and SpikeSlab models were found to be effective in predicting the wheat yield at an early stage(55-60 days after sowing) of crop growth. The KNN, Cubist, SLR, RF, SpikeSlab, XGB, GPR and PLS models were proved to be more useful in predicting the crop yield at the middlestage(70days after sowing) of the crop, while RF, SpikeSlab, KNN, Cubist, ELNET, GPR, SLR, XGB and MARS models were found good to predict the crop yield at late stage (80days after sowing). The study quantified the impact of different levels of rust severity on crop biophysical parameters and demonstrated the usefulness of remote sensing and biophysical parameters data integration using machine-learning models for early yield prediction under biotically stressed conditions.

Crop Classification and Growth Monitoring in Coal Mining Subsidence Water Areas Based on Sentinel Satellite

In high groundwater level mining areas, subsidence resulting from mining can lead to waterlogging in farmland, causing damage to crops and affecting their growth and development, thereby affecting regional food security. Therefore, it is necessary to restore agricultural production in the coal mining subsidence water areas in the densely populated eastern plains. This study focuses on the Yongcheng coal mining subsidence water areas. It utilizes Sentinel-1 and Sentinel-2 data from May to October in the years 2019 to 2022 to monitor the growth and development of crops. The results demonstrated that (1) the accuracy of aquatic crops categorization was improved by adjusting the elevation of the study region with Mining Subsidence Prediction Software (MSPS 1.0). The order of accuracy for classifying aquatic crops using different machine learning techniques is Random Forest (RF) > Classification and Regression Trees (CART) ≥ Support Vector Machine (SVM). Using the RF method, the obtained classification results can be used for subsequent crop growth monitoring. (2) During the early stages of crop growth, when vegetation cover is low, the Radar Vegetation Index (RVI) is sensitive to the volume scattering of crops, making it suitable for tracking the early growth processes of crops. The peak RVI values for crops from May to July are ranked in the following order: rice (2.595), euryale (2.590), corn (2.535), and lotus (2.483). (3) The order of crops showing improved growth conditions during the mid-growth stage is as follows: rice (47.4%), euryale (43.4%), lotus (27.6%), and corn (4.01%). This study demonstrates that in the Yongcheng coal subsidence water areas, the agricultural reclamation results for the grain-focused model with rice as the main crop and the medicinal herb-focused model with euryale as the main crop are significant. This study can serve as a reference for agricultural management and land reclamation efforts in other coal subsidence water areas.

Iron and copper on Botrytis cinerea: new inputs in the cellular characterization of their inhibitory effect.

Certain metals play key roles in infection by the gray mold fungus, Botrytis cinerea. Among them, copper and iron are necessary for redox and catalytic activity of enzymes and metalloproteins, but at high concentrations they are toxic. Understanding the mechanism requires more cell characterization studies for developing new, targeted metal-based fungicides to control fungal diseases on food crops. This study aims to characterize the inhibitory effect of copper and iron on B. cinerea by evaluating mycelial growth, sensitivity to cell wall perturbing agents (congo red and calcofluor white), membrane integrity, adhesion, conidial germination, and virulence. Tests of copper over the range of 2 to 8 mM and iron at 2 to 20 mM revealed that the concentration capable of reducing mycelial growth by 50% (IC50) was 2.87 mM and 9.08 mM for copper and iron, respectively. When mixed at equimolar amounts there was a significant inhibitory effect mostly attributable to copper. The effect of Cu50, Fe50, and Cu50-Fe50 was also studied on the mycelial growth of three wild B. cinerea strains, which were more sensitive to metallic inhibitors. A significant inhibition of conidial germination was correlated with adhesion capacity, indicating potential usefulness in controlling disease at early stages of crop growth. Comparisons of the effects of disruptive agents on the cell wall showed that Cu, Fe, and Cu-Fe did not exert their antifungal effect on the cell wall of B. cinerea. However, a relevant effect was observed on plasma membrane integrity. The pathogenicity test confirmed that virulence was correlated with the individual presence of Cu and Fe. Our results represent an important contribution that could be used to formulate and test metal-based fungicides targeted at early prevention or control of B. cinerea.

Performance of Different Percentage of Urea as Foliar Fertilizer on Growth and Development of Rice (Rabi dhan1)

Nitrogen can dramatically stimulate plant productivity as well as yield potential of intensified agriculture system. The unavailability of Nitrogen fertilizer specially urea during critical period (sowing season, drought, flood.) hamper the rice production. It is noticed in literature that the effectiveness of nitrogen fertilizer increased if it is applied in plants through foliage along with soil application. Present research work was planned to evaluate this theme so that the loss of yield due to unavailability of urea can be reduced by foliar application at various growth stages of rice. A research work was conducted to examine the “Performance of different percentage of urea as foliar fertilizer on growth and development of rice (Rabi dhan 1).” at the Agronomy and Agricultural extension Department Field, University of Rajshahi, Bangladesh from December 2022-May 2023. Rabi Dhan-1 rice variety with six different percentage of urea as foliar application were used for this experiment. 1. T1= N0% Control, 2. T2= 65% of recommended dose (N 50% as SA + 15% as FA), 3. T3= 70% of recommended dose (N50% as SA + 20% as FA), 4. T4= 75% of recommended dose (N50 % as SA + 25 % as FA), 5. T5= 80% of recommended dose (N60% as SA + 20% as FA) , 6. T6= N100% Soil application (Traditional practice). The experiment was laid out in a randomized complete block design with three replications. The significant effect of liquid fertilizer was found on almost all the parameters under studied. All the phenological and growth parameters showed the highest performance in T4 = 75% of recommended dose (N50 % as SA+ 25 % as FA) and lowest in T1= N0% Control. The result showed that the phenological characters like plant height, total number of tiller hill-1 was highest inT4 while the lowest was in T1. Plant height was increased with the increase of the age of plants. Chlorophyll content increased slowly at the early stage of crop growth and later declined. All the yield contributing characters like plant height, total number of tiller hill-1, effective tiller hill-1, panicle length, number of grains panicle-1, number of filled grain panicle-1, 1000 grain weight and grain yield were varied significantly due to different doses of fertilizer application.

Responses of soil CO2 efflux and microbial activity to water deficit under conventional and adaptation technology

Frequent periods of low rainfall accompanying climate change are expected to affect many aspects of agricultural production and greenhouse gas emissions. In this work, we conducted field experiments on soil CO2 efflux and soil microbial activity using two precipitation treatments and two crop management technologies. While the control treatment was exposed to natural precipitation, the reduced water availability was induced during the early stages of crop growth using rain-exclusion shelters within the water deficit treatment. We aimed to test the difference in the response to water deficit between two management technologies: conventional tillage (CT - ploughed without cover crops) and adaptation technology (AT - no-till with species rich cover crops). After a month of simulated water deficit, the soil CO2 efflux under a regime of adaptation technology was essentially the same as that under the conventional technology, and the decrease in soil CO2 efflux ranged between 22 % and 55 %. The water deficit had a technology-specific effect on microbes. While no effect on the microbial carbon under CT was observed, the water deficit decreased Cmic under AT. In contrast, the opposite pattern was observed for DHA. When a normal soil water regime was restored, CO2 efflux recovered rapidly while recovery of microbial biomass and dehydrogenase activity was more variable. Generally, the soil microbial biomass and activity recovered better under AT. The results show that while the reduction in Rs during water deficit and its recovery after the restoration of the ambient precipitation regime were not affected by the technology, the response of microbial biomass and activity to water deficit depended on the technology with better recovery under AT.

Turbulent fluxes at kilometer scale determined by optical-microwave scintillometry in a heterogeneous oasis cropland of the Heihe River Basin

Observations of kilometer-scale turbulent fluxes of sensible (H) and latent heat (LE) are required for the validation of flux estimate algorithms from satellite remote-sensing data and the development of parameterization schemes in the hydro-meteorological models. Since 2019, two sets of Optical and Microwave scintillometer (OMS) systems have been operated in the Heihe River Basin of northwestern China, one on an alpine grassland of upper reaches, another on an oasis cropland of middle reaches, to measure both the areal H and LE. Combined with the observations of eddy-covariance (EC) and meteorological tower systems in both sites, an improved procedure for OMS data processing is proposed. The newly proposed procedure especially improves the preprocessing of raw scintillation data, properly uses the current probably better Lüdi et al. (2005) method in deriving meteorological structure parameters, and chooses the coefficients of similarity functions by Kooijmans and Hartogensis (2016) in calculating fluxes. Evaluated with the results of rather homogeneous grassland, the area-averaged H and LE over the heterogeneous oasis are then determined. Estimates of H and LE agree reasonably well with those obtained from EC in most cases. However, the most interesting is that LE over the oasis during the early crop growing stages is clearly larger than that of EC; while both agree well during the longer crop grown periods. Footprint analysis shows that, compared with EC, the OMS has clearly larger source area that contains a slight area of orchard and shelterbelts distributed near the light path, leading to larger LE during the early stages of crop growth. The area-averaged evapotranspiration (ET) over the oasis is then analyzed more acceptably, which varies from 3 to 5 mm day−1 depending on meteorological conditions during the 39 days of the crop growing period. These results are used to validate the Penman-Monteith-Leuning Version 2 (PML-V2) scheme.

Plot level sugarcane yield estimation by machine learning on multispectral images: A case study of Bundaberg, Australia

Early crop yield prediction provides critical information for Precision Agriculture (PA) procedures, policymaking, and food security. The availability of Remote Sensing (RS) datasets and Machine Learning (ML) approaches improved the prediction of sugarcane crop yield on the local and global scales, but an additional effort on the plot scale prediction is required. Challenges for plot-level prediction include a high ratooning capacity of the sugarcane crop, the lack of high spatial resolution data during the critical growth stages, and the non-linear complexation of yield data. The principal objective of the study is to analyse the potential of a time series of high-resolution multispectral Unmanned Aerial Vehicle (UAV) imagery along with three advanced ML techniques, namely Random Forest Regression (RFR), Support Vector Regression (SVR), and Nonlinear Autoregressive Exogenous Artificial Neural Network (NARX ANN) as a solution to the plot-level sugarcane yield prediction. An experimental sugarcane field containing 48 plots was selected, and UAV imagery was collected during the three consecutive cropping seasons' early and middle crop growth stages. Each dataset per growth stage was analyzed separately to predict the sugarcane crop yield in an attempt to discover how early the prediction of pre-harvest yield can be achieved. The datasets of the first two cropping seasons were trained and tested using the three ML techniques, utilizing 10-fold cross-validation to avoid overfitting. The third cropping season dataset was then used to evaluate the reliability of the developed prediction models. The results show that the correlation of Vegetation Indices (VIs) with crop yield in the middle stage outperforms the early stage in all three ML models. Moreover, comparing these models indicates that the NARX ANN method outperformed the others in the middle stage with the highest correlation coefficient (R2) of 0.96 and the lowest Root Mean Square Error (RMSE) of 4.92 t/ha. It was followed by the SVR (R2 = 0.52, RMSE of 14.85 t/ha), which performed similarly to the RFR method (R2 = 0.48, RMSE = 11.20 t/ha). In conclusion, the best-suited model for predicting sugarcane yields during the middle growth stage is a NARX ANN model employing the Normalized Difference RedEdge (NDRE), which demonstrates the feasibility of the ML approaches to predict the plot level sugarcane yield at a specific period of growth as they are less sensitive to the inconsistency of data collection times.

RSARE: A physically-based vegetation index for estimating wheat green LAI to mitigate the impact of leaf chlorophyll content and residue-soil background

The green leaf area index (LAI) is an important structural parameter that can be used for monitoring plant transpiration and carbon cycling over large spatial extents. LAI is also critical for understanding biophysical processes and predicting crop productivity. However, the retrieval of wheat green LAI based on canopy reflectance is always affected by the background material. For example, in the Yangtze River Basin of China, rice residues are typically returned to rice–wheat rotation fields. Thus the background for observing wheat canopy varies from a mixture of traditional wheat-soil components to wheat-residue or wheat-residue-soil, posing a significant challenge for accurate LAI estimation at the early stages of crop growth. Furthermore, when current vegetation indices (VIs) are used to estimate LAI, leaf chlorophyll content (LCC) and field moisture are two additional confounding factors. To resolve these issues, we propose a novel residue-soil adjusted red edge difference index (RSARE), which draws on concepts from a simple physical algorithm, i.e., linear spectral mixture analysis (LSMA). The results of field, satellite and modeling experiments demonstrate that the RSARE-LAI model is generally not sensitive to LCC, or to variations in residue-soil and traditional soil backgrounds, and estimates LAI with high accuracy (RMSEval = 0.55, RRMSEval=20.71%). In comparison to traditional VI-LAI maps, RASRE-LAI maps are less sensitive to confounding factors associated with the background components, field moisture, LCC, and both inter-annual and geographical differences. This novel residue-soil background-resistant RSARE particularly improves the accuracy of wheat LAI estimation at low LAI levels, thus facilitating large-scale mapping of LAI in the early growing season.

Effect of Municipal Solid Wastes On Grain Yield of Three Maize Cultivars

The growth and yield of three maize cultivars were examined in relation to the impact of Municipal Solid Wastes (MSW) enriched soil. According to the results, plants grown on soil treated with MSW and inorganic fertilizer (NPK) produced significantly (P≤0.05) taller plants, larger leaves and higher number of leaves than plants grown on the control soil. On the other hand, plants grown on soils fertilized with MSW 1 showed significantly (P≤0.05) greater values of these indicators. The study also demonstrated that maize cultivar TZE yellow, EVDT 99 and Comp-1-synthetic showed no significant variation at the later growth stage but TZE yellow significantly (P≤0.05) had taller plants, larger leaves, and higher number of leaves at the early stage of crop growth. Comp-1-synthetic produced more grain and more grains per cob which was statistically at par with TZE yellow but differ significantly (P≤0.05) from EVDT 99. Crops grown on MSW and NPK treated soils had significantly (P≤0.05) higher grain yields and more grains per cob than those grown on the control soil. However, compared to NPK treated soils, MSW treated soils significantly (P≤0.05) produced better grain yield and number of grains per cob. The maize cultivars' differences in crude protein content were significant at P≤0.05. The crude protein content of TZE yellow was significantly (P≤0.05) higher than that of EVDT 99 and Comp-1-synthetic. Additionally, plants cultivated on MSW 1 treated soil had considerably higher crude protein content than plants grown on other soil types. Therefore, if municipal solid wastes are sorted and handled, MSW enriched soils could be employed to boost maize production.