Search Results (181)

Plant height is a key agronomic trait that influences plant architecture and mechanical harvesting suitability in cotton; however, the molecular mechanisms underlying its dynamic development remain unclear. In this study, two recombinant inbred line (RIL) populations sharing CCRI127 as a common paternal parent (RIL-GH07, n = 150; RIL-2358B, n = 276) were developed. Based on stable plant-height performance across multiple environments, tall and short extreme lines were selected from the two RIL populations for transcriptome sequencing. By integrating differential expression analysis with weighted gene co-expression network analysis (WGCNA), we identified hub genes associated with cotton plant height development, characterized the molecular features and core pathways governing dynamic stem elongation at different growth stages, thereby providing insights into the transcriptional regulation of plant height development in cotton. The two RIL populations showed broadly similar plant-height growth patterns, with slow elongation at 15 DOS, rapid elongation during 30–60 DOS, and reduced growth after 70 DOS. Transcriptome differential expression analysis identified 15,052 non-redundant DEGs, which exhibited clear population- and stage-specific expression patterns. In the GH07 population, the largest number of DEGs was detected at 15 DOS (7193), whereas in the 2358B population relatively large numbers of DEGs were maintained at both 30 DOS (3839) and 70 DOS (3118). Analysis of DEGs shared by the two populations across four developmental stages showed that, in addition to genes with consistent expression trends, each stage also contained a substantial number of DEGs with opposite expression directions. WGCNA identified 25 gene expression modules, among which the green and yellow modules were significantly positively correlated with plant height. Functional enrichment analysis indicated that genes in these two modules were mainly enriched in hormone regulation and signal transduction, protein modification and degradation, and intracellular transport. Seven hub genes were identified by integrating intramodular connectivity and kME values. Functional prediction suggested that these genes may play important roles in cotton plant height development. This study provides genetic resources and a theoretical basis for subsequent functional validation of cotton plant height-related genes and the improvement of plant architecture in cotton. Full article

The concepts of anthromes and human appropriation of net primary production (HANPP) are both valuable in understanding our human-dominated planet, yet they have never been integrated theoretically or empirically. Here we utilize an extensive county-level dataset on HANPP and its product-level components to derive, through cluster analysis, ten contemporary US anthromes. From highest to lowest density of harvested HANPP, the anthromes are: rainfed corn–soy, dairy fodder, spring wheat–small grain, dryland winter wheat, subtropical soy–cotton, commercial timber, mixed hardwood and pasture, recovered eastern forest, prairie–sagebrush rangeland, and arid and alpine sparse grazing. Expanding to thirteen anthromes maintains these, while bifurcating the commercial timber (softwood, hardwood), rainfed corn–soy (core, fringe) and mixed hardwood and pasture anthromes. Trend analysis shows the expansion of the high-HANPP rainfed corn–soy and the low-HANPP recovered eastern forest anthromes between 2002 and 2017, while some other anthromes with moderate HANNP_harvest are contracting. The methods described here can be applied to any country where data on HANPP can be obtained. Full article

Regarding the current issues in Xinjiang, China, during the harvesting of cotton stalks, the lack of specialized, efficient, and durable cutting blades for cotton stalks causes uneven cutting, high power consumption, and short blade life. In this study, a biomimetic serrated blade was designed based on the Trictenotomidae mandible for efficient, low-power-consumption cutting. The biomimetic design, FEM-SPH coupled simulation, bench test, combined with response surface methodology, and field test were used. The simulation results showed that under the same working conditions, the maximum shear stress was 34.81% lower than that for the ordinary blade and 22.05% lower than that for the ordinary serrated blade. And the bench test results showed that cutting power consumption was reduced by about 20.12% and 15.69% compared to the ordinary cutting blade and serrated cutting blade, respectively. When cutting velocity was 1.3 m/s, cutting inclination angle was 11°, and ratio of cutting velocity and feeding velocity was 1.1, the biomimetic serrated cutting blade could achieve effective cutting of cotton stalks and obtain better quality of cutting—the cutting power per unit area and the cutting-edge angle after cutting cotton stalks were 52.08 kJ/m² and 6°, respectively. The research results can provide a theoretical basis and support for the utilization of cotton stalks out of the field, as well as the cutting of other similar crop stalks. Full article

Effective crop monitoring during monsoon growing seasons in Central India faces challenges from persistent cloud cover that limits optical remote sensing during critical agricultural periods. This study presents the first attempt to develop a novel set of SAR-derived phenological metrics organized into five thematic categories for monsoon crop discrimination in smallholder agricultural systems. Five major monsoon crops (cotton, rice, maize, soybean, and urad) were analyzed across five different agroclimatic zones in Central India using Sentinel-1 data for the 2021 growing season. Phenological features were extracted from VV, VH polarizations, and their ratio, including seasonal extrema, threshold crossings, duration measures, curve shape descriptors, and area under the curve. Distinct crop-specific signatures were observed, with cotton showing extended phenology and cereal–legume crops displaying compressed, overlapping growth patterns. VV polarization achieved the highest statistical discrimination for intensity-based metrics, with 75% thresholds (VV_HP75V: F = 1287) providing higher separability than other thresholds by capturing near-peak biomass differences. VH performed best for duration and integration-based metrics, while VH/VV provided limited additional separability across metric types. For area-under-the-curve metrics, AUC25 outperformed AUC50 and AUC75 by capturing cumulative backscatter across the broader growing season while remaining robust to soil- and residue-dominated backscatter variability at sowing and harvest. Multiclass classification achieved 48.3% overall accuracy with systematic cereal–legume confusion, reflecting fundamental phenological convergence among monsoon-aligned crops. Cotton achieved the highest performance (F1: 0.79), with VH polarization dominating feature importance (65% of top 20 features). Binary classification revealed crop-specific discrimination patterns: cotton was best separated using VV intensity metrics, maize using the VH/VV ratio, and rice using timing-based features. Cross-district transferability showed the highest mean overall accuracy for rice (74%) and cotton (72%), while the remaining crops showed lower accuracy due to their phenological similarity. These findings highlight both the potential and limitations of SAR phenological metrics for monsoon crop discrimination, with effective results for structurally distinct crops but persistent cereal–legume confusion, requiring further investigation with multi-sensor approaches. Full article

This article analyzes the scale, fluctuations and geographical distribution of olive (Olea europaea) cultivation in Palestine over 550 years, from the Late Mamluk period (1300–1517), through the Ottoman era (1517–1917), until the end of the British Mandate in 1947. Although olive oil played a dominant role in the diet and the local economy, there is currently no research that measures and quantifies the number of olive trees or the number of villages and towns that cultivated olive trees and produced olive oil. We reconstruct the agricultural landscape with its vast olive groves and examine the cultural history of olive tree farming, the growth of the olive oil industries and their economic role and importance. The earliest figures we have, that are from the year 1596, show that 400 villages cultivated 1,400,794 olive trees. By 1943, there were 6,053,367 olive trees that were cultivated by 644 villages. We found a strong correlation (R² = 0.96, p < 0.01) between the number of olive trees and the number of villages, indicating that olive oil demand and the olive oil industry align with population size. The research data derives from a variety of medieval local chroniclers, as well as diaries by European, North African and Middle Eastern travelers who provide descriptions of olive groves and the olive oil industry. Among the most important sources are the 1596 Ottoman tax registers. The tax registers are the first document that present clear-cut figures on the numbers of olive trees, olive presses and the names of the villages that cultivated olive groves. The main sources for the last period dealt with in this study are the British Mandate maps (1943), which display the acreage of the different crops across Palestine. The data from the maps is supplemented by two modern works on olive cultivation written by agronomists Assaf Goor (b. 1894) and Ali Nasouh (b. 1906) who were born in Palestine and employed by the British department of agriculture. The analysis of data shows that demands of local and oversea markets; the olive oil soap industry, which was based on the local olive oil; as well as competing agricultural crops like sugarcane, cotton and citrus, contributed to a complex economic structure. Olive tree cultivation did not depend on government investment. Olive groves in Palestine were rain fed, and, except for the harvest, they required relatively few working days a year. Hence, moderate policies (low taxation during periods of drought and low yields) adopted by enterprising local rulers and the central British government created a unique and relatively balanced relationship between rulers and farmers, which encouraged olive cultivation and led to a constant increase in the number of olive trees and the development of the olive oil industry. Full article

Cotton planting efficiency is increasingly constrained by narrow planting windows, motivating interest in higher operating speeds if stand establishment and seed placement accuracy can be maintained. Field experiments were conducted in Georgia between 2020 and 2025 to quantify the effects of planter operating parameters and system configurations on cotton planter performance. Trials evaluated combinations of planting speed, row-unit downforce, seed plate type (singulated vs. hill-drop), and seed delivery system using conventional gravity-tube planters and two high-speed planter systems equipped with advanced delivery systems. The achieved population was determined from stand counts, planting quality was assessed using plant position classification relative to theoretical plant spacing, and lint yield was measured at harvest. Across site-years, the achieved population was generally not affected by planting speed or downforce within the tested ranges. With conventional gravity-tube delivery systems, the proportion of perfectly spaced plants declined from 44.0% to 22.1% in 2020 and from 52.8% to 28.4% in 2021 as planting speed increased from 5 to 11 km ${\mathrm{h}}^{-1}$. In contrast, across the advanced planter systems evaluated in 2025, mean perfect spacing remained within a narrow range of 45.8% to 49.5% across 8 to 14 km ${\mathrm{h}}^{-1}$. Hill-drop seed plates increased the achieved population relative to singulated plates in the seed plate × downforce trials, increasing mean achieved population from 79.6 to 87.8 thousand plants h${\mathrm{a}}^{-1}$ at Midville and from 62.2 to 73.1 thousand plants h${\mathrm{a}}^{-1}$ at Plains in 2022, and from 45.4 to 58.1 thousand plants h${\mathrm{a}}^{-1}$ at Midville in 2024, but these increases did not result in consistent lint yield differences. The high-speed hill-drop configuration evaluated in 2025 did not consistently produce plant pairs meeting the hill-drop spacing criterion. These results indicate that current high-speed planter systems can be used for singulated cotton to increase planting productivity while maintaining placement accuracy, although additional research is needed to determine the environmental and management conditions under which spacing improvements translate into yield benefits. Full article

Secondary salinization in mulched drip-irrigated cotton fields of arid oasis–desert transition zones in Xinjiang imposes coupled root-zone constraints, including salt-induced aggregate structural degradation and ionic stress. However, field evidence remains limited on whether integrating a structure-oriented soil conditioner with humic acid can generate stable improvements across growing seasons. A two-year field experiment with a randomized block design (three replicates) was conducted to evaluate four treatments: control (CK), polyacrylamide (PAM, 30 kg ha⁻¹), humic acid (HA, 450 kg ha⁻¹), and PAM + HA. Soil physical and chemical properties and aggregate-size distribution were determined after harvest, while enzyme activities and root traits were assessed at the flowering–boll stage. Structural equation modeling (SEM) and random forest (RF) analysis were used to explore soil–root–yield linkages and identify key soil predictors associated with yield variation. Treatment effects were most evident in the 0–20 cm layer, with PAM + HA showing the greatest overall improvement. In the topsoil, PAM + HA lowered soil pH from 8.35 to 7.88 in 2024 (p < 0.05), increased soil organic carbon (SOC) to 4.29 g kg⁻¹ in 2025 (p < 0.01), and increased NO₃⁻–N to 25.51 and 30.27 mg kg⁻¹ in 2024 and 2025, respectively (both p < 0.05). PAM + HA also enhanced cellulase activity from 6.17 to 16.85 mg glucose g⁻¹ 72 h⁻¹ in 2024 and increased seed cotton yield to 6683.69 and 5996.89 kg ha⁻¹ in 2024 and 2025, with a 51.0% yield increase over CK in 2024. SEM showed that root development had the strongest direct positive effect on yield (β = 0.79, R² = 0.63; goodness of fit (GOF) = 0.74), while random forest identified alkaline phosphatase, cellulase, and NO₃⁻–N as the main yield predictors (out-of-bag R² (OOB R²) = 0.672, p = 0.01). This study elucidated the effects of the combined application of a structure-oriented soil conditioner and humic acid on the root-zone environment of mulched drip-irrigated cotton fields in arid regions, providing a theoretical basis for the coordinated regulation of soil structural improvement and nutrient activation in saline–sodic cotton fields. Full article

Accurate cotton boll identification and boll number estimation from UAV imagery are essential for large-scale yield prediction and precision management, yet severe leaf occlusion and complex canopy backgrounds often hinder robust performance. Here, UAV RGB images were acquired 3 days before defoliant application and at 3, 6, 9, 12, 15, and 18 days after defoliation. Cotton bolls were extracted using Mahalanobis distance, a support vector machine, and a neural network. Boll number was then estimated using an improved random forest model with multi-feature fusion. Across all defoliation stages, the NN produced the most accurate and stable boll extraction, achieving a maximum Kappa of 0.914, an overall accuracy of 95.77%, and an F1 score of 0.96. Extraction accuracy increased rapidly from 3 to 9 days after application and stabilized from 12 to 18 days. For boll number estimation, fusing the boll pixel ratio with color indices and texture features improved accuracy and consistency over time; the best performance was obtained at 18 days after application (R² = 0.7264; rRMSE = 4.9%). Overall, imagery acquired 15–18 days after defoliation provided the most reliable estimation window, supporting operational pre-harvest assessment and harvest-timing decisions. Full article

Emerging pharmaceutical pollutants such as acetaminophen (ACE) pose health and environmental risks. Solar photocatalysis provides a sustainable and efficient treatment option. In this study, UiO-66-NH₂ metal–organic framework was immobilized on cotton fabrics to enable their application in both batch and continuous flow systems. Cotton, a biodegradable and low-cost support, was first functionalized by two strategies: hydroxylation (-OH) and carboxylation (-COOH), to promote MOF anchoring. Cotton fabric functionalization and MOF growth were confirmed by ATR and X-ray diffraction, while SEM and EDX analyses revealed that carboxylated fibers achieved higher MOF loading. Photocatalytic experiments under simulated solar irradiation demonstrated significantly higher degradation of acetaminophen when the carboxylated cotton fabric-based catalyst (F-COOH-UiO-66-NH₂) was used. Mott–Schottky analysis and band alignment revealed that, under the applied reaction conditions, hydroxyl radical generation was not favored due to the position of the valence band. Studies with scavengers identified the superoxide radical as the dominant oxidative agent responsible for the photodegradation process. In particular, the F-COOH-UiO-66-NH₂ system demonstrated its suitability for application in continuous flow systems, achieving acetaminophen conversion of up to 50% under simulated solar irradiation. This confirms its potential for scalable application in practical water treatment technologies. These results reinforce the feasibility of immobilizing MOF-based photocatalysts on functionalized textile waste, offering a dual-purpose solution that combines the removal of pharmaceutical pollutants with the valorization of waste materials. The synergistic integration of high photocatalytic efficiency, sunlight harvesting and recyclability of the materials underlines the eco-friendly and cost-effective nature of the proposed strategy. Full article

The picking-head drivetrain is a critical transmission component of cotton harvesters, and its fault condition monitoring and diagnosis are essential for ensuring stable and reliable operation of the equipment. In practical engineering applications, diagnostic models for picking-head drivetrains are typically initialized using data collected under a limited number of representative operating conditions. Although sufficient fault samples can often be obtained during the initial training stage, the coverage of operating conditions is inherently restricted. As the model is deployed and used in the field, fault samples collected under new operating conditions are gradually acquired in a stage-wise manner. How to stably update the diagnostic model while the operating-condition coverage continuously expands, and how to avoid performance degradation and catastrophic forgetting, remain critical challenges. To address these issues, this paper proposes a continual learning method, termed DP-CL (Disentangled Prototype-Driven Continual Learning), for fault diagnosis of cotton harvester picking-head drivetrains under gradually expanding operating conditions. The proposed method is built upon an explicit disentanglement of condition-invariant features and condition-specific features. Within a unified framework, three types of structured prototypes, including class prototypes, condition prototypes, and condition-aware class prototypes, are constructed to form a multi-level representation hierarchy. A prototype-driven structured update mechanism is then employed to impose stable constraints on fault-discriminative semantics across different operating conditions. In addition, an operating-condition similarity measurement based on condition-specific features is introduced, based on which a proportion-adaptive sample selection strategy is designed. This strategy enables controlled knowledge transfer and preservation of discriminative structures during multi-stage model updates. Experimental results obtained under a laboratory-constructed cumulative operating-condition expansion scenario demonstrate that the proposed method achieves superior performance in terms of overall performance retention, cross-stage stability, and resistance to performance degradation. Moreover, as the number of operating conditions increases, the proposed method maintains a relatively smooth performance variation trend, while preserving clear class structures and a controllable level of confusion. These results validate the effectiveness of the proposed approach for stable fault diagnosis under expanding operating-condition coverage. Full article

Timely monitoring of cotton defoliation progress is crucial for optimizing the quality of mechanical harvesting. To accurately assess the defoliation status prior to mechanical picking, a field experiment was conducted in Hejian, Hebei Province, China, in 2022. Using a DJI P4M multispectral drone, canopy images of cotton were collected before and after defoliation at three flight altitudes: 25 m, 50 m, and 100 m. The study employed machine learning algorithms including linear regression, Support Vector Machine (SVM), Generalized Additive Model (GAM), and Random Forest (RF) to invert the Leaf Area Index (LAI). Additionally, SVM-based supervised classification was introduced to eliminate background interference from soil and open cotton bolls, while the XGBoost model and SHAP method were used to analyze the main factors influencing LAI inversion. Key findings include the following: The univariate linear relationship between EVI and LAI proved to be the most robust, with the model constructed from 100 m flight altitude data performing best (validation set: R² = 0.921, RMSE = 0.284). The rate of LAI change showed a strong positive correlation with field-measured defoliation rate (r = 0.83–0.88), confirming its reliability as a proxy indicator for defoliation progress. Soil and open cotton bolls were identified as major negative factors affecting LAI inversion accuracy. The optimal machine learning prediction model varied with days after spraying, demonstrating significant temporal variability. This study demonstrates that high-throughput LAI inversion based on drone-derived multispectral EVI enables precise and dynamic monitoring of cotton defoliation. The approach provides farmers and field managers with an efficient, non-destructive monitoring tool. By delivering real-time insight into defoliation progress, it plays a pivotal role in enabling precision defoliation management, reducing excessive chemical use, optimizing the scheduling of mechanical operations, and ultimately enhancing both the sustainability and profitability of cotton production. Full article

The optimal timing of chemical defoliation is a critical bottleneck in stabilizing yield and fiber quality for short-season cotton, particularly under the intensifying pressure of mechanized global production. Current practices rely heavily on population-level boll opening rates, often overlooking the physiological maturity of late-season bolls. Here, we investigate the trade-offs between late-boll development and defoliation-induced senescence in short-season summer cotton. Our results demonstrate that defoliation timing based on a specific heat-unit or temporal threshold after flowering—rather than simple visual indicators—is essential for maximizing biological potential. We identified a critical physiological window (43 days post-anthesis) that synergistically optimizes boll weight, seed cotton yield, and fiber micronaire. Beyond this window, delayed defoliation leads to excessive fiber coarsening and reduced spinnability, while earlier application terminates dry matter accumulation prematurely, incurring significant yield penalties. These findings provide a mechanistic basis for synchronizing reproductive maturation with mechanical harvesting requirements. By establishing a precision defoliation framework, this study offers a scalable strategy to enhance the economic sustainability and resource-use efficiency of short-season cotton systems in double-cropping regions globally. Full article

Optimizing planting density and mepiquat chloride (MC) is essential for simplified, machine-harvestable cotton production in the Yangtze River Basin. A two-year field experiment was conducted to explore the synergistic regulatory mechanisms of MC and planting density on plant architecture, physiology, and yield in short-season direct-seeding cotton. A split-plot design was employed with varying gradients of MC dosage and planting density. The results indicate that density and MC function complementarily in shaping plant architecture: MC primarily controls vertical growth (“dwarfing”), while density elevates the initial fruiting node (“elevation”), with no antagonistic interaction between the two. Regarding canopy structure, increasing density is the primary driver for improving the leaf area index (LAI), while MC optimizes light distribution during the critical boll stage. In terms of yield formation, high density significantly enhances seed cotton yield by increasing the number of bolls per unit area, which effectively overcompensates for the reduction in bolls per plant. Notably, a dose-dependent synergistic effect was observed where high MC dosage maximized the yield potential of high-density populations. Furthermore, fiber quality remained stable across treatments, driven primarily by interannual climate factors rather than agronomic regulation. Consequently, an independent synergistic optimization strategy is recommended, combining high density to secure population yield with medium-to-high MC dosage to shape an ideal machine-harvestable architecture. This approach provides a theoretical basis and technical pathway for high-yield and efficient cotton cultivation in the region. Full article

At present, conventional cold storage facilities in China are poorly suited to on-farm storage demands for agricultural produce, mainly due to their large spatial requirements, complex and labor-intensive installation procedures, limited portability, and insufficient coverage in rural areas. These limitations significantly contribute to post-harvest losses of perishable crops such as cherry tomatoes. To address this challenge, the present study proposes a compact and temporary cold storage system—gas-inflated membrane cold storage (GIMCS)—which exploits the inherent safety, cost-effectiveness, ease of deployment, and adaptability of inflatable membrane structures. A series of mechanical performance tests, including tensile strength, pressure resistance, and burst tests, were conducted on PA/PE (Polyamide/Polyethylene) composite membranes. The optimal configuration was identified as a membrane thickness of 70 μm, a gas column width of 2 cm, and a PA/PE composition ratio of 35%/65%. Thermal performance evaluations further revealed that filling the inflatable structure with 100% CO₂ yielded the most effective insulation. Through structural optimization, a cotton-filled gas-inflated membrane cold storage system (CF-GIMCS) incorporating a dual insulation strategy—combining intra-membrane and extra-membrane insulation—was developed. This multilayer configuration significantly reduced conductive and convective heat transfer, resulting in enhanced thermal performance. A comparative evaluation between GIMCS and a conventional cold storage system of equivalent capacity was conducted over a 15-day storage period, considering construction cost, temperature uniformity, and fruit preservation quality. The results showed that the construction cost of GIMCS was only 38% of that of conventional cold storage. The internal temperature distribution of GIMCS was highly uniform, with a maximum horizontal temperature difference of 1.4 °C, demonstrating thermal stability comparable to conventional systems. No statistically significant differences were observed between the two systems in key post-harvest quality indicators, including weight loss and respiration rate. Notably, GIMCS exhibited superior performance in maintaining fruit firmness, with a hardness of 1.30 kg·cm⁻² compared to 1.26 kg·cm⁻² in conventional storage, indicating a potential advantage in shelf-life extension. Overall, these findings demonstrate that GIMCS represents an affordable, technically robust, and portable cold storage solution capable of delivering preservation performance comparable to—or exceeding—that of conventional cold storage. Its modularity, mobility, and ease of relocation make it particularly well suited to the operational and economic constraints of smallholder farming systems, offering a practical and scalable pathway for improving on-farm cold chain infrastructure. Full article

The cotton boll opening rate (BOR) is an important indicator for evaluating the physiological maturation process of cotton and the critical stage of yield formation, and it provides essential guidance for subsequent defoliant application and mechanical harvesting. The investigation of cotton BOR usually relies on manual field surveys, which are time-consuming and destructive, making it difficult to achieve large-scale and efficient monitoring. UAV remote sensing technology has been widely used in crop growth monitoring due to its operational flexibility and high image resolution. However, because of the dense growth of the cotton canopy in UAV remote sensing imagery, the boll opening condition in the lower parts of the canopy cannot be completely observed. In contrast, UAV imagery can effectively monitor cotton leaf chlorophyll content (SPAD) and leaf area index (LAI), both of which undergo continuous changes during the boll opening process. Therefore, this study proposes using SPAD and LAI retrieved from UAV multispectral imagery as physiological intermediary variables to construct an empirical statistical equation and compare it with end-to-end machine learning baselines. Multispectral and ground synchronous data (n = 360) were collected in Baibi Town, Anyang, Henan Province, across four dates (8/28, 9/6, 9/13, 9/24). Twenty-eight commonly used vegetation indices were calculated from multispectral imagery, and Pearson’s correlation analysis was conducted to select indices sensitive to cotton SPAD, LAI, and BOR. Prediction models were constructed using the Random Forest (RF), Gradient Boosting Decision Tree (GBDT), Support Vector Machine (SVM), and Partial Least Squares (PLS) models. The results showed that GBDT achieved the best prediction performance for SPAD (R² = 0.86, RMSE = 1.19), while SVM performed best for LAI (R² = 0.77, RMSE = 0.38). The quadratic polynomial equation constructed using SPAD and LAI achieved R² = 0.807 and RMSE = 0.109 in BOR testing, which was significantly better than the baseline model using vegetation indices to directly regress BOR. The method demonstrated stable performance in spatial mapping of BOR during the boll opening period and showed promising potential for guiding defoliant application and harvest timing. Full article