Quantitative Evaluation Of The Fertility Of Tropical Soils Research Articles

Nutrient uptake and yield response of wheat (Triticum spp.) to different fertiliser applications in Ethiopia

In northern Ethiopia, the persistently low crop yield is often attributed to inadequate soil fertility and inappropriate fertiliser recommendations. This study employed the Quantitative Evaluation of the Fertility of Tropical Soils (QUEFTS) model for site-specific fertiliser recommendations to enhance wheat (Triticum spp.) yield. The objectives included quantifying soil nitrogen (N), phosphorus (P) and potassium (K) supplies, modelling observed and simulated yields, and validating the QUEFTS model. The experimental trials encompassed four treatments: (T1) model-based fertilisation; (T2) blended fertilisation; (T3) farmer-developed fertilisation; (T4) control, stratified across three different farm income groups. Soil information from the experimental plots was analysed and utilised as model inputs to estimate soil nutrient supplies. Yield and agronomic data were recorded, and nutrient uptake was analysed. Model performance was evaluated using metrics including root mean square error, coefficient of determination, index of agreement, and percent bias. Results indicated an N:P:K soil supply ratio of 7.5:1:7.8 in the experimental fields. The highest grain yield (5 886 kg ha−1) was observed in QUEFTS-based fertilisation, representing a significant (p < 0.05) increase in yield. Statistical analyses confirmed a promising level of accuracy of the model predictions. Therefore, the QUEFTS model could be a valuable tool to assist farmers in adjusting fertiliser application based on crop requirements and economic considerations.

Identifying drivers for variability in maize (Zea mays L.) yield in Ghana: A meta-regression approach

CONTEXTMaize is the main cereal crop in Ghana, but its production is adversely affected by various biotic and abiotic factors. OBJECTIVEThis study aimed to highlight the factors related to maize yield variability. To this end, yields from 978 data points within 3 agro-ecological zones (AEZs) were used in crop-based and statistical modelling. METHODSThe QUantitative Evaluation of the Fertility of Tropical Soils (QUEFTS) model, the Linear Mixed Effects Model (LMM), and the Random Forest (RF) model were used to evaluate multiple effect sizes. RESULTS AND CONCLUSIONSAnalyzing an entire set of yield data points with QUEFTS, and LMM explained 19%, and 26% of yield variability, respectively. Considering all data points in the RF model, nitrogen fertilizer (NF) rate, temperature, root zone depth, rainfall, and variety accounted for 27%, 15%, 13%, 10%, and 9% of yield variation, respectively. In Guinea Savanna (GS), Transition Zone (TZ), and Deciduous Forest (DF), QUEFTS explained 30%, 20%, and 4% of yield variability, respectively. LMM, however, explained 47%, 51%, and 79% of yield variability in those AEZs. LMM showed that the phosphorus fertilizer (PF) rate was very important and exceeded the importance of the NF rate in GS. LMM showed also that yield variability was significantly related to maize variety at the AEZ scale. In DF, soil chemistry (marginal R2 = R2m = 0.48) and environmental variables (R2m = 0.43) contributed more to explaining yield variability, whereas in GS and TZ, fertilizer rates (R2m = 0.35 in GS and 0.26 in TZ) and variety (R2m = 0.04 in GS and 0.20 in TZ) played a much larger role. In GS, TZ, and DF, the RF model explained 74%, 79%, and 84% of the variance in yield, respectively. These findings suggest low impact of fertilization on yield on the inherently fertile soils in the DF, while fertilization drives yield increase in the less fertile TZ and GS AEZs. We may conclude that QUEFTS was unable to capture yield variability and, according to RF and LMM analysis, the NF rate was the most important factor in explaining yield variability in the data. It can also be concluded that the factors responsible for yield variability are AEZ dependent. SIGNIFICANCEWe discuss the implications of these findings to uncover factors driving maize yield variability. It also provides information to guide and prioritize actions to be taken based on the importance of these factors in contributing to yield variability.

Spectral soil analysis for fertilizer recommendations by coupling with QUEFTS for maize in East Africa: A sensitivity analysis

Laboratory analysis of soil properties is prohibitively expensive and difficult to scale across the soils in sub-Saharan Africa. This results in a lack of soil-specific fertilizer recommendations, where recommendation can only be provided at a regional scale. This study aims to assess the feasibility of using spectral soil analysis to provide soil-specific fertilizer recommendations.Using a range of spectrometers [NeoSpectra Saucer (NIR), FieldSpec 4 (vis-NIR) with contact probe or mug light interface, FTIR Bruker Tensor 27 (MIR)], 346 archived soil samples (0–20 cm) with known soil chemical properties collected from Ethiopia, Kenya and Tanzania were scanned. Partial least square regression (PLSR) was used to develop prediction models for selected soil properties including pH, soil organic carbon (SOC), total nitrogen, Olsen P, and exchangeable K. These predicted properties, and associated uncertainty, were used to derive fertilizer recommendations for maize using the Quantitative Evaluation of the Fertility of Tropical Soils (QUEFTS) model parameters for sub-Saharan Africa.Most soil properties (pH, SOC, total nitrogen, and exchangeable K) were well predicted (Concordance Correlation Coefficient values between 0.88 and 0.96 and Ratio of Performance to Interquartile values between 1.4 and 5.9) by all the spectrometers but there were performance variations between soil properties and spectrometers. Use of the predicted soil data for the development of fertilizer recommendations gave promising results when compared to the recommendations obtained with the conventional soil analysis. For example, the least performing NeoSpectra Saucer over/under-estimated up to 8 and 24 kg ha-1N and P, respectively, though there was insignificant variation in estimation of P fertilizer among spectrometers. We conclude that spectral technology can be used to determine major soil properties with satisfactory precision, sufficient for specific fertilizer decision making in East Africa, possibly even with portable equipment in the field.

Spatial predictions of maize yields using QUEFTS – A comparison of methods

Using fertilisers is indispensable for closing yield gaps in Sub Saharan Africa. Current fertiliser recommendations, however, are often blanket recommendations which do not take spatial variation in soil conditions within a region or country into account. Soil maps can potentially support fertiliser recommendations at a higher spatial resolution. The QUantitative Evaluation of the Fertility of Tropical Soils (QUEFTS) model is a decision support tool that predicts crop yields as an indicator of soil fertility and can be used to evaluate yield responses to fertilisers. It was designed for field level output and runs on field-specific soil information. The aim of this study was to compare two methods for developing maps of QUEFTS output, i.e. maize yield and the yield-limiting nutrient, with Rwanda as a case study. We used a database containing soil analysis results of 999 samples collected across Rwanda. Transfer functions were applied to predict the required P-Olsen and Exchangeable K input for QUEFTS based on the soil data. For the “Calculate-then-Interpolate” (CI) method, transfer functions and QUEFTS were applied to point data, and the final output was then interpolated using random forest modelling. For the “Interpolate-then-Calculate” (IC) method, maps of the soil parameters were developed first, before applying calculations. Implications of the chosen method (i.e. CI or IC) on QUEFTS predictions on a national scale were evaluated using set-aside locations. Results showed low precision and accuracy of QUEFTS maize yield predictions across Rwanda. The CI method performed better in predicting QUEFTS yield and yield-limiting nutrient than the IC method. Correlations between mapped yield predictions and predictions on set-aside evaluation locations were similar for the CI (r = 0.444) and IC (r = 0.439) methods. The poorer performance of the IC method was mostly due to overestimation of yields, which was most likely caused by the effect of smoothing on the soil maps used as input for QUEFTS. We conclude that the CI method is the preferred method for spatial application of QUEFTS.

Estimation of Cotton Nutrient Uptake Based on the QUEFTS Model in Xinjiang

The Xinjiang cotton-producing region is the main cotton-growing region in China; however, the combination of excessive application but low utilization of fertilizers has restricted its agricultural development. Estimating the balanced nutrient requirements for cotton is essential to increase its yield and nutrient use efficiency. The results from 372 field experiments performed between 1996 and 2019 were used to build a QUEFTS (quantitative evaluation of the fertility of tropical soils) model to investigate relationships between cotton seed yield and nutrient accumulation, with the data from 2017 to 2020 used to validate the model. The results demonstrated that the QUEFTS model predicted a linear relationship between target yield and nutrient uptake until the target yield reached about 60–70% of the potential yield. To produce 1000 kg of seed cotton, 28.3 kg N, 6.1 kg P, and 29.6 kg K were required for cotton, and the corresponding ratio of N, P, and K was 4.64:1:4.85. The corresponding internal efficiencies (IEs) for N, P, and K were 35.4, 163.2, and 33.7 kg/kg, respectively. Field validation indicated that the QUEFTS model could be used to estimate nutrient uptake at a targeted yield and contribute to developing a fertilizer recommendation strategy for Xinjiang cotton production.

QUEFTS Model-based Estimation of the Nutrient Requirements and Fertilizer Recommendation for Chinese Onion

Accurate estimation of the nutrient requirements of Chinese onion is essential to increase its nutrient utilization efficiency and yield. In this study, the yield and nutrient uptake data were collected from major Chinese onion growing regions during 2001 to 2018, and the relationship between Chinese onion yield and nutrient uptake was evaluated using the Quantitative Evaluation of the Fertility of Tropical Soils (QUEFTS) model. The QUEFTS model predicted the linear-parabolic platform curve of the balanced nutrient uptake of Chinese onion and estimated the demand of nitrogen (N), phosphorus (P), and potassium (K) for the potential target yields ranging from 40 t/ha to 120 t/ha. The nutrients required for the target yield increased linearly before reaching 60% to 70% of the potential yield. Nutrient requirements for producing 1 t of Chinese onion were 1.91 kg N–0.28 kg P–1.71 kg K. The corresponding nutrient internal efficiency (IE, yield per unit nutrient uptake) was 524.6 kg/kg, 3585.7 kg/kg, and 584.3 kg/kg for N, P and K, respectively. Subsequently, a nutrition decision-making software, Nutrient Expert (NE), for the Chinese onion was developed based on the improved QUEFTS model. Field verification studies for NE fertilizer recommendation were conducted in multiple Chinese onion growing plots for 2 consecutive years. Results showed that the QUEFTS model can be used to accurately estimate the nutrient requirements for Chinese onion within a defined range of target yield.

Transfer functions for phosphorus and potassium soil tests and implications for the QUEFTS model

Multi-element soil extractions such as Mehlich 3 (M3) have gained popularity in recent years, but comparing outcomes to other soil testing methods is not always straightforward. In this study, extraction mechanisms of M3, Olsen and neutral 1 M ammonium acetate (AA) soil tests were explored and transfer functions were derived between P-Olsen and P-M3 as well as between K-AA and K-M3. Soils from tropical and temperate areas were used to derive these P and K transfer functions and were evaluated separately. The application of these transfer functions for tropical soils was evaluated by using them as input for the Quantitative Evaluation of the Fertility of Tropical Soils (QUEFTS). AA and M3 generally extracted similar amounts of K, but relations between K-AA and K-M3 were different for tropical and temperate soils. For tropical soils, the transfer function did not require additional parameters besides K-M3 to predict K-AA, but for temperate soils inclusion of clay content and pH was needed. This difference between tropical and temperate soils was explained by clay mineralogy. The relation between P-Olsen and P-M3 in tropical soils was found to be dependent on pH, Al-M3, Fe-M3 and Ca-M3. P-Olsen and K-AA values, calculated with their respective transfer functions, were used as input for QUEFTS. The yields predicted with measured P-Olsen and Exch. K were used as benchmark. For 63 out of 81 soil samples, predicted maize yields with transfer functions deviated less than 10% from the benchmark. The largest deviations from the benchmark were found for low P-Olsen and K-AA values, which corresponds to QUEFTS maize yield predictions up to 3000 kg ha−1. We conclude that a M3 extraction results and soil pH can reliably be transferred to, and thus replace P-Olsen and K-AA determinations with the functions developed for tropical soils. The transfer functions can be used to generate input for the QUEFTS model with minor effects on yield predictions, thus expanding its applicability in cases where only M3 extraction results are available.

Adapting the QUEFTS model to predict attainable yields when training data are characterized by imperfect management

Abstract Understanding yield responses to nutrient application is a key input for extension advice and strategic agricultural investments in developing countries. A commonly used model for yield responses to nutrient inputs in tropical smallholder farming systems is QUEFTS (QUantitative Evaluation of the Fertility of Tropical Soils). While QUEFTS has a strong conceptual foundation, a key assumption is that nutrients are the only limiting factors. One implication of this is the required assumption of ‘perfect management’. This may be problematic in the application of QUEFTS in smallholder farming systems with a wide variety of yield limiting factors. In a previous study, QUEFTS was calibrated using farm trials in two major maize production zones in Nigeria. To reduce observed variability in correlations between estimated soil nutrient (N, P, K) supply and soil parameters (e.g. soil organic carbon, soil pH; step 1 of QUEFTS) a Mahalanobis distance method was used to remove data points not adhering to expected correlations. In this study, we assessed an alternative approach: can the QUEFTS model be adapted to fit smallholder farming systems and associated variation in management? Using 676 observations from the same nutrient omission trials in two major maize production zones in Nigeria, we compare a standard linear regression approach with a quantile regression approach to calibrate QUEFTS. We find that under the standard linear regression approach, there is a poor relation between predicted and observed yields. Using quantile regression, however, QUEFTS performed better at predicting attainable yields – defined as the 90th percentile of observed yields – under a wide variety of production conditions. Our results indicate that using quantile regression as a way to predict attainable yields, is a useful alternative implementation of QUEFTS in smallholder farming systems with high variability in management and other characteristics.

Genotypic Variation in Nutrient Uptake Requirements of Rice Using the QUEFTS Model

Nutrient requirements for single-season rice using the quantitative evaluation of the fertility of tropical soils (QUEFTS) model in China have been estimated in a previous study, which involved all the rice varieties; however, it is unclear whether a similar result can be obtained for different rice varieties. In this study, data were collected from field experiments conducted from 2016 to 2019 in Zhejiang Province, China. The dataset was separated into two parts: japonica/indica hybrid rice and japonica rice. To produce 1000 kg of grain, 13.5 kg N, 3.6 kg P, and 20.4 kg K were required in the above-ground plant dry matter for japonica/indica hybrid rice, and the corresponding internal efficiencies (IEs) were 74.0 kg grain per kg N, 279.1 kg grain per kg P, and 49.1 kg grain per kg K. For japonica rice, 17.6 kg N, 4.1 kg P, and 23.0 kg K were required to produce 1000 kg of grain, and the corresponding IEs were 56.8 kg grain per kg N, 244.6 kg grain per kg P, and 43.5 kg grain per kg K. Field validation experiments indicated that the QUEFTS model could be used to estimate nutrient uptake of different rice varieties. We suggest that variety should be taken into consideration when estimating nutrient uptake for rice using the QUEFTS model, which would improve this model.

Estimation of Watermelon Nutrient Requirements Based on the QUEFTS Model

Estimating balanced nutrient requirements for a watermelon plantation is essential to increase its fruit yield and nutrient use efficiency. This is vital for China, which produces 60% of world’s watermelons with excessive fertilizer application. Therefore, datasets between 2000 and 2019 from field experiments in major watermelon producing regions across China were collected to assess relationships between fruit yield and nutrient uptake, and to estimate nitrogen (N), phosphorus (P), and potassium (K) requirements for a target yield using a modified Quantitative Evaluation of the Fertility of Tropical Soils (QUEFTS) model. The results showed that the QUEFTS model predicted a linear increase in fruit yield to 60–70% of the total potential yield when balanced amounts of N, P, and K nutrients were absorbed. To produce 1000 kg of watermelon, 2.11 kg N, 0.27 kg P, and 2.69 kg K were required in shoot, and the corresponding internal efficiencies (IE) were 475, 3682, and 372 kg fruit per kg of N, P, and K, respectively. The modified QUEFTS model also simulated a balanced N, P, and K removal by fruit (accounting for 50.9%, 58.2%, and 66.4% of these nutrient accumulations in shoots, respectively). Field validation experiments further verified that the modified QUEFTS model could be used for estimating balanced nutrient requirements. Results from this study can provide practical guidance on fertilizer recommendations for improving fruit yield while preventing excessive or deficient nutrient supplies in China’s watermelon plantations.

Temporal variation in nutrient requirements of tea (Camellia sinensis) in China based on QUEFTS analysis

Fertilisation datasets collected from field experiments (n = 21) in tea-producing areas from 2016 to 2018 were used to build a quantitative evaluation of the fertility of tropical soils (QUEFTS) model to estimate nutrient uptake of tea plants, and to investigate relationships between tea yield and nutrient accumulation. The production of 1000 kg spring tea (based on one bud with two young expanding leaves) required 12.2 kg nitrogen (N), 1.2 kg phosphorus (P), and 3.9 kg potassium (K), and the corresponding internal efficiencies (IEs) for N, P, and K were 82.0, 833.3, and 256.4 kg kg−1. To produce 1000 kg summer tea, 9.1 kg N, 0.8 kg P, and 3.1 kg K were required, and the corresponding IEs for N, P, and K were 109.9, 1250.0, and 322.6 kg kg−1. For autumn tea, 8.8 kg N, 1.0 kg P, and 3.2 kg K were required to produce 1000 kg tea, and the corresponding IEs for N, P, and K were 113.6, 1000.0, and 312.5 kg kg−1. Field validation experiments performed in 2019 suggested that the QUEFTS model can appropriately estimate nutrient uptake of tea plants at a certain yield and contribute to developing a fertiliser recommendation strategy for tea production.

Estimating Nutrient Uptake Requirements for Potatoes Based on QUEFTS Analysis in China

Excessive or imbalanced fertilization has not only decreased nutrient use efficiency, but also degraded arable land and posed a great threat to the environment. In this study, the datasets were collected from field experiments for the period 1992 to 2017 in the main potato (Solanum tuberosum L.) production regions of China. We used the Quantitative Evaluation of the Fertility of Tropical Soils (QUEFTS) model to estimate the soil‐plant balanced N, P, and K requirements for the potato in China. Our results revealed that there were great differences in the potato yield and nutrient uptake. The upper and lower 2.5th percentiles of N, P, and K internal efficiencies (IE, kg tuber per kg nutrient in the total plant) data were used as maximum accumulation (a) and maximum dilution (d) boundary parameters in the QUEFTS model, which were 133 and 463 kg kg–1 for N, 652 and 3030 kg kg–1 for P, and 119 and 790 kg kg–1 for K, respectively. The QUEFTS model predicted plant nutrient uptake of 4.0 kg N, 0.7 kg P, and 3.5 kg K to produce 1 Mg of tuber in the linearly distributed portion, and a corresponding tuber nutrient demand of 2.9 kg N, 0.5 kg P, and 2.3 kg K to produce 1 Mg of tuber. Field validation experiments confirmed that the QUEFTS model could be used to simulate optimum nutrient uptake and provide appropriate parameters in building fertilizer recommendation, which helps improve nutrient use efficiency for potato in China.Core Ideas This is the first report of QUEFTS model on estimating the optimum nutrient requirements for potato crops in China. The datasets used in this study were collected from multi‐year‐site field experiments. Multi‐site field validation experiments in potato‐producing areas confirmed the feasibility of QUEFTS model‐simulated nutrient uptake.

Estimating nutrient uptake requirements for radish in China based on QUEFTS model

Imbalanced fertilization has caused lower yield and nutrient use efficiency for radish (Raphanus sativus L.) production in China. Estimating nutrient requirements for radish is crucial in optimizing fertilization to resolve the problem. On-farm experiments in the radish-growing regions of China from 2000 to 2017 were collected to investigate the relationship between fleshy root yield and nutrient accumulation in radish plant using the Quantitative Evaluation of the Fertility of Tropical Soils (QUEFTS) model. The QUEFTS model predicted a linear increase in fleshy root yield if nutrients were taken up in balanced amounts until yield reached about 60%–70% of the potential yield. The balanced N, P, and K requirements in radish plant simulated by the QUEFTS model were 2.15, 0.45, and 2.58 kg to produce 1000 kg of fleshy root, and the corresponding internal efficiencies (IEs, kg fleshy root per kg nutrient in total plant dry matter) for N, P, and K were 465.1, 2222.2, and 387.1 kg kg−1. The simulated balanced N, P, and K removal by fleshy root to produce 1000 kg fleshy root were 1.34, 0.30, and 1.93 kg, respectively. Approximately 62%, 67%, and 75% of N, P, and K in radish plant were presented in the fleshy root and removed from the soil. Field validation experiments confirmed the consistency between the observed and simulated nutrient uptake values. The QUEFTS model was proven to be effective for estimating nutrient requirements of radish and will contribute to develop fertilizer recommendations for radish cultivated in China.

Characteristics of Yield and Harvest Index, and Evaluation of Balanced Nutrient Uptake of Soybean in Northeast China

The balance between fertilizer application and plant nutrient demand is essential for ensuring agricultural production because it is effective to prevent nutrient deficiency and excess, especially for soybean. This study used data from 29 sites of field experiments carried out in the soybean planting area of Liaoning province, China in 2011 to 2013. We (i) study the characteristics of yield, nutrient concentration, and harvest index to (ii) valuate the balanced nutrient uptake at different potential yield levels for soybean. The grain yield ranged from 804 to 4484 kg/ha, and average N, P, and K concentrations in grains were 45.7, 5.0, and 10.1 g/kg, respectively, while those in straw were 14.1, 1.8, and 6.7 g/kg, respectively. Average harvest index values of N, P, and K were 0.69, 0.65, and 0.52 kg/kg, respectively, while approximately 69% N and 65% P of the plant were stored in soybean grain, and 48% K was stored in straw. The two boundary lines of the QUEFTS (quantitative evaluation of the fertility of tropical soils) model were aN = 10.5, dN = 20.6, aP = 65.6, dP = 289.6, aK = 30.4, and dK = 162.7 as model parameters. The QUEFTS model estimated the balanced nutrient uptake with yield targets increased following a linear–parabolic–plateau curve. A continual linear increase in grain yield with 65.5 kg N, 7.0 kg P, and 13.9 kg K was required to produce 1000 kg grain, until the yield target reached approximately 60–70% of the potential yield, and the corresponding ratio of N, P, and K was 9.35:1:1.8. Results could be used to estimate balanced nutrient uptake to prevent excessive fertilizer being applied and reduce environment risk for ensuring sustainable agricultural development.

Understanding cassava yield response to soil and fertilizer nutrient supply in West Africa

Background and aimsEnhanced understanding of plant and nutrient interactions is key to improving yields. We adapted the model for QUantitative Evaluation of the Fertility of Tropical Soils (QUEFTS) to assess cassava yield response to soil and fertilizer nutrients in West Africa.MethodsData from on-station and farmer’s field experiments across Togo and Ghana were used.ResultsSoil nutrient supply ranged from 86 to 177, 18–24 and 70–104 kg ha−1 of N, P and K, respectively, and induced variable cassava responses to fertilizer application. Considering harvest (HI) in deriving the physiological nutrient use efficiencies for maximum dilution (PhEmax) and for maximum accumulation (PhEmin), which are key QUEFTS parameters, improved model predictions with reduction in normalized root mean square error from 32 to 13% at Davié (Southern Togo) and from 18 to 13% at Kumasi (Southern Ghana). Model overestimated yields in Nyankpala (Northern Ghana) where drought stress reduced yields. Estimated PhEmin and PhEmax at HI of 0.50 were 41 and 96 kg kg−1 N, 232 and 589 kg kg−1 P, and 34 and 160 kg kg−1 K.ConclusionsQUEFTS can be used for site-specific estimates of cassava yield responses to fertilizers under rain-fed conditions in West Africa, provided that yield is primarily constrained by N, P and K supplies, and not by drought or other nutrients.

Estimating nutrient uptake requirements for soybean using QUEFTS model in China.

Estimating balanced nutrient requirements for soybean (Glycine max [L.] Merr) in China is essential for identifying optimal fertilizer application regimes to increase soybean yield and nutrient use efficiency. We collected datasets from field experiments in major soybean planting regions of China between 2001 and 2015 to assess the relationship between soybean seed yield and nutrient uptake, and to estimate nitrogen (N), phosphorus (P), and potassium (K) requirements for a target yield of soybean using the quantitative evaluation of the fertility of tropical soils (QUEFTS) model. The QUEFTS model predicted a linear–parabolic–plateau curve for the balanced nutrient uptake with a target yield increased from 3.0 to 6.0 t ha−1 and the linear part was continuing until the yield reached about 60–70% of the potential yield. To produce 1000 kg seed of soybean in China, 55.4 kg N, 7.9 kg P, and 20.1 kg K (N:P:K = 7:1:2.5) were required in the above-ground parts, and the corresponding internal efficiencies (IE, kg seed yield per kg nutrient uptake) were 18.1, 126.6, and 49.8 kg seed per kg N, P, and K, respectively. The QUEFTS model also simulated that a balanced N, P, and K removal by seed which were 48.3, 5.9, and 12.2 kg per 1000 kg seed, respectively, accounting for 87.1%, 74.1%, and 60.8% of the total above-ground parts, respectively. These results were conducive to make fertilizer recommendations that improve the seed yield of soybean and avoid excessive or deficient nutrient supplies. Field validation indicated that the QUEFTS model could be used to estimate nutrient requirements which help develop fertilizer recommendations for soybean.

Estimating nutrient uptake requirements for rice in China

Accurate knowledge of the nitrogen (N), phosphorus (P), and potassium (K) requirements for rice (Oryza sativa L.) in China is essential to quantitatively estimate optimal fertilizer application regimes to maximize crop yield and increase nutrient use efficiency. On-farm experiments were collected in China's major rice-producing regions from 2000 to 2013, to determine the relationship between grain yield and nutrient uptake in the above-ground plant dry matter using the quantitative evaluation of the fertility of tropical soils (QUEFTS) model. The large datasets obtained which covered broader rice growing areas and ecology types (both indica and japonica) in China. The dataset was divided into two groups: single-season rice and early/middle/late rice, according to QUEFTS analysis. The QUEFTS model predicted a linear increase in grain yield if nutrients were taken up in balanced amounts until yield reached about 60–70% of yield potential. To produce 1000kg of single-season rice grain, 14.8kgN, 3.8kg P, and 15.0kgK were required in the above-ground plant dry matter, and the corresponding internal efficiencies (IEs) were 67.6kg grain per kg N, 263.2kg grain per kg P, and 66.7kg grain per kg K. For early/middle/late rice, the amount of 17.1kgN, 3.4kgP, and 18.4kgK were required in the above-ground plant dry matter to produce 1000kg of grain, and corresponding IEs were 58.5kg grain per kgN, 294.1kg grain per kgP, and 54.3kg grain per kgK. The QUEFTS model provides a scientific foundation for filtering high-yielding and high-efficiency variety, optimizing fertilizer application rate and developing nutrient management strategies to increase yield and nutrient use efficiency. This is the first report on comparison of the nutrient uptake of different ecology types in rice production area of China using QUEFTS model and test their feasibility through field validation.

Estimating nutrient requirements for winter oilseed rape based on QUEFTS analysis

SUMMARYEstimating crop nutrient requirements for winter oilseed rape (Brassica napus L.) is a crucial step in optimizing fertilization to enhance seed yield and improve fertilizer use efficiency. In the present paper, a database composed of 1035 on-farm observations collected from 2005 to 2010 across the major winter oilseed rape production regions in China was used to evaluate internal nutrient efficiencies (kg seed per kg nutrient in plant dry matter); then the Quantitative Evaluation of the Fertility of Tropical Soils (QUEFTS) model for winter oilseed rape was developed to describe the nutrient uptake-yield relationship of oilseed rape over a wide range of environmental conditions and predict the nutrient requirements for a target yield. After excluding observations with low harvest index values, &lt;0·20, and excluding 0·025 of the highest and lowest internal nutrient efficiencies, the minimum and maximum internal nutrient efficiencies were estimated as 13·1 and 31·6 kg seed/kg nitrogen (N), 68·9 and 200·3 kg seed/kg phosphorus (P) and 8·9 and 31·1 kg seed/kg potassium (K), respectively. On the basis of the data settings, the balanced N, P and K uptake at different yield potential levels was calculated using a linear–parabolic–plateau curve with the QUEFTS model. Crop nutrient requirements increased linearly until the yield reached approximately 0·60–0·70 of the potential yield, and 46·0 kg N, 8·0 kg P and 57·1 kg K were found to be needed to produce 1000 kg of seed. The corresponding internal nutrient efficiencies were 21·8, 125·1 and 17·5 kg seed/kg N, P and K, respectively. However, when the target yields approached the yield potential, a decrease in internal nutrient efficiencies was detected in the model. The predicted nutrient requirement values simulated by the QUEFTS model compared well with observed values across a range of conditions. To conclude, the QUEFTS model was shown to be a practical and robust tool for assessing the crop nutrient requirements of winter oilseed rape.

Estimation of separate effects of water and nutrient limitation for rainfed lowland rice within a province in the Mekong region

Drought and low soil fertility are major constraints for high yield in rainfed lowland rice in Laos. To examine the separate effects of low water and nutrient availability and then to provide regional-scale fertilizer recommendations for rainfed lowland rice, a simulation study, together with field measurements, was carried out for Savannakhet province in the 2007 and 2008 seasons. To achieve this, a soil nutrient model QUEFTS (Quantitative Evaluation of the Fertility of Tropical Soils) was combined with a recently developed soil water balance (SWB) model. The nutrient model was used to estimate yield from N, P and K uptake under various supply of nutrient in the soil, and then yield reduction due to water stress was calculated from the water balance model. The combined model was validated with the yield results of field experiments conducted in the dry season with no water limitation and also with yields obtained from 101 farms across the province in two wet seasons where both water and nutrient may have been limiting yield. The yield under inherent nutrient supply without fertilizer input was calculated from soil organic carbon, available P and K, and pH, and without water limitation, and was estimated to range widely between 1 and 2tha−1 for the central Lao province of Savannakhet. Yield was estimated to increase on average from 1.6tha−1 to 2.9tha−1 with the recommended fertilizer application rate of 60–13–16 N–P–K kgha−1, and up to around 6tha−1 under non-limited nutrient conditions. Yield reduction due to water stress alone, estimated from the soil water balance model, was 4–12%. These results indicate that the influence of water stress on the yield estimated for the two wet seasons was rather small, compared with that of nutrient stress. Fertilizer rates to achieve a particular yield target, which were calculated by subtracting inherent nutrient supply from nutrient uptake required for the yield target and then dividing by fertilizer nutrient taken up per kg applied, were also estimated to determine the variability of nutrient requirements at different locations. In most of the rice-growing areas, nitrogen, phosphorus and potassium required to achieve the yield target of 3tha−1 varied widely between 20 and 70kgha−1, 5 and 35kgha−1 and 10 and 30kgha−1, respectively, suggesting the importance of utilizing the site-specific fertilizer recommendation for rainfed lowland rice.

Nutrient requirements for maize in China based on QUEFTS analysis

Estimating balanced nutrient requirements for maize (Zea mays L.) in China is essential to manage nutrient application more effectively for increasing crop yield and reducing the risk of negative environmental impact. On-farm datasets were collected from 2001 to 2010 from China's maize-producing regions to investigate the relationship between grain yield and nutrient accumulation in the above-ground plant dry matter of commercial hybrid maize. The QUEFTS (quantitative evaluation of the fertility of tropical soils) model was used to estimate the balanced nitrogen (N), phosphorus (P) and potassium (K) requirements in China's maize growing regions. The analysis indicated that there were great differences in the grain yield and nutrient uptake between spring maize and summer maize: minimum and maximum internal nutrient efficiencies (IE, kg grain per kg nutrient in the above-ground plant dry matter) were 36 and 89kg grain per kg N, 135 and 558kg grain per kg P, 30 and 132kg grain per kg K for spring maize, 31 and 70kg grain per kg N, 108 and 435kg grain per kg P, 32 and 110kg grain per kg K for summer maize. The model predicted a linear increase in grain yield if nutrients were taken up in balance until yield reached about 60–70% of the potential yield. To produce 1000kg of spring maize grain yield, 16.9kgN, 3.5kgP and 15.3kgK were required by above-ground dry matter of maize, and the corresponding IE were 59kg grain per kg N, 287kg grain per kg P and 65kg grain per kg K. For summer maize, 20.3kgN, 4.4kgP, 15.9kgK were needed to produce 1000kg maize grain in the linear part, and the corresponding IE were 49kg grain per kg N, 227kg grain per kg P and 63kg grain per kg K. Optimal N:P:K ratios in plant biomass were 4.83:1:4.37 for spring maize and 4.61:1:3.61 for summer maize, respectively. QUEFTS analysis also indicated that a balanced N, P and K removal by grain to produce 1000kg grain, when the target yield reached about 80% of the potential yield, the grain absorption of N, P and K accounted for 54%, 69% and 23% of above-ground N, P and K uptake for spring maize, and 67%, 85% and 23% for summer maize, respectively. Two-year field validation experiments indicated that the QUEFTS model could be used for estimating balanced nutrient requirements and contributed to developing fertilizer recommendations.