Crop Growth Data Research Articles

A Coffee Plant Counting Method Based on Dual-Channel NMS and YOLOv9 Leveraging UAV Multispectral Imaging

Accurate coffee plant counting is a crucial metric for yield estimation and a key component of precision agriculture. While multispectral UAV technology provides more accurate crop growth data, the varying spectral characteristics of coffee plants across different phenological stages complicate automatic plant counting. This study compared the performance of mainstream YOLO models for coffee detection and segmentation, identifying YOLOv9 as the best-performing model, with it achieving high precision in both detection (P = 89.3%, mAP50 = 94.6%) and segmentation performance (P = 88.9%, mAP50 = 94.8%). Furthermore, we studied various spectral combinations from UAV data and found that RGB was most effective during the flowering stage, while RGN (Red, Green, Near-infrared) was more suitable for non-flowering periods. Based on these findings, we proposed an innovative dual-channel non-maximum suppression method (dual-channel NMS), which merges YOLOv9 detection results from both RGB and RGN data, leveraging the strengths of each spectral combination to enhance detection accuracy and achieving a final counting accuracy of 98.4%. This study highlights the importance of integrating UAV multispectral technology with deep learning for coffee detection and offers new insights for the implementation of precision agriculture.

Grain Crop Yield Prediction Using Machine Learning Based on UAV Remote Sensing: A Systematic Literature Review

Preharvest crop yield estimation is crucial for achieving food security and managing crop growth. Unmanned aerial vehicles (UAVs) can quickly and accurately acquire field crop growth data and are important mediums for collecting agricultural remote sensing data. With the rapid development of machine learning, especially deep learning, research on yield estimation based on UAV remote sensing data and machine learning has achieved excellent results. This paper systematically reviews the current research of yield estimation research based on UAV remote sensing and machine learning through a search of 76 articles, covering aspects such as the grain crops studied, research questions, data collection, feature selection, optimal yield estimation models, and optimal growth periods for yield estimation. Through visual and narrative analysis, the conclusion covers all the proposed research questions. Wheat, corn, rice, and soybeans are the main research objects, and the mechanisms of nitrogen fertilizer application, irrigation, crop variety diversity, and gene diversity have received widespread attention. In the modeling process, feature selection is the key to improving the robustness and accuracy of the model. Whether based on single modal features or multimodal features for yield estimation research, multispectral images are the main source of feature information. The optimal yield estimation model may vary depending on the selected features and the period of data collection, but random forest and convolutional neural networks still perform the best in most cases. Finally, this study delves into the challenges currently faced in terms of data volume, feature selection and optimization, determining the optimal growth period, algorithm selection and application, and the limitations of UAVs. Further research is needed in areas such as data augmentation, feature engineering, algorithm improvement, and real-time yield estimation in the future.

Application of Clay and Rice Husk Biochar and its effect on Soil Pore Distribution of Psamment and Corn Yield

Abstract The effect of clay and rice husk biochar application under minimum soil tillage on soil pore distribution of psamment and corn yield was conducted in Batang Anai District, Padang Pariaman Regency. Plot experiment consisting of four treatment (without clay or biochar; 20 ton clay/ha; 20 ton biochar/ha; and 20 ton biochar/ha + 20 ton clay/ha) with 3 replications. Soil samples were analyzed at the soil laboratory, Faculty of Agriculture, Andalas University Padang and Soil Laboratory, Bogor Soil Research Institute. Soil physical properties were categorized by soil criteria, while crop growth and yield data were statistically analyzed using F test and DNMRT at 5% level. Results showed that application of 20 ton/ha of clay and rice husk biochar improved soil physical properties of psamment namely: decreasing soil bulk density, increasing total soil porosity, aeration pores, and available water pores. The application of biochar and clay has also increased the growth and yield of corn.

Development of IoT-Based Remote Monitoring Module for Greenhouse Environment to Facilitate Crop Growth Data Analysis

Development of IoT-Based Remote Monitoring Module for Greenhouse Environment to Facilitate Crop Growth Data Analysis

IHUP: An Integrated High-Throughput Universal Phenotyping Software Platform to Accelerate Unmanned-Aerial-Vehicle-Based Field Plant Phenotypic Data Extraction and Analysis.

With the threshold for crop growth data collection having been markedly decreased by sensor miniaturization and cost reduction, unmanned aerial vehicle (UAV)-based low-altitude remote sensing has shown remarkable advantages in field phenotyping experiments. However, the requirement of interdisciplinary knowledge and the complexity of the workflow have seriously hindered researchers from extracting plot-level phenotypic data from multisource and multitemporal UAV images. To address these challenges, we developed the Integrated High-Throughput Universal Phenotyping (IHUP) software as a data producer and study accelerator that included 4 functional modules: preprocessing, data extraction, data management, and data analysis. Data extraction and analysis requiring complex and multidisciplinary knowledge were simplified through integrated and automated processing. Within a graphical user interface, users can compute image feature information, structural traits, and vegetation indices (VIs), which are indicators of morphological and biochemical traits, in an integrated and high-throughput manner. To fulfill data requirements for different crops, extraction methods such as VI calculation formulae can be customized. To demonstrate and test the composition and performance of the software, we conducted case-related rice drought phenotype monitoring experiments. In combination with a rice leaf rolling score predictive model, leaf rolling score, plant height, VIs, fresh weight, and drought weight were efficiently extracted from multiphase continuous monitoring data. Despite the significant impact of image processing during plot clipping on processing efficiency, the software can extract traits from approximately 500 plots/min in most application cases. The software offers a user-friendly graphical user interface and interfaces for customizing or integrating various feature extraction algorithms, thereby significantly reducing barriers for nonexperts. It holds the promise of significantly accelerating data production in UAV phenotyping experiments.

머신러닝 기반 노지 환경 변수에 따른 예측 토양 수분에 미치는 영향에 대한 연구

As understanding sudden climate change and agricultural productivity becomes increasingly important due to global warming, soil moisture prediction is emerging as a key topic in agriculture. Soil moisture has a significant impact on crop growth and health, and proper management and accurate prediction are key factors in improving agricultural productivity and resource management. For this reason, soil moisture prediction is receiving great attention in agricultural and environmental fields. In this paper, we collected and analyzed open field environmental data using a pilot field through random forest, a machine learning algorithm, obtained the correlation between data characteristics and soil moisture, and compared the actual and predicted values of soil moisture. As a result of the comparison, the prediction rate was about 92%. It was confirmed that the accuracy was . If soil moisture prediction is carried out by adding crop growth data variables through future research, key information such as crop growth speed and appropriate irrigation timing according to soil moisture can be accurately controlled to increase crop quality and improve productivity and water management efficiency. It is expected that this will have a positive impact on resource efficiency.

Generic optimization approach of soil hydraulic parameters for site-specific model applications

Site-specific crop management is based on the postulate of varying soil and crop requirements in a field. Therefore, a field is separated into homogenous management zones, using available data to adapt management practices environment to maximize productivity and profitability while reducing environmental impacts. Due to advancing sensor technologies, crop growth and yield data on more minor scales are common, but soil data often needs to be more appropriate. Crop growth models have shown promise as a decision support tool for site-specific farming. The Decision Support System for Agrotechnology Transfer (DSSAT) is a widely used point-based model. To overcome the problem of inappropriate soil input data problem, this study introduces an external plug-in program called Soil Profile Optimizer (SPO), which uses the current DSSAT v4.8 to calibrate soil profile parameters on a site-specific level. Developed as an inverse modelling approach, the SPO can calibrate selected soil profile parameters by targeting available in-season plant data. Root Mean Square Error (RMSE) and normalized RMSE as error minimization criteria are used. The SPO was tested and evaluated by comparing different simulation scenarios in a case study of a 3-yr field trial with maize. The scenario with optimized soil profiles, conducted with the SPO, resulted in an R2 of 0.76 between simulated and observed yield and led to significant improvements compared to the scenario conducted with field scale soil profile information (R2 0.03). The SPO showed promise in using spatial plant measurements to estimate management zone scale soil parameters required for the DSSAT model.

Regulating the time of the crop model clock: A data assimilation framework for regions with high phenological heterogeneity

in crop growth data assimilation systems, the mismatch between simulated and observed phenology significantly deteriorates the performance of crop growth modeling. This situation may be more severe for smallholder farmers-managed fields, where the phenological heterogeneity was high even when climate condition was relatively uniform. Previous studies investigated the non-sequential methods to retrospectively assimilate historical phenology observations. However, approaches to dynamically assimilating phenological measurements through sequential data assimilation methods remain unexplored one of the most intractable challenges of dynamic phenology assimilation is that a considerable proportion of model parameters and variables are entangled with phenology, therefore simply assimilating phenological measurements could disturb the model clock. This study aims to establish a robust crop data assimilation framework capable of assimilating phenological measurements in real time without disturbing the model clock the framework used an open-source version of the AquaCrop model to simulate crop growth and used the ensemble Kalman filter (EnKF) to assimilate observations sequentially. A parameter refresh method was proposed to restore the phenological consistency of model parameters after updating the phenology state. Assimilation strategies with different observation types and compositions of state vectors were designed after a global sensitivity analysis of model parameters. These strategies were evaluated through the Observing System Simulation Experiments (OSSE), and the selected strategies were tested in a real-world case. the results of the OSS Experiments show that the phenological mismatch problem greatly affects crop growth simulation, and this mismatch could not be narrowed effectively by assimilating non-phenological observations. Assimilating phenological measurements with the proposed parameters refresh method and assimilation strategies closed this mismatch and produced better performance compared to the Restart-EnKF method. In the real-world paddy rice case, assimilating phenology with the proposed strategies significantly improved yield estimation in low-yield plots (less than 4 ton/ha) compared to assimilating canopy cover (CC) alone, with an R2 increase from 0.07 to 0.48. Assimilating CC, biomass and phenology simultaneously produced the best yield estimation for all plots, with R2 = 0.57 and RMSE = 1.00 ton/ha. assimilating phenology under a consistent model clock significantly improved yield estimation when the phenological heterogeneity of plots was high. the results highlight the effectiveness and robustness of the established data assimilation framework for dynamic crop growth simulation, indicating the potentials of the proposed data assimilation framework for regional in-season crop modeling and yield forecasting.

Wheat yield modeling under water-saving irrigation and climatic scenarios in transition from surface to sprinkler irrigation systems

• Remote sensing can provide historical growth data for crop modeling on large scales. • Water productivity could be improved by 18% by deficit irrigation. • Irrigation could be reduced by 150 mm in wet and normal years with no yield loss. • Irrigation strategy under erratic rainfall is vital in improving water productivity. • Sprinkler irrigation in dry years decreased water productivity. Water-saving through deficit irrigation management is a prominent approach to improving water productivity (WP) in sustainable agriculture. However, it is not feasible to assess many irrigation scenarios and their impact on WP. Therefore, crop modeling is a valuable tool for evaluating the effect of different irrigation scenarios on yield and WP. No study has evaluated surface irrigation and transition to sprinkler irrigation under different climatic scenarios and irrigation strategies. In this study, historical remotely sensed crop growth and field-measured grain yield (GY) data were used to calibrate and validate the AquaCrop model for a wide wheat-cultivated area. Then, the model was used to investigate the impact of different surface deficit irrigation strategies under five different climatic scenarios, including wet, normal, and dry on WP and GY. Then the model was used to assess the effect of different sprinkler irrigation strategies and their application efficiency on WP and GY under the climatic scenarios. Based on AquaCrop scenario analyses, irrigation water requirements varied between 270 and 620 mm in different growing seasons regarding rainfall characteristics (amount and distribution). Also, reducing irrigation water by 30% improved WP between 0% and 18% in different growing seasons. Moreover, sprinkler irrigation could improve WP and sustain yield production only in normal and wet years with high application efficiency and proper irrigation strategy. Otherwise, sprinkler irrigation would decrease WP and increase the pressure on water recourses. Therefore, in such areas with erratic rainfalls, synchronizing irrigation strategy with the rainfall characteristics increases WP and sustains crop production.

Response of dryland crops to climate change and drought-resistant and water-suitable planting technology: A case of spring maize

Climate change has a significant impact on agriculture. However, the impact investigation is currently limited to the analysis of meteorological data, and there is a dearth of long-term monitoring of crop phenology and soil moisture associated with climate change. In this study, temperature and precipitation (1957–2020) were recorded, crop growth (1981–2019) data were collected, and field experiments were conducted at central and eastern Gansu and southern Ningxia, China. The mean temperature increased by 0.36°C, and precipitation decreased by 11.17 mm per decade. The average evapotranspiration (ET) of winter wheat in 39 years from 1981 to 2019 was 362.1 mm, demonstrating a 22.1-mm decrease every 10 years. However, the ET of spring maize was 405.5 mm over 35 years (1985–2019), which did not show a downward trend. Every 10 years, growth periods were shortened by 5.19 and 6.47 d, sowing dates were delayed by 3.56 and 1.68 d, and maturity dates advanced by 1.76 and 5.51 d, respectively, for wheat and maize. A film fully-mulched ridge–furrow (FMRF) system with a rain-harvesting efficiency of 65.7-92.7% promotes deep rainwater infiltration into the soil. This leads to double the soil moisture in-furrow, increasing the water satisfaction rate by 110-160%. A 15-year grain yield of maize increased by 19.87% with the FMRF compared with that of half-mulched flat planting. Grain yield and water use efficiency of maize increased by 20.6 and 17.4% when the density grew from 4.5×104 to 6.75×104 plants ha–1 and improved by 12.0 and 12.7% when the density increased from 6.75×104 to 9.0×104 plants ha–1, respectively. Moreover, responses of maize yield to density and the corresponding density of the maximum yield varied highly in different rainfall areas. The density parameter suitable for water planting was 174 maize plants ha–1 with 10 mm rainfall. Therefore, management strategies should focus on adjusting crop planting structure, FMRF water harvesting system, and water-suitable planting to mitigate the adverse effects of climate change and enhance sustainable production of maize in the drylands.

The Modified Brière Equation and Its Applications.

The Brière equation (BE) is widely used to describe the effect of temperature on the development rate of insects, and it can produce both symmetrical and asymmetrical bell-shaped curves. Because of its elasticity in curve fitting, the integrated form of BE has been recommended for use as a sigmoid growth equation to describe the increase in plant biomass with time. However, the start time of growth predicted by the sigmoid growth equation based on the BE is not completely comparable to empirical crop growth data. In the present study, we modified the BE by adding an additional parameter to further increase its elasticity for data fitting. We termed this new equation the modified Brière equation (MBE). Data for the actual height and biomass of 15 species of plants (with two cultivars for one species) were fit with the sigmoid growth equations based on both the BE and MBE assuming that the growth start time was zero for both. The goodness of fit of the BE and MBE sigmoid growth equations were compared based on their root-mean-square errors and the corresponding absolute percentage error between them when fit to these data. For most species, we found that the MBE sigmoid growth equation achieved a better goodness of fit than the BE sigmoid growth equation. This work provides a useful tool for quantifying the ontogenetic or population growth of plants.

5G-Oriented IoT Big Data Analysis Method System

The application degree and application scope of 5G Internet of Things technology and big data analysis technology are becoming wider and wider, bringing opportunities for the development of traditional enterprises and providing technological innovation support for the development of new enterprises. Based on 5G Internet of Things technology and big data technology, this paper designs and studies an intelligent agricultural monitoring platform. We collect crop growth data and monitor crop growth status through this platform to study the 5G-oriented IoT big data analysis method system. This paper studies the data collection and storage issues involved in the huge agricultural IoT data environment. This article analyzes the specific sources of agricultural big data, the specific methods of data collection, and the methods of various database storage technologies. Combining wireless sensor network technology, large-source data processing technology, and distributed data storage technology, a method is proposed to solve the problem of rural Internet data collection and storage in the big data environment. This paper proposes a spatiotemporal block processing TSBPS to store the first detection data. The method uses spatiotemporal preblocking, data compression, and caching to significantly improve the recording speed of near real-time storage and microdetection data. In the experimental part of this article, experiments are carried out on the key parts of the IOT-HSQM system model that may limit storage or query performance. Experimental results show that this article compares TSBPS and direct writing methods. The maximum write speed increased by 79%, and the average write speed increased by 42%. The IOT-HSQM system model can meet the requirements of compiling and query performance and statistical analysis.

Saline Irrigation Water Retards Growth of Amaranthus in Coastal Kenya

Salinity is a major biotic factor that negatively affects growth and yield of crops. Over 90% of the coastal region of Kenya is arid and semi-arid, most farmers in the region use borehole irrigation water which is saline. Amaranthus spp. is one of the main vegetables grown in coastal region. There is limited information regarding the effect of salinity on amaranthus production. The study sought to determine the effect of saline irrigation water on amaranthus growth in coastal Kenya. Two experiments were set up, one at Mivumoni Secondary School farm in Kwale County and another at Pwani University farm in Kilifi County from beginning of September 2019 to the end of January, 2020. The experiments were laid out in a randomized complete block design and replicated three times. The six treatments tested were: fresh water alone, 75% saline water alone, 100% saline water alone, fresh water + DAP, 75% saline water + DAP, 100% saline water + DAP. Crop growth data collected were: emergence rate, plant height, leaf number, leaf area, chlorophyll content, stem thickness, root density, root weight, root volume and total plant biomass. Data obtained were subjected to analysis of variance using SAS statistical package (SAS, Version 10) and treatment effects were tested for significance using F-test. Significant means at F-test was ranked using Tukey’s test at 5% level of significance. Amaranthus seeds sown in fresh water had higher emergence rate compared to seeds sown in saline water. Salinity regardless of concentration used and application of DAP, resulted in decrease in height, leaf number, leaf area, stem tickness, chlorophyll content, root length, root weight, root volume and total biomass. The study demonstrates that saline irrigation water in coastal Kenya has a negative effect on Amaranthus growth.

Smart Contract-Based Agricultural Food Supply Chain Traceability

The complexity of a supply chain makes product safety or quality issues extremely difficult to track, especially for the basic agricultural food supply chains of people's daily diets. The existing agricultural food supply chains present several major problems, such as numerous participants, inconvenient communication caused by long supply chain cycles, data distrust between participants and the centralized system. The emergence of blockchain technology effectively solves the pain-point problem existing in the traceability system of agricultural food supply chains. This paper proposes a framework based on the consortium and smart contracts to track and trace the workflow of agricultural food supply chains, implement traceability and shareability of supply chains, and break down the information islands between enterprises as much as possible to eliminate the need for the central institutions and agencies and improve the integrity of the transaction records, reliability and security. At the same time, farmers record details of the environment and crop growth data in the InterPlanetary File System (IPFS) and store file IPFS hashes in smart contracts, which not only increases data security but also alleviates the blockchain storage explosion problem. This framework has been applied in Shanwei Lvfengyuan Modern Agricultural Development Co., Ltd. Although there are still many defects, the framework has successfully realized functions such as disintermediation and tracing of agricultural product information through QR codes. Thus, the framework proposed in this paper is of great significance and reference value for enterprises to ensure product quality and safety traceability.

Improving the monitoring of corn phenology in large agricultural areas using remote sensing data series

Aim of study: Mexico's large irrigation areas demand non-structural actions to improve the irrigation service, such as monitoring crop phenology; however, its application has been limited by the large volumes of field information generated, diversity of crop management and climatic variability. The objective of this study was to generate and validate a methodology to monitor corn (Zea mays L.) phenology from the historical relationship of the vegetation indexes (VIs), EVI and NDVI, with the phenological development (PD) of corn grown in large irrigation zones.Area of study: Irrigation District (ID) 075 “Valle del Fuerte”, northern Sinaloa, Mexico.Material and methods: We used a database of 20 years of climate, field crop growth and crop phenology data, and Landsat satellite images. A methodology was proposed on a large scale supported with GIS and remote sensing data series.Main results: The methodology was validated in 19 plots with an acceptable correlation between observed PD and estimated PD for the two VIs, with slightly better values for EVI than for NDVI. NDVI and EVI models agreed with experimental PD observations in 92.1% of the farms used to validate the methodology, in 2.5% only the NDVI model coincided with the real, in 3.1% only the EVI model coincided, and in 2.3% both models disagreed with observation, generated a stage out of phase with respect to the real phenological stage.Research highlights: is possible to generalize the methodology applied to large irrigation zones with remote sensing data and GIS.

Comparison of root water uptake models in simulating CO<sub>2</sub> and H<sub>2</sub>O fluxes and growth of wheat

Abstract. Stomatal regulation and whole plant hydraulic signaling affect water fluxes and stress in plants. Land surface models and crop models use a coupled photosynthesis–stomatal conductance modeling approach. Those models estimate the effect of soil water stress on stomatal conductance directly from soil water content or soil hydraulic potential without explicit representation of hydraulic signals between the soil and stomata. In order to explicitly represent stomatal regulation by soil water status as a function of the hydraulic signal and its relation to the whole plant hydraulic conductance, we coupled the crop model LINTULCC2 and the root growth model SLIMROOT with Couvreur's root water uptake model (RWU) and the HILLFLOW soil water balance model. Since plant hydraulic conductance depends on the plant development, this model coupling represents a two-way coupling between growth and plant hydraulics. To evaluate the advantage of considering plant hydraulic conductance and hydraulic signaling, we compared the performance of this newly coupled model with another commonly used approach that relates root water uptake and plant stress directly to the root zone water hydraulic potential (HILLFLOW with Feddes' RWU model). Simulations were compared with gas flux measurements and crop growth data from a wheat crop grown under three water supply regimes (sheltered, rainfed, and irrigated) and two soil types (stony and silty) in western Germany in 2016. The two models showed a relatively similar performance in the simulation of dry matter, leaf area index (LAI), root growth, RWU, gross assimilation rate, and soil water content. The Feddes model predicts more stress and less growth in the silty soil than in the stony soil, which is opposite to the observed growth. The Couvreur model better represents the difference in growth between the two soils and the different treatments. The newly coupled model (HILLFLOW–Couvreur's RWU–SLIMROOT–LINTULCC2) was also able to simulate the dynamics and magnitude of whole plant hydraulic conductance over the growing season. This demonstrates the importance of two-way feedbacks between growth and root water uptake for predicting the crop response to different soil water conditions in different soils. Our results suggest that a better representation of the effects of soil characteristics on root growth is needed for reliable estimations of root hydraulic conductance and gas fluxes, particularly in heterogeneous fields. The newly coupled soil–plant model marks a promising approach but requires further testing for other scenarios regarding crops, soil, and climate.

Use of Multiple Environment Variety Trials Data to Simulate Maize Yields in the Ogallala Aquifer Region: A Two Model Approach

AbstractWith a long‐term goal to optimize use of groundwater in the Ogallala Aquifer Region (OAR) to sustain food production systems, this study was conducted to calibrate Decision Support System for Agrotechnology Transfer (DSSAT) and AquaCrop crop modeling platforms to simulate maize production at a regional scale using historic datasets. Calibration of the models with local crop growth data and crop management practices is important, but usually this in‐season crop growth information is not available. This study determined the possibility of using maize variety trial data for the evaluation of the CSM‐Crop Estimation through Resources and Environmental Synthesis‐Maize and AquaCrop models in the OAR. The models were calibrated and tested in three counties in Nebraska. Both the models were then used to simulate irrigated maize yield during 1988 to 2015 for all three counties. The criteria for evaluating the performance of these crop models included statistical parameters and graphical analysis. The performance of both models were then compared with the observed yield from field variety test results and historic National Agricultural Statistical Service yields. The results indicated that difference between yield of calibrated DSSAT model and observed yield was less than 10% and AquaCrop root mean square error ranged from 740 to 1,820 kg/ha. Long‐term comparison between observed and simulated Nebraska county yields also indicated confidence in calibrating crop models with typical end of season yield data and using these models for studying crop production at regional scales when detailed in‐season crop growth observed data are not available.

Crop Height Monitoring Using a Consumer-Grade Camera and UAV Technology

Recent advances in the ability to capture high spatial resolution images by unmanned aerial vehicles (UAVs) have shown the potential of this technology for a wide range of application including exploring the effects of different external stimuli when monitoring environmental and structural variables. In this paper, we show the application of UAV technology for crop height monitoring and modelling to provide quantitative crop growth data and demonstrate the remote sensing and photogrammetric capabilities of the technology to the farming industry. This study was carried out in a field trial involving a combination of six wheat varieties and three different fungicide treatments. The UAV imagery of the field trial site was captured on five occasions throughout crop development. These were used to create digital surface models from which crop surface models (CSMs) were extracted for the cropped areas. Crop heights are estimated from the photogrammetric derived CSMs and are compared against the reference heights captured using Real-Time Kinematic Global Navigation Satellite System (GNSS) to validate the CSMs. Furthermore, crop growth differences among varieties are analysed; and crop height correlations with grain yield as well as with independently estimated vegetation indices are evaluated. These evaluations show that the technology is suitable (with average bias range 2–10 cm depending on wind conditions relative to GNSS height) and has potential for quantitative and qualitative monitoring of canopy and/or crop height and growth.

Determination of the Critical Period of Cyper-diforce® Treatment against Arthropod fauna and Productivity of Watermelon

Watermelon has been reported to be vulnerable to insect pest pressure throughout its growth stages with a resultant indiscriminate calendar-based insecticide application (of up to 25 sprays/season in the study area), with its attendant consequences. In order to recommend the crop growth period(s) to effect chemical control measures that will give better return on investment, field trials were set-up in the early- and late-cropping seasons of 2016 and 2017. Forty, 5m long x 8m wide plots were demarcated in randomized complete block design in 4 replications. Treatments were applications of 0.5% Cyper-diforce® (Cypermethrin 30g/L + Dimethoate 250g/L) at seedling, mid-vegetative, mid-flowering, mid-fruiting stages and their combinations. Unsprayed plots served as control. Pest and beneficial arthropod density, leaf injury, crop growth and yield data were collected and subjected to variance analysis and significantly different means were separated by SNK at 5% level of probability. Cost:benefit ratios of the various treatments was also computed. Findings indicate that, leaf-eating beetles had the highest impact on stand survival and yield. Insect pest density and damage generally decreased with increase in frequency of insecticide application and treatment differences were significant (p< 0.05) except in the case of A. gossypii in the late-sown crop of 2017. Plot sprayed at seedling + vegetative + fruiting stages gave the highest fruit yield and return on investment. It is therefore suggested that, for economic production of watermelon, the crop should not be sprayed during the flowering period.

A systems-level yield gap assessment of maize-soybean rotation under high- and low-management inputs in the Western US Corn Belt using APSIM

Quantifying yield gaps (potential minus actual yield) and identifying management practices to close those gaps is critical for sustaining high-yielding production systems. The objectives of this study were to: 1) calibrate and validate the APSIM maize and soybean models using local field experimental data and 2) use the calibrated model to estimate and explain yield gaps in the long term as a function of management (high- vs low-input) and weather conditions (wet-warm, wet-cold, dry-warm and dry-cold years) in the western US Corn Belt. The model was calibrated and validated using in-season crop growth data from six maize-soybean rotations obtained in 2014 and 2015 in Kansas, US. Experimental data included two management systems: 1) Common Practices (CP, low-input), wide row spacing, lower seeding rate, and lack of nutrient applications (except N in maize), and 2) Intensified Practices (IP, high-input), narrow rows, high seeding rate, and balanced nutrition. Results indicated that APSIM simulated in-season crop above ground mass and nitrogen (N) dynamics as well yields with a modeling efficiency of 0.75 to 0.92 and a relative root mean square error of 18 to 31%. The simulated maize yield gap across all years was 4.2 and 2.5 Mg ha−1 for low- and high-input, respectively. Similarly, the soybean yield gap was 2.5 and 0.8 Mg ha−1. Simulation results indicated that the high-input management system had greater yield stability across all weather years. In warm-dry years, yield gaps were larger for both crops and water scenarios. Irrigation reduced yield variation in maize more than in soybean, relative to the rainfed scenario. Besides irrigation, model analysis indicated that N fertilization for maize and narrow rows for soybean were the main factors contributing to yield gains. This study provides a systems level yield gap assessment of maize and soybean cropping system in Western US Corn Belt that can initiate dialogue (both experimental and modeling activities) on finding and applying best management systems to close current yield gaps.