Predict Crop Response Research Articles

Potassium limits productivity in intensive cereal cropping systems in Southeast Asia.

Potassium (K) has received little attention as a potential yield-limiting factor in cropping systems. Here we investigated the K status in intensive cereal cropping systems in Indonesia, which are representative of many other Southeast Asian countries. Our analysis included nutrient input-output balance, leaf nutrient status, long- and short-term fertilizer trials, and farmer surveys. We revealed that soil K levels alone are insufficient to meet plant requirements, and current fertilizer applications are inadequate to prevent K deficiencies and large negative annual soil K balances in farmer fields (average -62 kgK ha-1). On-farm fertilizer trials indicated that nearly 80% of rice crops and 70% of maize crops achieved higher yields with the application of K fertilizer. Addressing K limitations will require an enhanced capacity to predict crop responses to K fertilizer, together with long-term, flexible fertilizer and crop residue management strategies. Furthermore, similar K limitations have probably emerged in other regions globally due to intensive cropping with insufficient K replenishment, which must be addressed to close yield gaps on existing farmland.

Integrating physiological and multi-omics methods to elucidate heat stress tolerance for sustainable rice production.

Heat stress presents unique challenges compared to other environmental stressors, as predicting crop responses and understanding the mechanisms for heat tolerance are complex tasks. The escalating impact of devastating climate changes heightens the frequency and intensity of heat stresses, posing a noteworthy threat to global agricultural productivity, especially in rice-dependent regions of the developing world. Humidity has been demonstrated to negatively affect rice yields worldwide. Plants have evolved intricate biochemical adaptations, involving intricate interactions among genes, proteins, and metabolites, to counter diverse external signals and ensure their survival. Modern-omics technologies, encompassing transcriptomics, metabolomics, and proteomics, have revolutionized our comprehension of the intricate biochemical and cellular shifts that occur in stressed agricultural plants. Integrating these multi-omics approaches offers a comprehensive view of cellular responses to heat stress and other challenges, surpassing the insights gained from multi-omics analyses. This integration becomes vital in developing heat-tolerant crop varieties, which is crucial in the face of increasingly unpredictable weather patterns. To expedite the development of heat-resistant rice varieties, aiming at sustainability in terms of food production and food security globally, this review consolidates the latest peer-reviewed research highlighting the application of multi-omics strategies.

Using Soil P Tests and P-Sorption Index to Predict P Requirement for Cassava Grown in Tropical Upland Sandy Soils

ABSTRACT The phosphorus (P) sorption index and efficiency of P extracting methods were investigated to quantify the available P content for accurate recommendations regarding cassava P fertilizer application in the sandy soils of Thailand. Phosphorus sorption isotherms were examined to predict the response of soils to the P fertilizers applied. Six rates of P fertilizers (0, 12.5, 25, 37.5, 50 and 62.5 kg P2O5 ha−1) were applied in a randomized complete block design at five field sites in major cassava-growing areas. The efficiency among the Bray II, CaCl2 and Mehlich III extracts was also tested by comparing their capacity to predict crop response. The P adsorption capacities of all soils were in the range 39.4–106 mg kg−1; therefore, there were classified as low P-sorbed soils. Phosphorus fertilizer produced significantly higher aboveground biomass than did zero-P fertilization. The critical P level adequate for 95% of the maximum yield was in the range 0.14–0.26 mg P L−1. The equation for calculating P fertilizer using soil P extracted based on the Bray II, CaCl2 and Mehlich III methods had accuracy levels of 73, 65 and 72%, respectively. Bray II was slightly superior to Mehlich III for use in soil-P availability indices, both of which were recommended for P fertilizer assessment.

Soil Phosphorus Sources and Their Relative Water Solubility and Extractability

ABSTRACT Soil testing is a tool used to predict crop response to residual soil phosphorus (P). It is commonly perceived that high soil test P indicates high potential for off-site movement and impact on surface water quality. However, P source potentially confounds the relation between soil test P and soil P solubility. This study evaluated how soil test P (Mehlich-1 and Mehlich-3) and total P (TP) related to water-soluble P (WSP) for manure-(n = 120) and fertilizer-amended (n = 120) soils, and soils forming in phosphate-rich parent material (phosphatic soils; n = 60). Results document marked differences in slopes of WSP as a function of soil test P and TP for the three P sources. Manure-amended soils showed the highest regression coefficients (R 2 = 0.89, 0.89 and 0.92) and slopes for WSP but phosphatic soils, in contrast, showed no tendency for increased WSP with increasing soil test P or TP despite having the highest range of TP content. Fertilizer-amended soils had lowest values for all P measures. Manure-applied P accumulated to much higher levels in these sandy soils than P applied as inorganic P suggesting the latter is either quickly recovered by crops or lost. Phosphate minerals in naturally phosphatic soils do not likely constitute the same extractable P pool as for agriculturally amended soils. Hence, soil test extractions may not be well-calibrated for phosphatic soils. Standard soil test P values may not be a reliable indicator of high P leaching loss potential for naturally phosphatic soils, to the extent that WSP is related to leaching risk.

Phenomics data processing: A plot-level model for repeated measurements to extract the timing of key stages and quantities at defined time points

• General plot-level model for repeated high-throughput field phenotyping measurements. • Extraction of three main intermediate trait categories for dynamic modelling. • Seamless processing approach that integrates temporal and spatial modelling. • Phenomics data processing cheatsheet. Decision-making in breeding increasingly depends on the ability to capture and predict crop responses to changing environmental factors. Advances in crop modeling as well as high-throughput field phenotyping (HTFP) hold promise to provide such insights. Processing HTFP data is an interdisciplinary task that requires broad knowledge on experimental design, measurement techniques, feature extraction, dynamic trait modeling, and prediction of genotypic values using statistical models. To get an overview of sources of variation in HTFP, we develop a general plot-level model for repeated measurements. Based on this model, we propose a seamless step-wise procedure that allows for carry on of estimated means and variances from stage to stage. The process builds on the extraction of three intermediate trait categories; (1) timing of key stages, (2) quantities at defined time points or periods, and (3) dose-response curves. In a first stage, these intermediate traits are extracted from low-level traits’ time series (e.g., canopy height) using P-splines and the quarter of maximum elongation rate method (QMER), as well as final height percentiles. In a second and third stage, extracted traits are further processed using a stage-wise linear mixed model analysis. Using a wheat canopy growth simulation to generate canopy height time series, we demonstrate the suitability of the stage-wise process for traits of the first two above-mentioned categories. Results indicate that, for the first stage, the P-spline/QMER method was more robust than the percentile method. In the subsequent two-stage linear mixed model processing, weighting the second and third stage with error variance estimates from the previous stages improved the root mean squared error. We conclude that processing phenomics data in stages represents a feasible approach if estimated means and variances are carried forward from one processing stage to the next. P-splines in combination with the QMER method are suitable tools to extract timing of key stages and quantities at defined time points from HTFP data.

Effects of elevated air CO2 concentrations on the carbon and nitrogen contents of rice and winter wheat

Investigating the effects of different air CO2 concentrations on the carbon (C) and nitrogen (N) contents of straw and roots of different generations of crops is crucial to predict crop responses to elevated CO2. We performed a field experiment with open-top chambers (OTCs) and applied consecutively elevating CO2 concentrations (120 ppm and 160 ppm during the investigated 2018 rice and 2018–2019 winter wheat seasons, respectively, with a consecutive increase of 40 ppm per year beginning from the first winter wheat–rice rotation season in November 2015) and a continuously elevated CO2 concentration of 200 ppm during each season. The results indicated that elevated CO2 concentrations generally increased the root C content of both rice and winter wheat. The most obvious effects between the treatments appeared in the crops grown from seeds harvested in the OTCs each season. An elevated CO2 concentration increased the rice root N content, with more obvious effects over multiple generations that had been planted in the OTCs for three or four growing seasons than on a single generation that was planted in the OTCs for only one growing season. An elevated CO2 concentration also induced an increase in the ratio of C to N (C/N) in the roots of rice and in the straw and roots of winter wheat. The C content of the rice and winter wheat roots increased significantly with increasing root N content. The enhancement effect of an elevated CO2 concentration on the C and N content and the C/N ratios in rice and winter wheat is more significant for multiple generations than for one generation.

Predicting the response of soil potassium to broccoli (Brassica oleracea var. italica) in a Gangetic Inceptisol of West Bengal, India through suitable chemical extractants

Finding a suitable method for estimating soil and crop specific potassium (K) bioavailability under variable range of soil fertility has been a mighty challenge. To undermine this, a field experiment was conducted under varying levels of fertilizer application on pre-established fertility gradient strips in the Indo-Gangetic plains of West Bengal, India for predicting crop response and to assess the K availability in winter growing broccoli by employing six acclaimed chemical extractants viz. ammonium bicarbonate-diethylenetriaminepentaacetic acid (AB-DTPA), calcium chloride (CaCl2), Kelowna, Mehlich-III, ammonium acetate (NH4OAc), and sodium tetraphenylborate (NaTPB4). The availability study of K followed the pattern as, CaCl2 < AB-DTPA < Kelowna < NH4OAc < Mehlich-III < sodium tetraphenylborate (NaTPB4). A higher extractability of K was envisaged under the conjoint application of higher doses of K fertilizers along with the organics. Among the extractants, NaTPB4 showed the highest correlation sum (3.98) and also yielded a significant relationship with plant tissue K concentration (0.858) and uptake (0.715). The NaTPB4, Mehlich-III, and NH4OAc extractants were found to be significantly related to most of plant indices. The Euclidean distance based cluster analysis and linear regression study further proved the relatively higher efficiency of NaTPB4 extractant followed by Mehlich-III and NH4OAc to estimate plant K response in broccoli under the Gangetic alluvial soils of West Bengal, India.

Crop Management in Controlled Environment Agriculture (CEA) Systems Using Predictive Mathematical Models.

Proximal sensors in controlled environment agriculture (CEA) are used to monitor plant growth, yield, and water consumption with non-destructive technologies. Rapid and continuous monitoring of environmental and crop parameters may be used to develop mathematical models to predict crop response to microclimatic changes. Here, we applied the energy cascade model (MEC) on green- and red-leaf butterhead lettuce (Lactuca sativa L. var. capitata). We tooled up the model to describe the changing leaf functional efficiency during the growing period. We validated the model on an independent dataset with two different vapor pressure deficit (VPD) levels, corresponding to nominal (low VPD) and off-nominal (high VPD) conditions. Under low VPD, the modified model accurately predicted the transpiration rate (RMSE = 0.10 Lm−2), edible biomass (RMSE = 6.87 g m−2), net-photosynthesis (rBIAS = 34%), and stomatal conductance (rBIAS = 39%). Under high VPD, the model overestimated photosynthesis and stomatal conductance (rBIAS = 76–68%). This inconsistency is likely due to the empirical nature of the original model, which was designed for nominal conditions. Here, applications of the modified model are discussed, and possible improvements are suggested based on plant morpho-physiological changes occurring in sub-optimal scenarios.

Detection of major weather patterns reduces number of simulations in climate impact studies

AbstractWith climate change posing a serious threat to food security, there has been an increased interest in simulating its impact on cropping systems. Crop models are useful tools to evaluate strategies for adaptation to future climate; however, the simulation process may be infeasible when dealing with a large number of G × E × M combinations. We proposed that the number of simulations could significantly be reduced by clustering weather data and detecting major weather patterns. Using 5, 10 and 15 clusters (i.e., years representative of each weather pattern), we simulated phenology, cumulative transpiration, heat‐shock‐induced yield loss (heat loss) and grain yield of four Australian cultivars across the Australian wheatbelt over a 30‐year period under current and future climates. A strong correlation (r2≈1) between the proposed method and the benchmark (i.e., simulation of all 30 years without clustering) for phenology suggested that average duration of crop growth phases could be predicted with substantially fewer simulations as accurately as when simulating all 30 seasons. With mean absolute error of 0.64 days for phenology when only five clusters were identified, this method had a deviation considerably lower than the reported deviations of calibrated crop models. Although the proposed method showed higher deviations for traits highly sensitive to temporal climatic variability such as cumulative transpiration, heat loss and grain yield when five clusters were used, significantly strong correlations were achieved when 10 or 15 clusters were identified. Furthermore, this method was highly accurate in reproducing site‐level impact of climate change. Less than 7% of site × general circulation model (GCM) combinations (zero for phenology) showed incorrect predication of the direction (+/−) of climate change impact when only five clusters were identified while the accuracy further increased at the regional level and with more clusters. The proposed method proved promising in predicting selected traits of wheat crops and can reduce number of simulations required to predict crop responses to climate/management scenarios in model‐aided ideotyping and climate impact studies.

Soil Carbon Stock under Different Agro-climatic Zones and its Economic Valuation for Productivity Enhancement of Pigeon Pea (Cajanus cajan) in Southern Deccan Plateau

Soil Carbon (SC) storage is a key indicator of soil function used to monitor and predict crop response to management decisions. Under the SUJALA-3 Watershed Development Project, a total of 202 soil series representing major pigeon pea-growing soils were studied in seven agro-climatic zones of Karnataka. The organic and inorganic carbon stocks status were estimated at different soil depths to arrive at the economic value and assessed its relation with crop production. The SOC and SIC stock was 23.8 and 11.2 t ha-1 at 0-30 cm, 42.0 and 32.1 t ha-1 at 30-100 cm and 66.9 and 42.8 t ha-1 at 0-100 cm depth, respectively. The yield of pigeon pea was high (12.18 q ha-1) under high OC status of clay textured soils followed by medium SOC (11.11q ha-1) and low OC soils (11.52 q ha-1) in NETZ region. Using the agglomeration method, five groups were derived to determine the soil organic carbon management clusters. The soil organic carbon deficit (t ha-1) calculated to arrive investment requirement showed the order of cluster-4&gt;cluster-3&gt;cluster-1&gt;cluster-5&gt;cluster-2 and the investment required to bridge the gap of Rs. 30228, 26508, 26190, 22674 and 21990, respectively. As soil structure stability has an important role in carbon sequestration, the polynomial equation was constructed between soil carbon density and soil structural stability index, which yielded a threshold soil carbon stock value of 66.6 t ha-1 to attain a stable soil structure index value of nine. The ANOVA analysis showed statistically significant differences in SOC stock between regions (p = 0.022) and soil subgroups (p = 0.001). The study concludes an average investment of ₹ 25,000 ha-1yr-1 for 20 yr is required for enhancing the soil carbon status in Karnataka and to achieve the Pigeon pea potential yield of 20 q ha-1

Evaluation of Soil Tests for Measuring Potentially Mineralizable Soil N in Southern Idaho Soils

Core Ideas A reliable rapid soil test method to predict N mineralization is currently unavailable. Chemical and biological soil tests were evaluated against N mineralized by a 7‐d anaerobic incubation. Multiple tests were strongly correlated to the 7‐d anaerobic incubation (e.g., ASN, LOI, and FL_CO2_C). Developing rapid soil tests to predict N availability could improve management strategies for irrigated crops and would assist in sustainable production. No rapid soil test has been evaluated for applicability in irrigated systems in the semiarid western United States. Soils were collected at two sampling depths (0–30 and 30–60 cm) from irrigated fields across southern Idaho representing predominant soil classes, crops, and irrigation management systems; a 0–60 cm depth was calculated. Anaerobic incubations were used to estimate N mineralization (NMIN) in the sampled soils. The tests were positively correlated with NMIN: the amino sugar N (ASN) test was strongly correlated at 0 to 30 cm (r = 0.73) and 0 to 60 cm (r = 0.79). The flush of CO2 from soil after capillary rewetting (FL_CO2_C) was also strongly correlated at the 0‐ to 30‐cm depth (r = 0.73) and 0‐ to 60‐cm depth (r = 0.67). The loss on ignition (LOI) test was strongly correlated for the 0‐ to 30‐ (r = 0.65) and 0‐ to 60‐cm (r = 0.67) depths. Removal of sites with high soil organic matter resulted in improved r2 for LOI and ASN but not FL_CO2_C. The relationship of several tests with NMIN warrants further investigation of their ability to predict crop responses in the region. The ASN test uses a standard 2 M KCl extract, can be automated, and is easily implemented with only minor changes. These results should be expanded to field correlation calibration trials before any test is recommended.

Refining the Canegro model for improved simulation of climate change impacts on sugarcane

Abstract Crop models can be used for predicting climate change impacts and exploring adaptation strategies, but their suitability for such tasks needs to be assessed. Although the DSSAT-Canegro model has been used widely for climate impact studies, some shortcomings have been revealed. The objectives were to improve and evaluate the capability of DSSAT-Canegro to predict crop responses to climate change. Model changes included improved simulation of elevated temperature and atmospheric CO2 concentration ([CO2]) impacts, and revised algorithms for tillering, respiration and crop water relations. After calibration, the refined model was tested against an independent set of experimental data, demonstrating acceptable simulation accuracy for aerial dry mass, stalk dry mass and stalk sucrose mass (RMSE = 8.4, 5.2 and 3.3 t/ha respectively). A multiple-site sensitivity analysis revealed that simulated responses by the refined model, of canopy formation, crop water use, crop water status and stalk dry mass to changes in rainfall, temperature and [CO2], were more realistic than those of the old model. Highest average simulated stalk mass was achieved at a temperature regime that was 3 °C warmer than current climate, with yield increases ranging from 0.7% (irrigated Ligne Paradis, Reunion Island) to 7% (rainfed Piracicaba, Brazil). Elevated [CO2] increased yields for rainfed production only (7% for La Mercy, South Africa and 6% for Piracicaba, [CO2] = 750 ppm), through reduced transpiration and improved crop water status. The study highlighted the need for improvements in simulating reduced growth of older crops, and [CO2] effects on transpiration. This study has delivered an improved Canegro model that represents plant processes and their interactions with climatic drivers more realistically, and can predict crop growth, water use and yields, for a wide range of climates, reasonably accurately. We propose that this revised Canegro model is included in a forthcoming release of the DSSAT Cropping System Model, for use in climate change impact studies.

Bridging the gap between climate science and farmers in Colombia

Agriculture is highly sensitive to variations in both weather and climate. Farmers face uncertainty in the weather patterns over the short term, and climate over the longer term. The CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS) has promoted a system of Local Technical Agro-Climatic Committees (LTACs)4In spanish known as Mesas Técnicas Agroclimáticas.4 in two Colombian regions to explore means of creating dialogue between researchers and farmers that would provide farmers with options in the face of both short- and longer-term variations in climate. The article uses a case study approach to describe how the original LTACs were established, the benefits obtained from the LTAC system, and the expansion of the system to areas outside Colombia.The basic premise behind the LTAC approach is: If farmers and the local rural community at large can access and understand weather and climate forecasts and the responses of their crop production, processing, and marketing options under local conditions, they can make better decisions on how to manage their farms and businesses.There are six basic components that are required to implement the LTAC approach to bridging the gap between climate science and farmers: (i) Establishment of the LTAC with alignment of local parties interested in managing variation in the climate and definition of their roles; (ii) local climate and monthly climate forecast; (iii) crop modeling and understanding of climate variation on crop production, processing and marketing and the impact this will have on management; (iv) dialogue between scientists, experts, and farmers; (v) dissemination and socialization of the dialogue; and (vi) local capacity building, which cuts across all the other five components.The regular monthly meetings of the LTACs are the focal point of the overall process, bringing together information from various sources, organizing the ideas and thoughts, and then disseminating the information. A feature of the committees was their diversity. The committees required specific inputs, particularly on climate and the crop response before each meeting. Research organizations provided climate forecasts and crop response data. The forecasts proved closer to reality than long term means and the crops models that were used to predict crop response to changes in management and climate variation were relatively simple and unsophisticated. The committees gained confidence in these prognoses, which then formed the basis for dialogue on how best to manage climate variation. The production-side participants appreciated the opportunity to present their own points of view and the move from top-down recommendations, coming from the researchers and extension agents, towards a menu of options which they discussed. At the same time, it was evident in the early meetings that many of the participants came ill-prepared to grasp concepts related to managing climatic variation, thus highlighting the need for capacity building within the LTACs themselves.LTACs actively promulgated their findings through bulletins, social networks, extension services, farmers’ organizations radio, TV, and the press. In the dry El Niño year of 2015, many farmers used the information generated by the LTACs to better manage their crops, increasing yields, and reducing losses.More LTACs are now being established in Colombia, Honduras, Guatemala and Nicaragua. These countries have understood the value of creating mechanisms through which researches and farmers can exchange ideas, with the farmers choosing options to improve their management based on better weather and climate forecasts and an understanding of how the weather and climate affects their crops.

A comparison of soil tests for available phosphorus in long‐term field experiments in Europe

SummaryMost soil tests for available phosphorus (P) perform rather poorly in predicting crop response. This study was set up to compare different established soil tests in their capacity to predict crop response across contrasting types of soil. Soil samples from long‐term field experiments, the oldest &gt;100 years old, were collected in five European countries. The total number of soil samples (n = 218), which differed in cropping and P treatment, and originated from 11 different soil types, were analysed with five tests: ammonium oxalate (Ox), ammonium lactate (AL), Olsen P, 0.01 m CaCl2 and the diffusive gradient in thin film (DGT). The first three tests denote available P quantity (Q), whereas the last two indicate P intensity (I) of the soil solution. All five tests were positively related to the crop yield data (n = 317). The Q‐tests generally outperformed I‐tests when evaluated with goodness of fit in Mitscherlich models, but critical P values of the I‐tests varied the least among different types of soil. No test was clearly superior to the others, except for the oxalate extraction, which was generally poor. The combination of Q‐ and I‐tests performed slightly better for predicting crop yield than any single soil P test. This Q + I analysis explains why recent successes with I‐tests (e.g. DGT) were found for soils with larger P sorption than for those in the present study. This systematic evaluation of soil tests using a unique compilation of established field trials provides critical soil P values that are valid across Europe.Highlights We compared soil P tests for predicting crop response across contrasting soil types. No test was clearly superior to the others except for the oxalate extraction, which was generally poor. This study suggests that intensity tests do not perform markedly better than quantity tests. The evaluation of soil P tests on this unique dataset provided critical soil P values across Europe.

Accurate crop yield predictions from modelling tree-crop interactions in gliricidia-maize agroforestry

Agroforestry systems, containing mixtures of trees and crops, are often promoted because the net effect of interactions between woody and herbaceous components is thought to be positive if evaluated over the long term. From a modelling perspective, agroforestry has received much less attention than monocultures. However, for the potential of agroforestry to impact food security in Africa to be fully evaluated, models are required that accurately predict crop yields in the presence of trees. The positive effects of the fertiliser tree gliricidia (Gliricidia sepium) on maize (Zea mays) are well documented and use of this tree-crop combination to increase crop production is expanding in several African countries. Simulation of gliricidia-maize interactions can complement field trials by predicting crop response across a broader range of contexts than can be achieved by experimentation alone. We tested a model developed within the APSIM framework. APSIM models are widely used for one dimensional (1D), process-based simulation of crops such as maize and wheat in monoculture. The Next Generation version of APSIM was used here to test a 2D agroforestry model where maize growth and yield varied spatially in response to interactions with gliricidia. The simulations were done using data for gliricidia-maize interactions over two years (short-term) in Kenya and 11years (long-term) in Malawi, with differing proportions of trees and crops and contrasting management. Predictions were compared with observations for maize grain yield, and soil water content. Simulations in Kenya were in agreement with observed yields reflecting lower observed maize germination in rows close to gliricidia. Soil water content was also adequately simulated, except for a tendency for slower simulated drying of the soil profile each season. Simulated maize yields in Malawi were also in agreement with observations. Trends in soil carbon over a decade were similar to those measured, but could not be statistically evaluated. These results show that the agroforestry model in APSIM Next Generation adequately represented tree-crop interactions in these two contrasting agro-ecological conditions and agroforestry practices. Further testing of the model is warranted to explore tree-crop interactions under a wider range of environmental conditions.

Adapting and evaluating the CROPGRO-peanut model for response to phosphorus on a sandy-loam soil under semi-arid tropical conditions

Phosphorus (P) deficiency is a major constraint to crop production in many agricultural systems globally. Application of phosphorus fertilizer is essential for optimal crop yields when soils are P limiting. Crop simulation models can provide an alternative, less time consuming and inexpensive means of determining the optimum crop P requirements under varied soil and climatic conditions. The CROPGRO-peanut model is capable of simulating the growth and yield of peanut in response to weather, soil, water, nitrogen and management practices but its capability in predicting crop responses to soil and fertilizer P needs to be established. Our objective was to adapt and evaluate the CROPGRO-peanut model within the DSSAT system to simulate the growth and yield of peanut (Arachis hypogaea) in response to soil and fertilizer P. Data from four P fertilizer treatments (0, 13, 26 and 39kgPha−1) and two cropping seasons (2002 and 2003) experiments on an Alfisol were used to calibrate the model. The model was tested using two data sets from a P fertilizer×cultivar trial conducted on-station in 1997 and 1998 and P fertilizer×fungicide trials on-farm in 2002. The limited testing showed that the P module accurately simulated the seasonal patterns of aboveground biomass and pod yield in the on-farm trials. Averaged across sampling dates RMSEs in the on-farm trials ranged from 100 to 398kgha−1 (d≥0.99) for total biomass and from 97 to 263kgha−1 (d values≥0.98) for pod yield. At final harvest, the variability of simulated biomass and pod yield was about 5.4 and 10.0% of the observed biomass and pod yield respectively. In the P fertilizer×cultivar trial, the variability of simulated biomass and pod yield were 22 and 13% for cv. F-Mix and 19 and 5% for cv. Chinese respectively. The model simulated the seasonal patterns of vegetative P content in the four farmers’ fields fairly well with RMSEs ranging from 0.28 to 1.29kgha−1 when averaged across measurements dates. The model outputs were sensitive to soil P test values, the method of P fertilizer application and to plant P uptake factors, such as root P extraction radius and root length density. There were differences in the sensitivity of the simulations to changes in target tissue P concentrations. Increasing or decreasing the optimum P concentration of seed and minimum P concentrations of leaf and stem had the greatest effects on pod yield compared to other plant parts. We conclude that the generic soil and plant P model in DSSAT 4.5 is capable of simulating peanut growth and yield in response to soil P levels or fertilizer application on an Alfisol.

Turfgrass Growth and Color Correlated to Spring Illinois Soil Nitrogen Test and Soil Permanganate‐Oxidizable Carbon Concentrations

ABSTRACTThe Illinois soil nitrogen test (ISNT) and soil permanganate‐oxidizable C (POXC) concentrations have been used to estimate mineralization potential of agricultural soils, assess soil quality, distinguish differences between crop management treatments, and predict crop response to N fertilization. However, it is not known if these measures are correlated to growth and color quality responses of cool‐season turfgrasses. This study was conducted across 5 yr (2008–2012) in Connecticut to determine if a single spring measurement of Illinois soil nitrogen test‐N (ISNT‐N) and POXC concentrations could be used to estimate color and growth responses of Kentucky bluegrass (Poa pratensis L.) and tall fescue (Festuca arundinacea Schreb.) lawns. Randomized complete block field experiments were set out on the two species with varying rates of an organic fertilizer. Soil samples were collected in early May of each year and analyzed for concentrations of ISNT‐N and POXC. Turfgrass color, clippings yield, clippings total N concentration, and clippings total N uptake were measured from May through October. Turfgrass responses showed consistent positive linear responses (p &lt; 0.05) as a function of ISNT‐N and POXC. Across species and years, ISNT‐N and POXC were generally greater under tall fescue than under Kentucky bluegrass. A single spring measurement of soil ISNT‐N and POXC shows promise in categorizing Kentucky bluegrass and tall fescues lawns as to their likelihood of N fertilization response. The data suggest a low probability of meaningful Kentucky bluegrass and tall fescues lawn responses to N fertilization when spring ISNT‐N and POXC concentrations exceed 250 mg kg−1 and 1300 mg kg−1, respectively.

Testing phosphorus availability for maize with DGT in weathered soils amended with organic materials

Aims and background The phosphorus (P) fertilizer value of organic materials (OM) such as plant residues or manure can be larger than that of mineral fertilizers in weathered soils. Recently, the diffusive gradients in thin films (DGT) technique was shown to better predict crop response to applied mineral P fertilizer among soils than established tests. The goal of this study is to evaluate the potential of the DGT technique to predict plant-available P in different soils amended with various qualities of OM.

Optimization of the diffusive gradients in thin films (DGT) method for simultaneous assay of potassium and plant-available phosphorus in soils

Potassium (K) and phosphorus (P) are two important macronutrients for crops, and are usually applied to soils as granular fertilizer before seeding. Therefore, accurate soil tests prior to planting to predict crop response to fertilizers are important in optimizing crop yields. Traditional methods used for testing both available K and P in soils, which are based on chemical extraction procedures, are to be soil-type dependent, and the predictive relationships across a broad range of soils are generally poor. The diffusive gradients in thin films (DGT) technique, based on diffusion theory, is extensively used to measure the diffusive supply of trace elements, metals and some nutrients in soils and water. When DGT is used to assess plant-available P in soils, a good relationship is found between crop response to P fertilizer and concentrations of P in soil measured by DGT, and therefore the DGT method provides a more precise recommendation of P fertilizer requirements. Adaptation of the DGT method to measure plant-available K in soils has already been attempted [1], but limitations were reported due to the non-uniform size of the resin gel, decreased K binding rate of the gel at long deployment times and a limited ability to measure a wide range of K concentrations. To eliminate these problems, a new resin gel has been developed by combining Amberlite and ferrihydrite. This mixed Amberlite and ferrihydrite (MAF) gel has improved properties in terms of handling and even distribution of Amberlite in the gel. The elution efficiencies of the MAF gel for K and P were 90% and 96%, respectively. The diffusion coefficient of K through the diffusive gel was 1.30×10−5cm2s−1 at 22±1°C and was stable through time. Since ferrihydrite is already used in DGT P testing, the ability of the MAF gel to assess available P simultaneously was also assessed. The MAF gel performed the same as the traditional ferrihydrite gel for available P assessment in a wide variety of agricultural soils. This means that the newly developed gel has the potential to measure K and plant-available P in soils simultaneously.

Can a Labile Carbon Test be Used to Predict Crop Responses to Improve Soil Organic Matter Management?

Permanganate (KMnO4) oxidizable C (POXC), an estimate of labile soil C, was evaluated for use as a soil test to identify soils that may respond positively to soil organic matter (SOM) management. We hypothesized that soils lower in POXC would be more likely than soils higher in POXC to show increased crop productivity in response to practices that increase SOM. At four sites, paired fields of the same soil but contrasting management history (cropping vs. sod) were studied. Fields with sod history tested higher in total organic C (TOC) and POXC than fields with cropped history. Permanganate‐oxidizable C was strongly related to TOC (r = 0.94). We examined crop stover, grain, and biomass responses to two cover crop treatments within each field: winter rye (Secale cereale L.) or no rye. After at least 1 yr of treatment, there was a significant negative correlation between relative stover response to rye and POXC (r = –0.60) at sites with finer textured soils. After at least 2 yr of treatment, crop responses to rye showed a significant negative correlation with POXC and TOC. The strongest relationships to POXC occurred in the stover response at two sites with finer textured soils (Keedysville: r = –0.74; Holtwood: r = –0.84). Permanganate‐oxidizable C was comparable to TOC at predicting crop responses to rye. These results suggest that POXC may be a useful test for identifying soils where improved SOM management is likely to improve productivity. The rapid, simple POXC methodology enables on‐site or laboratory soil testing.