Crop Yield Response Research Articles

Optimizing fertilizer use by providing soil quality information: experimental evidence from Madagascar

BackgroundImproving food security in sub-Saharan Africa (SSA) requires increasing agricultural productivity. The increased and effective use of chemical fertilizers is widely recognized as one of the key strategies for achieving this goal. However, many smallholder farmers in SSA still grow crops without using fertilizer, and even when they do use fertilizer, the amount applied is often less than the recommended level. In addition to various constraints related to input markets and farmers’ socioeconomic characteristics, uncertainty about crop yield response is known to discourage fertilizer use. The purpose of this study was to investigate how site-specific information on soil characteristics can help farmers optimize their fertilizer application decisions by reducing uncertainty in yield response. Our unique approach uses simple binary information about the expected effectiveness of nitrogen fertilizer based on a single soil parameter.MethodologyThe simple binary information was generated for a focal rice plot of each 70 household. Based on the evaluation of oxalate-extractable phosphorus content in the soil composites collected from each of these plots, each plot was categorized into either high or low in terms of the expected effectiveness of nitrogen application. A randomized controlled trial was conducted to estimate the impact of providing this simple binary information primarily on nitrogen application rate and, consequently, on rice yield and income at plot-level and household-level.ResultsThe results showed that first, compared to those in the target plots of the control households, the nitrogen application rate was greater in the target plots of the treatment households who were informed of the high expected effectiveness. Second, information that the expected effectiveness was high increased the amount of nitrogen fertilizer in the target plot compared to that in other plots with no information about expected effectiveness within a household. Third, this change in fertilizer allocation led to higher rice yields and higher rice incomes at the household level.ConclusionsThese results highlight how the binary information about the expected effectiveness with a single soil parameter can improve fertilizer allocation among rice plots and its use efficiency to increase rice productivity and income.

Optimizing crop seeding rates on organic grain farms using on farm precision experimentation

Organic agriculture is often regarded as less damaging to the environment than conventional agriculture, though at the expense of lower yields. Field-specific precision agriculture may benefit organic production practices given the inherent need of organic farmers to understand spatiotemporal variation on large-scale fields. Here the primary research question is whether on-farm precision experimentation (OFPE) can be used as an adaptive management methodology to efficiently maximize farmer net returns using variable cover crop and cash crop seeding rates. Inputs of cash crop seed and previous-year green manure cover crop seed were experimentally varied on five different farms across the Northern Great Plains from 2019 to 2022. Experiments provided data to model the crop yield response, and subsequently net return, in response to input (seeding) rates plus a suite of other spatially explicit data from satellite sources. New, field-specific spatially explicit optimum input rates were generated to maximize net return including temporal variation in economic variables. Inputs were spatially optimized and using simulations it was found that the optimization strategies consistently out-performed other strategies by reducing inputs and increasing yields, particularly for non-tillering crops. By adopting site specific management, the average increase in net return for all fields was $50 ha−1. These results showed that precision agriculture technologies and remote sensing can be utilized to provide organic farmers powerful adaptive management tools with a focus on within-field spatial variability in response to primary input drivers of economic return. Continued OFPE for seeding rate optimization will allow quantification of temporal variability and subsequent probabilistic recommendations.

Impact of crop residue removal on crop production, feedstock quality, and theoretical ethanol production in the Mid‐Atlantic United States

AbstractCellulosic biomass‐to‐bioenergy systems provide fuel, reduce emissions, and offer economic benefits. Corn (Zea mays L.) and wheat (Triticum aestivum L.) residues could be used as feedstocks for biofuel production. However, the impact of residue removal on crop productivity in the Mid‐Atlantic region has not been thoroughly assessed. A trial was conducted to assess crop yield and quality response to different biomass retention rates in grain cropping systems during 2015–2017. Various combinations of corn stover (0–10 Mg ha−1) and wheat straw (0–3 Mg ha−1) were applied in a corn–wheat/soybean [Glycine max (L.) Merr.] rotation in New Kent, VA. In Blacksburg, VA, corn stover (0–20 Mg ha−1) was applied in the continuous corn system. Residues were applied after grain harvest over two production cycles for each system. Residue retention showed no significant impact on grain or crop residue yields or nutrient uptake in either system. Treatment minimally impacted feedstock quality, except wheat straw's sulfur (S) concentration, optimized at around 70% retention in New Kent. Theoretical ethanol potential (TEP) and yield remained unaffected by total residue rates in New Kent. In Blacksburg, over 2 years, a minimum TEP for corn stover corresponded to a retention rate of approximately 30%. A retention rate of more than 30% increased TEP, likely due to improved feedstock quality. Nutrient replacement costs for primary macronutrients and S uptake ranged from $18.3 to $36.9 ha−1 for corn stover and $6.1 to $11.8 ha−1 for wheat straw. Residue harvest or addition did not harm short‐term biomass yield in Virginia's grain‐based cropping systems.

Phenological stages of wheat modulate effects of phosphorus fertilization in plant-soil microbial interactions

Abstract Aims Future phosphorus (P) fertilizer availability faces challenges due to limited phosphate rock mines and strict quality regulations regarding Cd contents in phosphate rock. In this study, conventional fertilization was partially substituted with meat bone meal (MBM), sludge (S), and the organo-mineral combination of S plus MBM (SMBM), in a wheat agroecosystem. Methods We investigated the impact of fertilization treatments and crop phenological stages on P availability, crop yield, and soil microbial responses. Analysis included enzyme activities, microbial biomass, and the composition of bacterial and fungal communities using metabarcoding. Additionally, we estimated functional genes related to the P cycle through qPCR. Crop yield and nutrient content in plants and soil were also determined. Results Replacing traditional fertilization with MBM and SMBM maintained crop yield at levels equivalent to conventional fertilization. S and SMBM produced 70% and 40% (respectively) more bioavailable P compared to conventional treatment (Trad). Significant differences between treatments in soil microbial biomass were observed in the flag leaf stage. S increased in 20% total soil microbial biomass compared to Trad. Crop phenology had a stronger impact on bacterial and fungal communities than fertilization treatments. The use of S enhanced microbial biomass and activity. Yield in both MBM and SMBM plots exhibited no statistically significant differences compared to traditional fertilization. Conclusion Organo-mineral fertilization emerges as a sustainable strategy for maintaining crop production while improving soil functionality. Our findings emphasize the primary influence of crop phenology on shaping soil microbial communities and influencing microbial biomass and functionality.

Closing the gaps in experimental and observational crop response estimates: a bayesian approach

Abstract A stylized fact of African agriculture is that crop responses to inorganic fertilizer application derived from experimental studies are often substantially greater than those from observational studies (e.g. surveys and administrative data). Recent debates on relative costs and benefits of expensive farm input subsidy programs in Africa, have raised the importance of reconciling these estimates. Beyond mean response differences, this paper argues for including parameter uncertainty and heterogeneity arising from variations in soil types, environmental conditions, and management practices. We use a Bayesian approach that combines information from experimental and observational data to model uncertainty and heterogeneity in crop yield responses. Using nationally representative experimental, survey, and administrative datasets from Malawi, we find that: (1) crop responses are low in observational data, (2) there are large spatial heterogeneities, and (3) based on sensitivity analysis, ignoring parameter uncertainty and spatial heterogeneity in crop responses can lead to questionable policy prescriptions.

Machine learning based on functional principal component analysis to quantify the effects of the main drivers of wheat yields

Assessing the response of crop yield to year-to-year climate variability at the field scale is often done using process-based models and regression techniques. Although powerful, these tools rely on strong assumptions and can lead to substantial prediction errors. In this study, we investigate the use of a flexible machine learning algorithm combining Functional Principal Component Analysis and Random Forest, to relate field scale wheat yield to local daily climate variables. Instead of computing seasonal, monthly or any other arbitrary time-frame climate averages, climate time series are decomposed by Functional Principal Component Analysis into a few data-driven basis functions, called Principal Curves, in order to summarize the dynamic of key climate variables by a limited number of interpretable components. Scores associated to these components are then used as inputs of a Random Forest algorithm for yield prediction and for analysing important factors responsible for yield variability. To evaluate our approach, we use a French national database including wheat yield data as well as climate and management practice data for 298 farm fields from 2011 to 2016 in four main producing regions. Depending on the regions, our approach can explain from 62 % to 81 % of the yield variability when both agronomic and climate variables are included, down to 56–81 % when ignoring agronomic variables and 51–74 % when ignoring climate variables. Based on a year-by-year cross-validation, RMSE ranges from 0.5 t ha−1 to 2.1 t ha−1 in non-extreme years (2012–2015). However, prediction error can reach 3.6 t ha−1 in case of exceptional weather conditions, such as those experienced in 2016 in Northern France. We find that this new approach performs in most cases better than the same machine learning algorithm using the usual time averages of climate variables, without the need to choose an arbitrary time-frame. We then show how important patterns in weather time series can be identified and how their effects on yield can be interpreted using the proposed modelling framework.

Herbicide effectiveness and crop yield responses in direct-seeded rice: insights into sustainable weed management

Herbicide effectiveness and crop yield responses in direct-seeded rice: insights into sustainable weed management

Do rotations and intercrops matter? Opportunities for intensification and diversification of maize-based cropping systems in Zambia

ContextRealizing the double-edged goals of food security and crop diversification require knowledge of crop yield response to different agricultural practices and the extent to which such response is context dependent. This is particularly important in Southern Africa where cropping systems are dominated by maize and smallholders use few external inputs. ObjectiveThe objectives of this study were (1) to evaluate the performance of cropping systems with different levels of legume diversification and intensification on maize productivity across the main agroecological zones and farming systems of Zambia, and (2) to establish the minimum land required to reach maize self-sufficiency at household level with the different cropping systems tested. MethodsSix maize-based cropping systems, comprising maize monocropping, maize-legume rotations and intercrops under ‘conventional’ tillage and conservation agriculture, were evaluated across 40 farms in contrasting agro-ecological zones and farming systems of Zambia. A household survey was conducted in the same communities hosting the on-farm trials to quantify the share of households with enough maize cultivated area to benefit from the tested cropping systems. ResultsMaize yield measured in the on-farm trials ranged, on average, between 3.6 and 5.4 t ha−1, which was greater than mean maize yields reported by farmers not hosting on-farm trials during the same growing seasons and in the same sites. Our results showed a clear positive effect of legume rotation on maize yield in Eastern province only, where cropping systems including groundnut as rotation crop yielded between 1.0 and 2.0 t ha−1 more maize than other cropping systems. Pigeon pea intercrops and Gliricidia hedgerows, crop residue management, and tillage practice had no significant effect on maize yield in any of the provinces. Finally, nearly 35, 50, and 70% of surveyed farms in Northern, Eastern, and Southern province would have enough land to achieve the same level of maize production obtained on their farm with the yields, and associated area requirements, of the maize-legume rotations tested in the on-farm trials. ConclusionLegume rotations can increase maize productivity on land abundant farms in high potential maize production areas of Southern Africa, whereas legume intercrops have benefits at cropping systems level and should be targeted to land constrained farms. SignificanceThe study contributes to better understand the niche for diversified maize-based cropping systems with legume rotations and intercrops in Southern Africa, along with their impacts on maize productivity at the field level and their land requirements at the farm level.

Characterization of High and Stable Yield Genotype for Maize based on Multivariate stability Analysis and Prediction of Crop Yield using Ensemble Learning Techniques

The production of maize globally surpassed the wheat and rice, because it is a staple crop in many regions of the world. Also, in addition to being directly consumed by people, maize is also used to make corn ethanol, animal food and several other products of maize, therefore the stability and high yield of maize is an extremely crucial part to promote long-term growth and food protection. This study looked at the effects of G ? E interaction on yield stability in 23 hybrids of maize in 53 distinct Indian environments. On the basis of analyses of variance, stability tests for multivariate stability parameters were carried out. In terms of all characteristics, the genotype and environment (G ? E interaction) differences were highly significant (p < 0.01), according to the pooled analysis of variance. A GGE biplot was created with the two principal components, which accounted for 66.02% and 8.51% variation in GEI for the corresponding yield per hectare. The GGE biplot and AMMI model exposed genotypes SYN916801, Bio 9682, DKC 9215, NMH 4313 as good with an indication of high mean yield and stability within the environment that were tested. By the results, we suggest breeding might boost output yield, and also the identified genotypes may be suggested for commercial farming. Also, for the creation of successful agricultural and food policy, reliable crop production estimates are essential. So, examinations were made on the machine learning techniques such as random forest and Gradient Boosting algorithm to predict crop yield responses in maize. While assessing statistical performance, GBM was found highly capable of predicting yield as compared to RF. Result showed that GBM is a good and adaptable machine learning technique for yield predictions.. KEYWORDS :Additive main effect and Multiplicative interaction, G ? E interaction, Machine learning techniques, Gradient boosting machine, Random forest.

An Intermittent Exposure Regime Did Not Alter the Crop Yield and Biomass Responses to an Elevated Ozone Concentration

The intermittent ozone (O3) exposure of crops to alternating high and low concentrations is common in fields, but its impact on crop production has not been thoroughly investigated. In this study, two widely planted and O3-sensitive crops, winter wheat and soybean, were intermittently exposed to elevated O3 concentrations in open-top chambers. The results showed that the winter wheat and soybean yields significantly decreased with O3 exposure (AOT40, cumulative hourly O3 concentration above 40 ppb) (p < 0.001). The relative yield losses were 0.99% per AOT40 for winter wheat and 1.2% per AOT40 for soybean, respectively. The responses of the crop biomasses to elevated O3 concentrations were lower than that of crop yield. Although the O3-induced crop yield and biomass losses under continuous O3 exposure were greater than those under intermittent O3 exposure, the differences were not statistically significant. Therefore, we can conclude that the effects of elevated O3 concentrations on crops are closely related to the exposure dose but not significantly related to the temporal distribution of elevated O3 concentrations. This study improves our understanding of how crop production responds to intermittent O3 exposure.

Can machine learning models provide accurate fertilizer recommendations?

Accurate modeling of site-specific crop yield response is key to providing farmers with accurate site-specific economically optimal input rates (EOIRs) recommendations. Many studies have demonstrated that machine learning models can accurately predict yield. These models have also been used to analyze the effect of fertilizer application rates on yield and derive EOIRs. But models with accurate yield prediction can still provide highly inaccurate input application recommendations. This study quantified the uncertainty generated when using machine learning methods to model the effect of fertilizer application on site-specific crop yield response. The study uses real on-farm precision experimental data to evaluate the influence of the choice of machine learning algorithms and covariate selection on yield and EOIR prediction. The crop is winter wheat, and the inputs considered are a slow-release basal fertilizer NPK 25–6–4 and a top-dressed fertilizer NPK 17–0–17. Random forest, XGBoost, support vector regression, and artificial neural network algorithms were trained with 255 sets of covariates derived from combining eight different soil properties. Results indicate that both the predicted EOIRs and associated gained profits are highly sensitive to the choice of machine learning algorithm and covariate selection. The coefficients of variation of EOIRs derived from all possible combinations of covariate selection ranged from 13.3 to 31.5% for basal fertilization and from 14.2 to 30.5% for top-dressing. These findings indicate that while machine learning can be useful for predicting site-specific crop yield levels, it must be used with caution in making fertilizer application rate recommendations.

Biochar effects on soil nitrogen retention, leaching and yield of perennial citron daylily under three irrigation regimes

Biochar can serve as a soil amendment to immobilize soil nitrogen (N) and reduce N leaching from cropland without negative effect on crop yield. However, the interaction effect of biochar application and irrigation regimes on soil N status (N retention and N loss) and crop yield is rarely reported in the open perennial vegetable field. A two-years field trial (transplanting in first year and consecutive growth in second year) was conducted in citron daylily vegetable cropping system on a sandy brown alluvial soil. Two biochar application rates (0 and 30 t ha−1) and three irrigation regimes (CDI, conventional drip irrigation; WSDI, water-saving drip irrigation with 80% of full irrigation quota; APRDI, alternate partial root-zone drip irrigation with 80% of full irrigation quota) were included. The response of crop yield and soil N status to both biochar application and irrigation regimes varied across planting years for perennial citron daylily. After the first planting year's harvest, APRDI enhanced flower bud yield by 18–28% compared to CDI and WSDI, likely due to improved nitrate use efficiency evidenced by lower soil nitrate retention in the surface soil (0–20 cm) post-harvest. However, biochar application resulted in a reduction of yield by 27% under APRDI. Additionally, WSDI with reduced yields, decreased soil TN in the sub-surface layer (20–50 cm) with 9–19% by comparison with other two irrigation regimes, resulting in higher TN concentration in the soil solution (14–28%) in and thus an increased risk for N leaching. However, after the second harvest year, there were no variations in crop yield induced by biochar application and irrigation regimes. Irrigation regimes exhibited limited influence on soil N status, while biochar application mitigated soil nitrogen decline in the 0–50 cm layer by enhancing organic nitrogen retention capacity for nearly 16–85%. Furthermore, the lowest TN concentration in the soil solution in the sub-surface layer (20–50 cm) with biochar application under APRDI suggested a reduced risk for N leaching. We conclude that combining biochar application with the APRDI regime could help retain soil N, decrease the risk of N leaching, and enhance crop yield in total for two consecutive planting years. Therefore, this approach is recommended for sustainable N management in long-term planting of perennial crops.

Assessing the impacts of climate change on crop yields, soil organic carbon sequestration and N2O emissions in wheat–maize rotation systems

Straw retention has been widely implemented to increase soil organic carbon (SOC) sequestration and greenhouse gas (GHG) mitigation for global agriculture. However, the combined effects of long-term straw retention on crop production, SOC sequestration and GHG emissions response to climate change remain unknown. Two nearby wheat–maize rotation field experiments in the North China Plain, combined with local weather, soil and agronomic measurements, were used to evaluate the applicability of the SPACSYS model. The model was then applied to assess the response of crop yield, SOC storage and nitrous oxide (N2O) emissions to three nitrogen fertilizer application levels (100, 200, 400 kg N ha−1) and three representative concentration pathway scenarios (RCP2.6, RCP4.5 and RCP8.5) under straw retention for the wheat–maize rotation systems during 2021–2100. In general, the model was reasonably accurate with R2 and model efficiency (EF) ranging from 0.25 to 0.96 and 0.32–0.94, respectively. Climate change decreased wheat yield by 4–39%, while maize showed a slight increase (3%) under RCP2.6 and a decrease of 1–15% under RCP4.5 and RCP8.5. The SOC storage in the 0–20 cm soil increased at a rate of 73–195 kg C ha−1 yr−1 but decreased within the upper 1 m soil at 280–390 kg C ha−1 yr−1. Climate change reduced the positive effect of SOC sequestration except in RCP2.6 and stimulated substantial N2O emissions ranging from 1–7.5 to 2.9–23.2 kg N ha−1 yr−1, consequently, the global warming potential increased from –79–2555 to 548–9357 kg CO2-eq ha−1 yr−1 under various N fertilizer application levels. This study reveals that the negative feedback under climate change with modelling approach, and extensive efforts are needed to make adaptations to ensure food production and reduce GHG emissions in the future.

Effect of plant architecture on the responses of canopy temperature and water use to population density in winter wheat

AbstractRevealing how plant architecture affects the responses of canopy temperature depression (CTD), water use (WU), and grain yield to population density (PD) in wheat (Triticum aestivum L.) would help explore a water‐saving pathway related to managing population. This study was conducted over three consecutive years under rainfed and supplemental irrigation conditions. The flat‐leafed Jinmai 47 and upright‐leafed Jing 411 genotypes were grown at high population density (HD), medium population density (MD), and low population density (LD); seeding rates were 450, 675, and 900 seeds m−2, respectively). CTD peaked at MD in Jinmai 47 but reached the highest at HD in Jing 411. WU generally increased with increasing PD but remained unchanged from LD to MD in Jinmai 47 under the two water conditions, while it remained unchanged from MD to HD under supplemental irrigation condition in Jing 411. Yield increased with increasing density in both genotypes under the two water conditions. It is thus clear that Jinmai 47 gained higher yield but did not consume more water at MD than at LD under the two water conditions; Jing 411 obtained higher yield at the expense of similar amount of water at HD than at MD under supplemental irrigation condition. The grain yield and WU responses of wheat crops to PD implied that through appropriately adjusting PD in combination with plant architecture and water condition, the dual goals of optimizing yield and effectively saving irrigation water could be realized synchronously.

Substantial Differences in Crop Yield Sensitivities Between Models Call for Functionality‐Based Model Evaluation

AbstractCrop models are often used to project future crop yield under climate and global change and typically show a broad range of outcomes. To understand differences in modeled responses, we analyzed modeled crop yield response types using impact response surfaces along four drivers of crop yield: carbon dioxide (C), temperature (T), water (W), and nitrogen (N). Crop yield response types help to understand differences in simulated responses per driver and their combinations rather than aggregated changes in yields as the result of simultaneous changes in various drivers. We find that models' sensitivities to the individual drivers are substantially different and often more different across models than across regions. There is some agreement across models with respect to the spatial patterns of response types but strong differences in the distribution of response types across models and their configurations suggests that models need to undergo further scrutiny. We suggest establishing standards in model evaluation based on emergent functionality not only against historical yield observations but also against dedicated experiments across different drivers to analyze emergent functional patterns of crop models.

Optimizing Nitrogen Fertilization for Barley Crop at Full and Deficit Irrigation in the Arid Region

Background: Nitrogen and water are two major limiting factors that affects the growth and yield of barley. Optimization of nitrogen application is crucial in enhancing barley yield and nitrogen use efficiency while minimizing the negative effects of its intense application on human health and the environment. However, the crop yield response to nitrogen application rates differ under different irrigation conditions owing to the complementary relationship between nitrogen and water. Therefore, it is vital to study the impact of different nitrogen application rates on improving barley growth and yield under full as well as deficit irrigation to select the best field management option, thereby enhancing sustainable agriculture. Methods: The present study investigated the impact of three nitrogen application rates [0 (control), 50 and 100 kgN/ha)] at two irrigation rates corresponding to 100% and 75% crop evapotranspiration (ETc) on the growth, yield and nitrogen use efficiency of barley. Stable isotopic technique was used to determine the nitrogen use efficiency. Result: The nitrogen application rate enacted a significant impact on plant height, number of plants/m2, number of spikes/m2, biomass yield and nitrogen use efficiency; whereas irrigation rates significantly affected the plant height, number of plants/m2, spike length, number of spikes/m2, biomass yield, grain and straw yield. In addition, a significant interaction between irrigation and nitrogen was observed for all the studied parameters except plant height, spike length, straw yield and total nitrogen uptake. The deficit irrigation 75% ETc with 50 kgN/ha presented the highest nitrogen use efficiency (37.56%), but the total biomass yield was reduced by 37% in comparison to 100% ETc irrigation. Thus irrigation and fertilization combination of 100% ETc with 50 kgN/ha was identified as the best irrigation and fertilization practice producing highest biomass yield, grain yield and straw yield in barley with a nitrogen use efficiency of 20.48%.

Model-averaging as an accurate approach for ex-post economic optimum nitrogen rate estimation

AbstractFinding economic optimum fertilizer rate with good accuracy is essential for optimal crop yield, efficient resource utilization, and environmental well-being. However, the prevailing incomplete understanding of input-output relationships leads to imprecise crop yield response functions, such as those for winter wheat, and potentially biased fertilizer choices. From a statistical point of view, there is uncertainity with regards to which model is most suitable to estimate the economic optimum fertilizer rate. This complexity is amplified when considering site-specific nitrogen fertilization, which factors into elements like soil attributes, topography, and crop variations within a field, as opposed to uniform application. This study undertakes a comparative analysis to evaluate biases, variance, mean squared errors and confidence intervals in Economic Optimum Nitrogen Rate (EONR) estimations across different functional forms. The goal is to uncover performance discrepancies among these forms and explore potential advantages of adopting model averaging for optimizing nitrogen use in crop cultivation. The results of simulations reveal noteworthy biases when comparing diverse yield functions with the averaged model, particularly evident in the Linear-Plateau and Mitscherlich models. Moreover, analysis of empirical data indicates that confidence intervals for the averaged model overlap with the projected ranges of all functions. This implies that the averaged model could be suitable for determining EONR and effectively address the problem of model specification without focusing on one specific functional form. The effectiveness of model averaging hinges on incorporating models that well approximate the true model. However, even if the true model is not known, the average model can provide reasonable information for determining the EONR, provided that similar model specifications are considered. This has implications for modelling of yield response for various applications and can contribute to unbiased estimations of yield response.

Response of Nitrification and Crop Yield to the Presence of NBPT and DCD in a Wheat-Corn Double Cropping System

The excessive application of nitrogen fertilizer aggravated the loss of nitrogen in farmland and exerted detrimental effects on the soil and water environment. Examining the effects of N-(n-Butyl)thiophosphoric triamide (NBPT) and nitrification inhibitor dicyandiamide (DCD) on nitrification and crop yield in wheat-corn double cropping systems would provide valuable insights for improving nitrogen efficiency and ensuring a rational application of inhibitors. A field experiment lasting one and a half years was performed in the winter wheat–summer maize double agroecosystem in North China. The four treatments that were applied included (I) conventional fertilization without inhibitors (CK), (II) conventional fertilization with 0.26 g/m2 NBPT (NBPT), (III) conventional fertilization with 1.00 g/m2 DCD (DCD), and (IV) conventional fertilization with 0.26 g/m2 NBPT and 1.00 g/m2 DCD (NBPT + DCD). The results demonstrated that the combined use of NBPT and DCD exerted better effects in reducing NO3−-N leaching. Nitrification could be inhibited for up to 95 days by combining NBPT and DCD, while 21 days by DCD. Ammonia-oxidizing archaea (AOA) (R2 = 0.07159, p < 0.01) along with ammonia-oxidizing bacteria (AOB) (R2 = 0.09359, p < 0.01), rather than a complete ammonia oxidizer (comammox), were significantly and positively correlated with NO3−-N content, which indicated that the ammoxidation process was mainly regulated by AOA and AOB, instead of comammox in the winter wheat–summer maize double agroecosystem in North China.

Mehlich 3 as an indicator of grain nutrient concentration for five cereals in sub-Saharan Africa

Context or ProblemSoil testing for available nutrients is an important tool to determine fertilizer rates, however many standard methods test the availability of a single nutrient only. In contrast, Mehlich 3 (M3) is a multi-element test for predicting crop yield responses to the addition of macro and micronutrients. However, the M3 test has rarely been validated against crop nutrient concentrations, which limits its application for dietary improvement studies in sub-Saharan Africa. Objective or Research QuestionThe primary objective was to test how well the M3 nutrient concentrations corresponds to grain nutrient concentrations as an indicator of plant nutrient status and grain quality. A secondary objective was to compare the performance of the M3 test with other extraction tests. MethodsThis study used 1096 paired soil and crop samples of five cereals: maize, rice, sorghum, teff and wheat, covering a broad range of soil types and soil properties in Ethiopia and Malawi (e.g., pH 4.5 - 8.8; Olsen P < 1 - 280 ppm). The samples were selected from a larger collection based on “high” or “low” grain nutrient concentrations in the crop, and the respective soil available nutrients were measured with M3 and other extraction tests: CaCl2 (P, K, Mg, Mn), Ca(NO3)2 (K and Mg), Olsen P, sequential extraction (S), and DTPA (Mn, Fe and Zn). ResultsThe M3 concentrations followed the trend of the “high” and “low” grain concentrations in nearly all nutrients and crops, and this was statistically significant in teff and wheat for all nutrients. The results were best for macronutrients, and slightly less good for micronutrients, probably because the concentration of micronutrients in the selected soil samples was generally quite low. Compared to the other multi-element extractant (CaCl2), the M3 test corresponded better to grain concentrations of K and Mg, and equally well to Olsen P, sequential extraction (S), and DTPA predictions of P, S, Zn and Fe, respectively. M3 extracted much greater concentrations than the other tests, and this was more pronounced in alkaline soils. ConclusionsGiven that the M3 test corresponded well to grain nutrient concentrations across a range of soils and crops in sub-Saharan Africa (SSA), we conclude that it can be considered a universal test for plant nutrients. We also proposed thresholds for M3 values, defining below optimum, optimum and above optimum soil fertility status. Implications or SignificanceThese results validate the use of the M3 test to assess soil fertility and develop fertilizer recommendations for improved produce quality to enhance diets in SSA.

Relative and Unified Skill of Environmental, Edaphic, and Management Factors to Explain Crop Yield Variance Using Machine Learning

Highlights Random forests equipped with recursive feature elimination were used to predict crop yield variation using diverse predictors. The predictors belonged to either environmental, soils, or management domains. The best performing models combined predictors from all three domains and explained 75%-80% spatiotemporal yield variance. Extreme heat was most important among soils, management, and seasonal environment predictors in maize and soybean. Irrigation intensity, tile drainage, % silt, and July rain were most important at the sub-seasonal aggregation scale. Abstract. Crop yields are dictated by a complex interplay between environment, edaphic (soils), and management that are subject to change across space and time. However, to what extent each of these influences and their interactions have been important in explaining yield variance is limitedly understood. The convoluted nature of this question motivates the application of modern machine learning approaches to decipher these influences and elucidate crop yield relations with their critical drivers. Here, we used random forest modeling with recursive feature elimination (RFE) to discern the diverse drivers of historical (1981-2019) county-level maize and soybean yields in the U.S. within the realms of environment (growing and extreme degree days, precipitation, vapor pressure deficit, evaporative demand, crop water use, soil moisture), soils (sand, silt, and clay contents, bulk density, soil organic carbon, available water capacity), and management (irrigation intensity, tile drainage, nitrogen input, depth to groundwater). We found that the most effective models selected predictors from all three realms and achieved remarkable explanatory capability, accounting for 75%-80% of the spatial and temporal variance combined. Environmental predictors exhibited a non-negotiable role in determining model performance, while their combination with either soil or management predictors approached the efficacy of the best-performing model. Specific variables within each individual predictor set and their combinations were analyzed for their relative importance to the model skill. The best performing model that used soils, management, and seasonal environmental predictors evaluated extreme heat as the most influential predictor for both crops. On further inclusion of sub-seasonal environmental variables with soil and management predictors, the relative importance shifted, with irrigation intensity assuming prominence as the most influential predictor, accompanied by tile drainage, silt content, and July precipitation for both crops. RFE revealed that only eight of the most relevant predictors were sufficient to explain &amp;gt;70% of the yield variance, even when the total predictors included were as high as 52. that Visual representations were developed to offer insights into the functional response of crop yield to changes in critical predictors, thereby facilitating predictions across diverse agricultural production systems and over time. This research underscores the value of including soil and management indicators alongside environmental predictors to improve understanding and predictability of the intricate dynamics governing crop yield variability. Keywords: Climate variability, Drainage, Irrigation, Maize, Nitrogen, Random forest, Soils, Soybean.