Agronomy Journal最新文献

Crop models are essential tools to understand risks, vulnerabilities, and uncertainties in agricultural systems. Advancements in digital data acquisition and accessibility of crop and land area data with larger spatial coverage and higher spatial resolutions necessitate corresponding developments in flexible multi-scale crop modeling application framework to facilitate decision-making and policy formulation from sub-national to global levels. While a few tools and approaches exist for spatial applications of crop models, their lesser flexibility owing to dependency on external programs, portability issues, complexity in files setup, and limited functionality thwarts users to employ crop models at larger scales. This paper aims to introduce Pythia, a novel gridded modeling framework for Decision Support System for Agrotechnology Transfer (DSSAT)-cropping system model (CSM), and demonstrate its application. The objectives are to explain Pythia design, execution workflow, and to show its main functionalities. Inputs to the Pythia framework include (i) point vector files that specify the sites of weather data and simulation points, (ii) a raster map of soil-profile identity numbers, (iii) a raster map of crop area (iv) a DSSAT FileX template, and (v) a configuration file to provide references to the required model input files and databases, and to set up the dynamic portions of the FileX template. A case study from maize cropping system in Ghana is used to demonstrate the applications of Pythia. Flexibilities in spatial coverage and parameterizing model inputs in Pythia provide DSSAT-CSM users a useful tool to run spatial simulations in local machines and in high-performance computing environment.

The accurate estimation of soybean (Glycine max) stand establishment is essential for evaluating crop emergence and informing early-season management practices. Recent advances in unmanned aerial vehicle (UAV) imagery and computer vision offer opportunities to automate plant population assessments; however, limited information exists on their accuracy in soybeans. This study evaluated two commercial UAV-based plant counting platforms, a point-based (convolutional neural network–derived) and a line-based (Hough transform–derived) approach across two growing seasons, three flight altitudes (15.2, 45.7, and 91.4 m), and seven plant removal treatments, including a control (no removal). UAV imagery was collected at 7- to 10-day intervals from 10 to 36 days after planting (DAP), and predictions were compared to manual on-ground counts. The point-based method provided the highest accuracy (within ±12% of ground-truth; R² = 0.81) when imagery was collected between 14 and 20 DAP at 15.2-m altitude. Accuracy declined beyond 27 DAP as canopy overlap increased. The line-based method remained more stable across altitudes and later growth stages but consistently overestimated plant counts, particularly in dense and narrow row canopies. Incorporating on-ground calibration areas improved accuracy by an average of 28% and up to 48% for the line-based approach in narrow rows. Row spacing and plant removal patterns had minimal effects on prediction error, although short repeating gaps were poorly detected by the line-based method. Overall, UAV-based plant counts in soybean are feasible and dependable when flights are timed during early vegetative growth and supported by calibration, providing a practical tool for in-season management and field-based crop monitoring.

Efficient nitrogen (N) fertilizer management is essential for advancing maize production, particularly in sandy soils of subtropical regions like Suwannee Valley, Florida, where NO₃─N leaching poses environmental risks. A 30-year simulation using a calibrated and evaluated Crop Environment Resource Synthesis-Maize model assessed the effects of N sources, rates, application timing, planting dates, and water regimes on grain yield (GY) and NO₃─N leaching. Results showed that moderate to high N rates (202–404 kg ha⁻¹) achieved the highest yields but increased NO₃─N leaching by 33%–38% compared to the recommended N rate (RNR) (269 kg N ha⁻¹). Early planting (March 1–22), split N applications, and precise irrigation (80%–90% of maximum available water [MAW]) improved yield up to 25% and reduced NO₃─N leaching up to 30%. Under rainfed conditions, a 50% reduction in the RNR led to a negligible change in yield but decreased NO₃─N leaching by 35%, while it reduced yield by 15%–30% under irrigated conditions. Optimal practices, including splitting N into five times, irrigation at 90% of MAW, and March 8 planting, achieved the highest yields (10,182–11,925 kg [dry matter] ha⁻¹) and the lowest NO₃─N leaching (60–63 kg ha⁻¹). Combined analysis revealed that the interaction between N rate and water management was significant for yield, N uptake, and NO₃─N leaching (p < 0.01). Irrigation at 90% of MAW with 100% of the RNR maximized yield (12,422 kg ha⁻¹) and N uptake (340 kg ha⁻¹), and reasonably lower NO₃─N leaching (40 kg ha⁻¹). These findings highlight the critical role of integrated management practices to improve GY while minimizing environmental impacts.

On-farm experimentation (OFE) data, when aggregated across years and locations, is rich with information that enhances agricultural science and informs more specific management recommendations. Standardizing collection of metadata within an OFE network augments the traditionally measured data (e.g., population, yield) to enable broader comparisons that may be impractical at the field level. This paper evaluates the utility of aggregated OFE data in informing agronomic recommendations, investigates potential state and regional agronomic analyses, and illustrates how integrating OFE data with complementary data sets (e.g., historical weather conditions) can help identify potential contributions to agronomic variability across the region of study and elucidate additional research inquiry. Seven years of OFE-derived data and associated metadata were aggregated to explore the analytical power of this type of integrative approach. The exploration of this dataset identified planting date and relative maturity as the strongest predictors of yield variability for corn and soybeans. Regional differences in corn yield response to fungicide application were also observed. This paper demonstrates how incorporating spatially and temporally complete historical weather data at sub-seasonal levels with OFE data enables additional analysis that could lead to a better understanding of the environmental factors that influence crop performance. Standardizing metadata collection and aggregating data from OFE offer significant opportunities for advancing agronomic learning. These examples highlight the potential of integrating data collected across a diverse range of environmental conditions and management practices to provide a more comprehensive understanding of the interactions among these variables and their effects on crop yield and profitability.

Climate change is increasing heat stress in many olive-growing regions, raising concerns about its effects on oil quality and cultivar performance. This study investigated the impact of high temperatures on the quality and chemical composition of olive oil from three irrigated Olea europaea L. cultivars, Arbosana, Chemlali, and Koroneiki, grown under arid climate conditions in southern Tunisia (Sfax, Mahres). The research aimed to clarify how heat stress influences key oil quality parameters and whether cultivars exhibit distinct adaptive responses. Over 3 years, fruit weight, oil content, and oil quality were analyzed. Results differed across cultivars. Chemlali showed the highest carotenoid concentrations (14.54 mg kg⁻¹) and Koroneiki showed the highest chlorophyll level (5.03 mg kg⁻¹). Significant differences in total phenol content were recorded, with Koroneiki reaching 940.14 mg kg⁻¹, followed by Chemlali (409.41 mg kg⁻¹) and Arbosana (320.66 mg kg⁻¹). In terms of fatty acids, oleic acid remained dominant in all cultivars (>55%), reaching 74% in Koroneiki. Koroneiki also had the lowest palmitic acid (12.59%) and linoleic acid (8.40%). All cultivars preserved stable oil quality under high temperatures and met extra virgin standards. Notably, heat stress increased oil concentration at harvest, exceeding 21% in all cultivars. These findings showed that high temperatures influenced several oil composition traits, and they do not necessarily reduce overall oil quality.

How to ensure that “the availability of soil water does not limit plant growth or transpiration?” Determining the so-called crop coefficient (K_c) relies on this assumption of optimal or non-limiting soil water conditions, which, in turn, is understood as the soil water content at field capacity, a parameter with, de facto, no relation to plants or transpiration. Many studies on crop evapotranspiration ignore the dynamic nature of plant available water (PAW) driven by the soil hydraulic properties (water retention and hydraulic conductivity) and the characteristics of the plant root system. This raises important questions: How can we guarantee that a K_c value is transferable if soil and plant hydraulics are not explicitly considered? And how can we reconcile the practical determination of crop evapotranspiration with the theoretical one using complex water transfer models? While addressing these complexities remains a challenge, recent advances in process-based modeling offer new opportunities to represent soil–plant–atmosphere interactions more accurately, and to refine the criteria underlying K_c and PAW. Integrating such physically based understanding with established approaches may help bridge the gap between theoretical knowledge and practical applications, ultimately supporting more reliable and adaptable methods for estimating crop evapotranspiration.

The study provides robust, multi-dimensional evidence on the drivers of Kharchia wheat (Triticum aestivum) conservation in the arid saline agroecosystems of Rajasthan. The objective is to understand how sociocultural, economic, and ecological factors influence farmers’ conservation behavior and highlight the role of traditional landrace conservation as a climate-resilient strategy for sustaining agriculture and livelihoods in an arid salt-affected soil in India. The findings showed that sociocultural enablers, especially enduring food traditions, a strong sense of local identity, and intergenerational seed-exchange networks, serve as the most significant predictors shaping farmers’ intentions to conserve this landrace. Drawing on a mixed-method approach in the Pali district of Rajasthan, India, the research integrates household surveys, focus group discussions, transect walks, and controlled field observations. Structural equation modeling was employed to analyze the ecological, economic, and sociocultural drivers shaping conservation behavior. Results reveal that farmers value Kharchia wheat not only for its adaptability to salinity and heat stress but also for its sociocultural significance and its economic role in ensuring stable yields and fodder supply. Education, access to extension, and local market demand emerged as critical enablers of conservation, while climate stress and price volatility posed key challenges. The study pinpoints the critical role of community-driven landrace conservation for sustaining agrobiodiversity, adaptive potential, and long-term rural resilience.

Phosphorus (P) is essential for maize (Zea mays L.) growth but is a limited and non-renewable resource. Studies using DSSAT CSM-CERES-Maize remain scarce for low-P soils. This study assessed maize yield response to P fertilization in P-deficient soils using an adapted version of the DSSAT CSM-CERES-Maize model with minor code modifications to vegetative partitioning. The CERES-Maize was calibrated and evaluated using data from two field experiments (2020–2023) conducted in Southern Germany on P-deficient soils, with P fertilizer treatments of 0 and 85 kg P ha⁻¹ in 2020 and 2021, and 0, 25, 50, and 75 kg P ha⁻¹ in 2022 and 2023. Evaluation results showed a good agreement between simulated and measured data for aboveground biomass (normalized root mean square error [nRMSE] = 24.4%, d-stat = 0.98) and grain yield (nRMSE = 11.4%, d-stat = 0.99). While overall leaf area index simulations were satisfactory across treatments (d-stat = 0.87), accuracy was lower in low or zero P fertilizer treatments, with a d-stat of 0.72 for the zero P treatment compared to 0.97 for the 85 kg P ha⁻¹ treatment in 2020 and 2021. CERES-Maize can be used to support P management decisions, but it showed a limited ability to simulate leaf/P dynamics under very low P levels in this study. These findings confirm the capability of CERES-Maize to simulate maize growth and yield responses to varying P fertilization. Future modeling research should further investigate performance under low P fertilizer levels in greater detail across diverse environments to enhance and validate the DSSAT P subroutine.

Past efforts have concentrated on top-down approaches in delivering precision nutrient management (PNM) practices to smallholder farmers, but with little yield and economic impact for farmers. Farmer-centric on-farm experimentation (OFE) is an approach designed to effectively engage farmers in generating technologies tailored to the agroecological, socioeconomic, and cultural complexities of smallholder farming systems. This study, implemented in East and West Africa, used a mixed methodology approach, including a survey and agronomic measurements, to gain insights into farmer learning and nutrient management decision-making, and the outcomes from their participation in OFE processes. For the agronomic assessment, a simple experimental design was employed from 2021 to 2024, whereby smallholder farm-scale fields (0.5–1 ha) were divided into two experimental plots to compare optimized treatment (OT) and a farmer practice (FP). Considerable co-learning between farmers and other stakeholders was observed, particularly in Kenya and Tanzania. There was a positive trend in maize yields over time in the FP treatment, attributed to the involvement of farmers in OFE. Yield improvements of up to 53% were achieved, although profitability was observed only in Côte d'Ivoire. While the scientist-led OT in most cases outperformed the FP, farmers continuously adopted improved nutrient management practices. This highlights the critical role of OFE in scaling PNM by providing a platform to integrate scientific and endogenous knowledge, entrench learning, and scale it by linking to wider innovation systems. OFE also offers precise and relevant data for farmer decision-making on nutrient management drawn from co-designed trials and co-developed agronomic knowledge.

Interest in liquid phosphorus (P) and potassium (K) fertilizers is increasing, often with claims of superior performance over dry-granular sources. We compared yield and tissue-nutrient responses of corn (Zea mays L.) and soybean [Glycine max (L.) Merr.] to dry versus liquid P and K. Field trials were conducted from 2021 to 2024 across 42 site-years, including eight corn and six soybean sites, with separate P-only, K-only, and combined P+K trials for each crop. Each trial included a no-fertilizer check and factorial combinations of source (dry vs. liquid) and rate (half vs. full). Triple superphosphate and muriate of potash were dry-fertilizer sources, and ammonium polyphosphate and Nachurs K-fuel were liquid-fertilizer sources. Leaf nutrients were analyzed at V11–V12 (corn) and R2–R3 (soybean) stages, and yield was measured at maturity. In P- or K-deficient soils, corn yield did not differ by sources; however, full-rates increased yield by 5%–13% over half-rates. For soybean, source and rate effects were not significant in single-nutrient trials, whereas in P+K trials, full-rates increased yield by 9%–10% over half-rates. Across environments, yield responses aligned with tissue diagnostics; tissue-K was consistently associated with yield gains, whereas tissue-P was less predictive under common luxury P uptake. Overall, liquid sources provided no advantage over dry-granular sources at equivalent rates. Half-rate liquid did not match full-rate dry yields, and co-applied P+K mirrored single-nutrient responses; yield differences reflected rate, not formulation or synergy. Results emphasize using soil-test-based rates, not formulation, when targeting yield, and tissue-K as a more reliable in-season indicator of crop response than tissue-P.