Economics of Variable Rate Lime in Indiana

In Indiana, variable rate application (VRA) of lime is often considered a good place to start site-specific management (SSM). This is because soil pH is one of the most variable of manageable soil characteristics in the state, the availability of essential nutrients is closely related to soil pH, and because spreaders can be retrofitted relatively inexpensively to do VRA. The objective of this study is to evaluate the profitability of VRA for lime as a stand-alone activity. The methodology involves a spreadsheet model using corn and soybean pH response functions estimated with small plot data. The overall results indicate increased annual returns to corn and soybean production with site-specific pH management strategies. On average, SSM with agronomic recommendations provides an increased annual return of $7.24 per hectare (ha) (+1.78%). SSM with the economic decision rule provides an average increase in annual return of $19.55 ha^–1 (+4.82%). Information strategy, which uses site-specific information to determine the economically optimal uniform rate of lime, provides an average increase in annual return of $14.38 ha^–1 (+3.54%).     

Long-Term Variable Rate Lime and Phosphorus Application for Piedmont No-Till Field Crops

Variable rate (VR) fertilizer application is a paradigm with potential to improve input efficiency and farm profitability. It is widely marketed by commercial applicators in the southeastern US. However, field studies comparing VR with traditional management have not demonstrated consistent, positive results. The objectives of this study were: (1) to determine the soil impact, crop response and economic potential of VR phosphorus (P) and lime application in a North Carolina Piedmont no-till field crop system using intensive soybean [Glycine max (L.) Merr.] production and (2) to economically evaluate alternatives to standard commercial grid soil sampling for directing VR P and lime. A 23-ha long-term no-till field in the SE Piedmont was divided into 0.4ha plots assigned to either VR or uniform P and lime application. Grid soil sampling and VR P and lime application were done prior to four crops over 3 years: full season soybean, wheat (Triticum aestivum L.)–double cropped soybean, and full season soybean. Soil test P, pH and crop yield response to VR P were inconsistent. Soil pH in areas with low pH initially did increase in response to VR lime, but it took two to three applications to bring all of these areas to the target pH. Once VR-liming raised initially low soil pH to levels close to target, yield of soybean, but not wheat, were up to 0.74Mgha^–1 higher than with uniform lime. Even with significantly higher soybean yields associated with VR lime, 3 years of grid sampling and VR application were not profitable compared to uniform application. The results indicated that VR lime could be profitable if the initial grid sampling data were used either for 2 consecutive years, or if it was used to restrict future grid sampling to specific areas requiring further VR lime.     

Evaluation of an on-the-go technology for soil pH mapping

Since conventional sampling and laboratory soil analysis do not provide a cost effective capability for obtaining geo-referenced measurements with adequate frequency, different on-the-go sensing techniques have been attempted. One such recently commercialized sensing system combines mapping of soil electrical conductivity and pH. The concept of direct measurement of soil pH has allowed for a substantial increase in measurement density. In this publication, soil pH maps, developed using on-the-go technology and obtained for eight production fields in six US states, were compared with corresponding maps derived from grid sampling. It was shown that with certain field conditions, on-the-go mapping can significantly increase the accuracy of soil pH maps and therefore increase the potential profitability of variable rate liming. However, in many instances, these on-the-go measurements need to be calibrated to account for a field-specific bias. After calibration, the overall error estimate for soil pH maps produced using on-the-go measurements was less than 0.3 pH, while non-calibrated on-the-go and conventional field average and grid-sampling maps produced errors greater than 0.4 pH.     

酸性土壤施用石灰提高作物产量的整合分析

【目的】施用石灰是改良土壤酸度获得作物增产的传统而有效的方法之一,整合分析石灰增加作物产量的效应和原因,对科学合理施用石灰维持作物高产提供指导。【方法】收集已公开发表有关石灰改良酸性土壤的文献数据,建立土壤p H和作物产量/生物量数据库。分析土壤初始p H(3.1—6.6)、作物类型(粮食作物、经济作物)、石灰施入量(750、750—1 500、1 500—3 000、3 000—6 000、6 000 kg·hm-2)和石灰类型(生石灰、熟石灰、石灰石粉)下,作物的增产率。【结果】与不施石灰相比,酸性土壤上施用石灰可提高作物产量,增产幅度为2%—255%,粮食类作物和经济类作物增产率分别为42%和47%,其中粮食类作物增产率大小排序:玉米(149%)高粱(142%)麦类(55%)豆类(32%)水稻(4%)薯类(2%),经济类作物增产率排序:蔬菜(255%)牧草(89%)油菜(26%)水果(23%)烟草(7%)。施用石灰作物增产率随土壤初始p H的升高呈先升高后降低趋势:当p H为4.3时,增产效果最好,达99%;p H 5.8以上出现减产。在常见土壤酸性范围(p H 4.5—5.5),石灰用量以3 000—6 000 kg·hm-2最佳,增产率达55%—173%。熟石灰的增产效果(100%)要优于生石灰(32%)和石灰石粉(64%)。施用石灰提高土壤p H和交换性钙含量、降低交换性铝含量,是作物增产的主要原因,且当交换性钙为6.2 cmol·kg~(-1)时增产率最大,也证实改良土壤酸度时需要中等石灰用量。【结论】酸性土壤添加石灰对蔬菜和玉米的增产效果最好,并优先选用熟石灰。石灰用量以3 000—6 000 kg·hm-2为宜,在p H大于5.8时不宜施用,即酸性土壤改良目标值为p H 5.8。     

石灰用量对酸性土壤酸度及大麦幼苗生长的影响

【目的】研究生石灰用量对酸性土壤(pH3.9)降酸效果和大麦幼苗生长的影响,以期明确适宜的生石灰用量,为酸性土壤改良及生石灰的合理施用提供科学依据。【方法】试验于华中农业大学盆栽场进行,采用土壤培养和盆栽试验方法,依据Ca(OH)2滴定法计算出石灰需要量,设置不施生石灰和生石灰用量0.3、0.9、1.8、2.4和4.8 g·kg~(-1)等6个处理,分别于培养后10、20、30、40、50、60、70和90 d取样8次,监测土壤p H、土壤交换性酸总量、土壤交换性H+含量和土壤交换性铝含量动态变化;于培养后第90天播种大麦,2周后进行观测,研究不同生石灰用量改良后的土壤对大麦幼苗生物量、根系形态指标及根系活力的影响。【结果】土壤培养试验结果表明,生石灰施入初期(前30 d)可明显提高土壤p H,降低土壤交换性酸总量和土壤交换性铝含量。石灰用量越高,潜在酸的含量越低,以至于石灰用量4.8 g·kg~(-1)处理的土壤交换性酸总量、土壤交换性H+和交换性铝含量均降为零。但受土壤缓冲性能的影响,其降酸效果随着培养时间的延长而降低,到培养第90天,低石灰用量(1.8 g·kg~(-1))对于提高土壤p H已没有明显效果,而对降低土壤潜在酸效果显著。盆栽试验结果表明,施用生石灰可以显著提高大麦株高和生物量,促进大麦根系生长发育。在生石灰用量1.8 g kg~(-1)的范围内,大麦幼苗株高、生物量、根系总根长、总表面积和根系活力均随生石灰用量的增加而提高,大麦幼苗根系平均直径随生石灰用量的增加而降低;用量超过1.8 g·kg~(-1)后,生石灰对大麦幼苗生长的促进作用明显减弱。尤其是当生石灰用量为4.8g·kg~(-1)时,大麦根系活力显著低于石灰用量0.9 g·kg~(-1)处理,过量生石灰的施用,抑制了根系的生长。这表明生石灰用量1.8 g·kg~(-1)的改良效果最佳,与采用Ca(OH)_2滴定法计算出的石灰需要量1.76 g·kg~(-1)相吻合。【结论】在酸性土壤上施用生石灰能明显中和土壤酸性,显著促进大麦幼苗生长。在本试验条件下,酸性土壤(p H3.9)最佳生石灰施用量为1.8 g·kg~(-1)(相当于4 t·hm~(-2)生石灰用量),与采用Ca(OH)_2滴定法计算出的石灰需要量一致,证实该方法确定的石灰用量是适宜的。     

The profitability of variable rate lime in wheat

Grid sampling allows a variable rate of lime to be applied and has been marketed as a cost saver to producers. However, there is little research that shows if this precision application is profitable or not. Previous research on variable-rate lime has considered only a small number of fields. This paper uses soil sampling data from 111 fields provided by producers in Oklahoma and Kansas. The 5-year average net present values are compared between variable-rate and uniform-rate lime for grain-only wheat production, dual-purpose wheat grain and forage production, and a wheat–soybean rotation. Sensitivity analysis was done for varying grain prices as well as grain yield potential. When using historical average yields and recent prices for Oklahoma, variable rate was not profitable on average for these 111 fields for either a grain-only, dual-purpose, or wheat–soybean production. However, when yield or prices were above average, variable rate was profitable. Thus, variable rate liming can be profitable for these fields, but it requires either above average yields, a high value crop, or above average prices.

     

Spatial variability of soil properties and yield of a grazed alfalfa pasture in Brazil

Knowledge of the spatial variability of soil properties and of forage yield is needed for informed use of soil inputs such as variable rate technology (VRT) for lime and fertilizers. The objective of this research was to map and evaluate the spatial variability of soil properties, yield, lime and fertilizer needs and economic return of an alfalfa pasture. The study was conducted in a 5.3 ha irrigated alfalfa pasture in Sao Carlos, SP, Brazil that was directly grazed and intensively managed in a 270-paddock rotational system. Alfalfa shoot dry matter yield was evaluated before grazing. Soil samples were collected at 0–0.2 m depth, and each sample represented a group of 2 or 3 paddocks. Apparent soil electrical conductivity (ECa) was measured with a contact sensor. The cost of producing 1 ha of alfalfa was estimated from the amount of lime and fertilizer needed and was then used to estimate the total cost of production for the dairy system. The alfalfa dry matter yield was used to simulate the pasture stocking rate, milk yield, gross revenue and net profit. The spatial variability of soil properties and site-specific liming and fertilizer needs were modeled using semi-variograms with VESPER software, the soil fertility information and economic return were modeled with SPRING software. The results showed that geostatistics and GIS were effective tools for revealing soil and pasture spatial variability and supporting management strategies. Soil nutrients were used to classify the soil spatial distribution map and design site-specific lime and fertilizer application maps. Spatial variation in forage and spatial estimates of stocking and milk yield are adequate pasture management tools. Spatial analyses of needs, forage availability and economic return are management tools for avoiding economic problems, as well as potential environmental problems, caused by unbalanced nutrient supplies and over- or under-grazing.     

A Two-Factor Empirical Deterministic Response Surface Calibration Model for Site-Specific Predictions of Lime Requirement

This paper describes the development of an empirical deterministic two-factor response surface model for the Woodruff lime-requirement buffer (WRF). The model may be used to produce variable-rate lime requirement maps, or to predict lime requirements in real-time. Hence it may be suitable as a component of a decision support system (DSS) for the site-specific management of acid soil. The models' predictions were compared to those of a one-factor response surface, and those of a linear regression. The models tested were validated against soil-CaCO₃ incubations using a statistical jackknifing procedure for error and bias estimations. The Akaike Information Criterion (AIC) was used to ascertain the best model in terms of goodness of fit and parsimony. The two-factor response surface model produced the best lime requirement estimates, followed by the single-factor model, then the conventional linear regression. The advantages of the response surface models are their improved prediction accuracy, and their flexibility in the choice of any target pH (from pH 5.5 to 7) without the need for excessive calibrations. The uncertainty of the model was assessed using data from an agricultural field in Kelso, New South Wales, Australia. Block kriged maps of soil pH measured in 0.01 M CaCl₂ (pH_CaCl2), WRF buffer pH (pH_buffer) and lime requirements to a target pH of 7 were produced, to compare their spatial distributions. Finally the economic and agronomic benefits of site-specific liming were considered.     

磷肥和石灰对酸性土壤上一年生黑麦草生长的影响

在二种强酸性、严重缺磷的土壤上,对一年生黑麦草做了磷肥和石灰用量的盆栽试验．结果表明：严重缺磷的土壤,施磷可成倍增加黑麦草产量;黄筋泥需磷量相对红砂土较高些．施磷量与收获后土壤速效磷含量与黑麦草产量密切相关,但土壤速效磷的等级指标,红砂土应比黄筋泥高些．石灰的增产作用远比磷肥低,增产效果还因施磷量不同而不同．在施石灰提高土壤ｐＨ达５．５的范围内,施磷量低的处理产量随石灰施用量的增加而增加;施磷量较高时,石灰的增产效果停留在低石灰用量上．     

Agitated soil measurement method for integrated on-the-go mapping of soil pH, potassium and nitrate contents

Knowledge of spatial variability of soil attributes within an agricultural field is critical for successful site-specific crop management. Soil sensing techniques to assess this variability on-the-go are being developed as an alternative to tedious manual soil sampling and laboratory testing. The goal of this study was to evaluate an Agitated Soil Measurement (ASM) method for integrated on-the-go mapping of soil pH, soluble potassium and residual nitrate contents using ion-selective electrodes. To implement ASM, an Integrated Agitation Chamber Module (IACM) was developed and attached to a commercial soil pH mapping implement. Precision of the tested electrodes was assessed through the root mean squared error (RMSE) and ranged from 0.10 for pK to 0.22 for pNO₃ (units represent the negative base 10 logarithm of the molar concentration of specified ions). The accuracy of the electrodes was assessed by comparing test results against reference measurements conducted in a commercial soil laboratory using the linear regression method. Average accuracy error ranged from 0.11 for pK to 0.23 for pNO₃. In a field simulation test, neither precision nor accuracy errors obtained with ASM were lower than for a previously investigated Direct Soil Measurement (DSM) method, which produced precision errors ranging from 0.11 for pH to 0.22 for pNO₃ and accuracy errors ranging from 0.12 for pNO₃ to 0.20 for pH. The coefficients of determination (r²) of linear regressions between individual field simulation measurements and corresponding average reference measurements were 0.85–0.89 (pH), 0.50–0.54 (pK), and 0.14–0.32 (pNO₃). However, laboratory evaluation of the ASM method revealed substantially lower measurement errors and increased r² values when compared to the field simulation, indicating that the proposed ASM method retains the potential for improving on-the-go field mapping. Except for reduced electrode abuse and the ability to use less expensive half-cell ion-selective electrodes, physical implementation of ASM through the IACM did not bring substantial improvement over conventionally available DSM. This could be attributed to the design of the IACM and use of half-cell electrodes. Further research is necessary to improve the design of the solution-based measuring equipment and to develop an algorithm integrating on-the-go measurements with other sources of spatial data for an improved decision-making process.     

Mobile TDR for geo-referenced measurement of soil water content and electrical conductivity

The development of site-specific crop management is constrained by the availability of sensors for monitoring important soil and crop related conditions. A mobile time-domain reflectometry (TDR) unit for geo-referenced soil measurements has been developed and used for detailed mapping of soil water content and electrical conductivity within two research fields. Measurements made during the early or late season, when soil moisture levels are close to field capacity, are related to the amount of plant available water and soil texture. Combined measurements of water content and electrical conductivity are closely related to the clay and silt fractions of a variable field. The application to early season field mapping of water content, electrical conductivity and clay content is presented. The water and clay content maps are to be used for automated delineation of field management units. Based on a spatial analysis of the soil water measurements, recommendations are made with respect to sampling strategies. Depending on the variability of a given area, between 15 and 30 ha can be mapped with respect to soil moisture and electrical conductivity with sufficient detail within 8 h.     

Adaptation of regional digital soil mapping for precision agriculture

In the initial phase of a national project to map clay, sand and soil organic matter (SOM) content in arable topsoil in Sweden, a study area in south-west Sweden comprising about 100 000 ha of arable land was assessed. Models were created for texture, SOM and two estimated variables for lime requirement determination (target pH and buffering capacity), using a data mining method (multivariate adaptive regression splines). Two existing reference soil datasets were used: a grid dataset and a dataset created for individual farms. The predictor data were of three types: airborne gamma-ray spectrometry data, digital elevation from airborne laser scanning, and legacy data on Quaternary geology. Validations were designed to suit applicability assessments of prediction maps for precision agriculture. The predictor data proved applicable for regional mapping of topsoil texture at 50 × 50 m² spatial resolution (root mean square error: clay = 6.5 %; sand = 13.2 %). A novel modelling strategy, ‘Farm Interactive’, in which soil analysis data for individual farms were added to the regional data, and given extra weight, improved the map locally. SOM models were less satisfactory. Variable-rate application files for liming created from derived digital soil maps and locally interpolated soil data were compared with ‘ground truth’ maps created by proximal sensors on one test farm. The Farm Interactive methodology generated the best predictions and was deemed suitable for adaptation of regional digital soil maps for precision agricultural purposes.     

Simulation of Cotton Production for Precision Farming

Most crop simulation models do not directly consider the spatial variability of inputs nor do they produce outputs that show the expected spatial variability of yield across a field. If such models were available for precision farming, then researchers could much better evaluate the effects of soil sampling densities to determine the number of samples necessary to adequately model a particular field. The objectives of this study were: (1) to design and implement a spatial simulation methodology for examining details of precision farming and (2) use this to evaluate the effects of different soil sampling resolutions on predicted yield and residual nitrates through spatially variable nitrogen applications. The GOSSYM/COMAX cotton growth model/expert system and the GRASS geographic information system were used to develop a spatial simulation that produces spatially variable outputs. Inputs to the model were collected from a 3.9-ha cotton field. Soil nitrate, a primary driver in fertilizer recommendations, was sampled on a 15.2-m regular grid for depths to 15 cm and on a 30.5-m regular grid at six 15-cm depth intervals (down to 90 cm). COMAX was used to determine spatially variable fertilizer recommendations. GOSSYM was used to simulate perfect application of these recommendations and predicted spatially variable yield and residual nitrates. Reductions in sampling density or resolution were simulated by systematically reducing the amount of data available to COMAX for calculating spatially variable fertilizer recommendations. GOSSYM subsequently used these recommendations (based upon less and less knowledge of soil nitrates) to simulate the effects of differing sampling resolutions on predicted yield and residual nitrates. For recommendations based upon a 15.2-m grid of inputs, 41.4 kg/ha of nitrate fertilizer produced 801.7 kg/ha of cotton and left an average of 9.4 ppm of nitrate in the soil profile. For a 30.5-m grid, 42.8 kg/ha of nitrate fertilizer resulted in a yield of 811.2 kg/ha and residual soil nitrate of 8.3 ppm. For 45.7-m and 61.0-m grids, the results were 43.3 kg/ha and 41.2 kg/ha of nitrate fertilizer, 755.3 kg/ha and 794.3 kg/ha of cotton, and 11.5 ppm and 8.1 ppm of residual soil nitrate, respectively. This study concluded that crop simulations and geographic information systems are a valuable combination for modeling the effects of precision farming and planning variable rate treatments. Simulation results indicate that excessive fertilization, while potentially damaging to the environment, may also have a negative impact on yield.     

Efficacy of Grid and Zone Soil Sampling Approaches for Site-Specific Assessment of Phosphorus,Potassium, pH,and Organic Matter

Within-field variability of plant-available nutrients often results in different fertilizer requirements across a field. There is uncertainty concerning the efficacy of alternative sampling strategies suitable for site-specific management. This study compared various soil sampling approaches for P, K, pH, and organic matter (OM) in eight agricultural fields. Soil samples were collected using an intensive 0.2-ha grid-point procedure, and were used to compare less intensive sampling approaches. The approaches were based on 1.2–1.6-ha grid cells (Grid), soil series of digitized soil survey maps (SSM), soil series of detailed soil survey (1:12,000 scale) maps, elevation zones, and management zones based on various information layers (ZS). The approaches varied in reducing the within-unit soil-test variability and maximizing mean soil-test values across sampling units, but none was superior across all fields and nutrients. All approaches were less efficient for P and K than for pH or OM. The Grid and ZS approach were the most effective across all nutrients and fields. However, the Grid approach was more effective for P, the Grid and ZS approaches were better for K and pH, and the SSM and ZS approaches were better for OM. The ZS approach often resulted in fewer sampling zones than the Grid approach, which implies lower soil testing costs for producers, but required more knowledge and subjective judgement than a Grid approach to adapt it to field-specific conditions.     

Grid soil sampling adoption and abandonment in cotton production

Technology adoption in precision agriculture has received considerable attention, while abandonment has received little. Survey data are now available to evaluate adoption and abandonment decisions. Understanding the factors motivating technology adoption and abandonment has implications for educational efforts directed toward improving the efficiency of production inputs and for research and development to improve the value of precision agriculture technologies. The objective of this research was to identify factors motivating the adoption and abandonment of grid soil sampling in precision cotton production. These decisions were evaluated assuming a random utility model. Data were obtained from a 2005 survey of cotton producers in 11 Southeastern states in the USA. Results from limited dependent variable regressions indicate that younger producers who farmed more cotton area, owned more of their cropland, planted larger amounts of non-cotton area, used a computer for farm management and used a Personal Digital Assistant (PDA) in the field were more likely to adopt grid soil sampling for cotton precision farming. Results also suggest that producers with more cotton area who owned livestock and adopted management zone soil sampling were more likely to abandon grid soil sampling, while those who used a PDA in the field, used grid soil sampling for more years and followed up grid soil sampling with variable-rate fertilizer application were less likely to abandon grid soil sampling for cotton production.     

石灰与生物炭对矿山废水污染农田土壤的改良效应

为研究石灰、生物炭单施和配施对酸性矿山废水污染农田土壤理化性质及作物生长的影响,在云南某酸性矿山废水污染农田,开展石灰（0、1 500、4 500 kg·hm^-2）、生物炭（0、15 000、45 000 kg·hm^-2）单施与配施的大田试验。结果表明：双因素分析表明,石灰和生物炭对土壤pH、速效养分含量、有效态Cd含量、养分含量、产量存在显著的影响,并且二者之间存在显著的交互作用。与不添加石灰和生物炭处理相比,石灰单施升高酸性磷酸酶活性,增加细菌和放线菌数量,降低脲酶活性和碱解N含量;生物炭单施增加真菌和放线菌数量,减少碱解N含量,降低酸性磷酸酶和脲酶活性;石灰、生物炭配施增加微生物数量和碱解N含量,升高脲酶活性,降低酸性磷酸酶活性;石灰、生物炭单施和配施均显著提高土壤pH和速效K含量,增加CEC,显著降低土壤速效P、有效态Cd和玉米植株Cd含量,同时增加玉米生物量、养分含量和产量。相关分析表明,土壤pH值与有效态Cd含量呈极显著负相关;玉米产量与土壤有效态Cd含量呈显著负相关。研究表明,石灰、生物炭单施和配施均能改善酸性矿山废水污染农田土壤理化性质,降低土壤Cd有效性和玉米Cd含量,提高玉米产量,具有明显的土壤改良效应。石灰与生物炭配合施用更佳,其中4 500 kg·hm^-2石灰+45 000 kg·hm^-2生物炭处理效果最好。     

Variable-rate nitrogen fertilization of winter wheat under high spatial resolution

Variable-rate application (VRA) addresses in-field variation in soil nitrogen (N) availability and crop response, and as such is a tool for more effective site-specific management. This study assessed the performance of a VRA system for on-the-go delivery of granular fertilizer in 7-m wide and 200-m long strips of a 2.4-ha wheat field. A randomized complete block design consisted of three treatment strips (a preplant uniform application of 100 kg N/ha, a preplant + in-season uniform farmer rate of 212 kg N/ha and a preplant + in-season VRA) within four blocks. The VRA prototype consisted of Crop Circle ACS-430 active canopy sensors, a GeoScout X data logger that processed the geospatial data to convey a real-time N rate signal (1 Hz) to a Gandy Orbit Air 66FSC spreader through a Raven SCS 660 controller. Crop monitoring included analysis of in-season soil and plant samples, water balance and grain yield. VRA delivered an economic optimum N rate using 72% less in-season N or 38% less total N (131 kg N/ha) than that applied by the farmer (212 kg N/ha). The reduction of total N inputs came about without any yield losses and translated to 58% N-use efficiency in comparison to 44% of the farmer practice and 52% of the preplant control. VRA also provided a much higher revenue over fertilizer costs, €68/ha and €118/ha higher than the preplant control and the farmer practice, respectively. The return of VRA per unit of N was equal to that of the large preplant application due to low leaching losses. Overall, the high-resolution VRA was superior in terms of environmental benefits and profitability with the least uncertainty to the farmer.     

Liming reduces soil phosphorus availability but promotes yield and P uptake in a double rice cropping system

Liming is often applied to alleviate soil acidification and increase crop yield on acidic soils, but its effect on soil phosphorus (P) availability is unclear, particularly in rice paddies. The objective of this study was to examine the effect of liming on rice production, yield and P uptake in a three-year field experiment in a double rice cropping system in subtropical China. We also conducted an incubation experiment to investigate the direct effect of liming on soil available P and phosphatase activities on paddy soils in the absence of plants. In the incubation experiment, liming reduced soil P availability (measured as Olsen-extractable P) by 14–17% and inhibited the activity of soil acid phosphatase. Nonetheless, lime application increased grain yield, biomass, and P uptake in the field. Liming increased grain yield and P uptake more strongly for late rice (26 and 21%, respectively) than for early rice (15 and 8%, respectively). Liming reduced the concentration of soil available P in the field as well, reflecting the increase in rice P uptake and the direct negative effect of liming on soil P availability. Taken together, these results suggest that by stimulating rice growth, liming can overcome direct negative effects on soil P availability and increase plant P uptake in this acidic paddy soil where P is not the limiting factor.