Variability Of Soil Fertility Research Articles

Plant organ rather than cover crop species determines residue incorporation into SOC pools

The implementation of cover crops has emerged as a promising approach to improve soil organic carbon (SOC) stocks, with particular emphasis on the perceived higher carbon use efficiency displayed by high-quality residues such as from leguminous plants. In this study, we explored how different cover crop residues, specifically from a legume and a grass cover crop, affects SOC formation and its distribution across various soil carbon pools. Over a 7-month period, we incubated 14C-labeled winter rye and hairy vetch residues in microcosms containing soils of varying soil fertility levels from a long-term field trial. We tracked the fate of carbon into free and occluded particulate organic matter (fPOM, oPOM), mineral-associated organic matter (MAOM), and carbon deposited outside the detritusphere.Despite notable differences in C:N ratio, chemical composition, and turnover rate, similar SOC formation efficiency between vetch and rye within each plant organ (shoots and roots) was observed. Interestingly, the plant organ appeared to exert a greater influence on the fate of cover crop carbon than whether the crop was leguminous or non-leguminous. This phenomenon seemed to be closely related to the lignin content.At medium soil fertility, we found that the largest proportion of cover crop residue C remained as MAOM (20% for shoots, 15-18% for roots), followed by fPOM (5-6% for shoots, 10-12% for roots) and oPOM (2.7-3.0% for shoots, 1.5-1.6% for roots). Notably, fPOM and oPOM exhibited opposite responses to residue quality, indicating functional distinctions between these often-pooled POM pools.Soil fertility exerted minimal influence on overall respiration rate patterns or SOC formation, although it did affect oPOM formation efficiency, likely due to differences in soil aggregation.In conclusion, our findings challenge the assumption regarding the superiority of N rich leguminous cover crop residues for enhancing SOC accrual in C pools believed to have longer persistence.

Calibrated SoilOptix ® estimates of soil pH and exchangeable cations in three agricultural fields in western Canada – implications for managing spatially variable soil acidity

Spatial variability in soil pH is a major contributor to within-field variations in soil fertility and crop productivity. An improved understanding of the spatial variability of soil pH within agricultural fields is required to determine liming requirements for precision farming. This study with the use of proximal sensors, firstly assessed the spatial pattern of soil pH and how it can be used to determine site-specific, spatially variable lime requirements. Secondly, the effects of soil pH on soil concentrations of nitrate nitrogen (N03-N), phosphorus (P), potassium (K), sulfur (SO4-S), calcium (Ca), magnesium (Mg), soil organic matter (SOM), aluminum (Al), and manganese (Mn) were assessed in three study fields in central Alberta, Canada. Soil pH varied between 4.5 and 7.5 across all field sites. The field-scale coefficient of variation (CV %) for soil pH, Al and Mn ranged between 4.39 and 7.50 %, 7.33–13.72 %, and 7.33–13.72 % across the three sites. The other soil properties showed low, moderate, and high variability, with field-scale CVs ranging between 6.39 and 17.70 % for SOM and 24.33–91.39 % for SO4-S. Soil pH exhibited positive correlations with both Ca and Mg, across all fields. Negative correlations were observed between soil pH and Al across all fields. A principal component analysis (PCA) was performed for all soil parameters and two principal components accounted for 50 %, 54.9 %, and 76.8 % of the total variance in field 1, field 2, and field 3, respectively. Geostatistical semivariance indicated a strong spatial dependence of all chemical parameters across fields. Large regions within a field were strongly acidic (pH < 5.5) and required lime applications ranging from 0 to 6 t ha−1. We conclude that proximal soil sensors can be calibrated to soil properties, enabling variable rate lime recommendations on spatially variable fields for the management of soil acidity.

Organic management pattern improves microbial community diversity and alters microbial network structure in karst tea plantation

Soil microbiomes play a crucial role in enhancing plant growth, health, and overall agricultural productivity. Nevertheless, the influence of distinct agricultural management practices on the microbial diversity and community structure within tea (Camellia sinensis) plantations has remained enigmatic. This study postulates that organic agricultural management models can enhance microbial diversity and optimise the microbial community structure within tea plantations, indirectly augmenting soil fertility and tea quality. We employed metagenome technology and conducted molecular ecological network analysis to explore the impact of organic management, pollution-free management, and conventional management on the microbial network structure of tea plantation soil in Weng'an County in the southwestern karst region. Soils subjected to organic management exhibited a higher relative abundance of soil microbial and carbohydrate-active enzyme functional genes than those subjected to other management regimes. Additionally, the relative abundance and diversity of dominant bacteria and keystone species were notably higher under organic management than under the other management regimes. Correlation analysis showed that soil microorganisms were closely related to soil fertility and tea quality, respectively. One-way analysis of variance and the structural equation modelling results showed significant variability in soil fertility under the three agricultural management modes and that soil fertility and soil microbial diversity had a direct impact on tea quality (P > 0.05). In conclusion, this study underscores the profound impact of management modes on microbial diversity and community structure within tea plantations. These management practices alter the soil microbial network structure and potential function, ultimately regulating the microecological dynamics of the soil community in tea plantations.

Spatial variability of soil fertility under different land use Types in Assosa District, Western Ethiopia

Spatial variability of soil fertility under different land use Types in Assosa District, Western Ethiopia

Optimizing Nutrient Recommendations for Bottle Gourd Crop by Formulation of Targeted Yield Equations in an Inceptisol of Odisha, India

A field experiment was conducted to formulate targeted yield equations for bottle gourd under a rice-vegetable cropping system in an Inceptisol of Odisha. The experiment started with the creation of three soil fertility gradient stripes by applying no fertilizer, recommended dose and double the recommended dose of fertilizer in rice (cv. Lalat) during the kharif season. Each fertility gradient strip was divided into 24 sub-plots and superimposed with 21 different combinations of nutrients containing N, P, and K; FYM in two plots at 5t and 10t ha-1 and one plot was kept as absolute control following which, bottle gourd was grown during the rabi season. The highest yield (116.3q ha-1) of bottle gourd was achieved in S-III with an application of 70, 100, and 100 kg N, P2O5, and K2O per hectare, respectively. The nutrient requirement (NR) for producing one quintal of bottle gourd yield was 0.30, 0.20, and 0.30 kg N, P2O5 and K2O, respectively. The effect of graded doses of fertilizers on nutrient requirement, yield and nutrient uptake of bottle gourd were studied and subsequently, fertilizer prescription equations were derived for targeted yield of bottle gourd. A ready reckoner chart has also been prepared for facilitating farmers to achieve the desired yield target of bottle gourd by applying the required quantity of plant nutrients in the existing soil fertility level. The equations provide a basis for site-specific nutrient management based on desired yield targets under varying soil fertility conditions.

Investigation of yield-limiting nutrients for rain-fed lowland rice production in the Fogera floodplain of the Amhara Region in Northwest Ethiopia

The productivity of rice (Oryza sativa L.) in Ethiopia is below the potential due to spatial and temporal soil fertility variability and sub-optimal fertilization. This on-farm study was conducted in 2019 and 2020 to determine yield-limiting nutrients, quantify yield gaps, and determine indigenous soil nutrient supply (ISNS) for lowland rice in Fogera floodplain of Northwest Ethiopia. The study comprised the following treatments; NPKSZnB, -N (PKSZnB), -P (NKSZnB), -K (NPSZnB), -S (NPKZnB), -Zn (NPKSB), -B (NPKSZn), and Zero fertilizer (F0). The treatments were arranged in a randomized complete block design considering farmers’ fields as replications. The results revealed that the growth and yield of rice were significantly affected by the -N and F0 treatments. However, the other nutrient omissions did not significantly influence the mentioned parameters. The highest (2299.5 kg ha−1) yield response was recorded from the -N treatment. However, very low and insignificant yield responses were recorded from the other nutrient omissions. The average agronomic N use efficiency was 16.7 kg kg−1, which indicated a high probability of yield response to the application of N fertilizer. The lowest (42.4%) ISNS was found for N, while the highest ISNS (>90%) were recorded for P, K, S, Zn, and B. The yield gaps due to the omission of N was 57.6%, while the yield gaps due to the omission of P, K, S, Zn, and B were very low. Therefore, N was found to be the most yield-limiting and most deficient nutrient for the lowland rice production in the study area.

Determination of Optimal Plant Density of Adapted Maize Varieties in the Low and Mid-altitude Ecologies of Tanzania

Purpose: The objective of this study is to validate the commonly recommended plant density in the target zone to maximize maize production and productivity. The commonly recommended plant density is 44,444 plants/ha for decades. However, there is no recent study done to validate this practice if it optimizes productivity under current climate and soil fertility variability. A blanket recommendation has been used for decades without validation for optimum productivity that farmer can realize. &#x0D; Methodology: The factorial experiment was used with the total of 18 treatment combinations in a Randomized Complete Block Design (RCBD) with three replications according to Gomez, A and Gomez, K (1983). The open pollinated varieties (OPV) commonly grown by small scale farmers were planted in three different plant densities of 44,444, 53,333 and 66,666 plants per hector with inter and intra row spacings of 0.75mx0.3m, 0.75mx0.25m and 0.75mx0.2m respectively. Field management was done as usual following the recommended agronomic practices.&#x0D; Findings: Analysis of variance (ANOVA) was done using GENSTAT (9th edition) statistical programme software and presented in a tabular form. The results indicated no significant difference in all parameters analysed except for 50% anthesis days (AD) and 50% Silking days (SD) both at 0.1% levels. The overall site mean for grain yield was found to be 6.43t/ha, 7.31t/ha and 6.11t/ha for the plant density of 44,444plants/ha, 53,333plants/ha and 66,666plants/ha respectively. Therefore, based on these findings the optimum plant density was found to be 53,333/ha unlike the commonly used plant density of 44,444 plants/ha.&#x0D; Unique Contribution to Theory, Practice and Policy: This study contributes to existing experience and practice commonly recommended by extension officers in many parts of the country without performing a site-specific study to determine the optimum spacing for particular environment. This, study also open up an avenue for further research to validate various research technologies. Plant population, in addition to plant height, maturity class, and row spacings, affects the geometry of plant spacing and leaf distribution. All these factors may affect light and CO2 penetration in the canopy, as well as water use rate (Tharp, B and Kells, J, 2001)

The physical, mechanical, and chemical properties of black cherry tree wood (Prunus serotina Ehrh.) and its susceptibility to fungal decomposition in areas where it is secondary and invasive: a case study in the Kampinos National Park (Poland)

The properties of black cherry mature wood (Prunus serotina Ehrh.) and its susceptibility to fungal decay were studied in the invaded range of the species on six trees aged between 39 and 47 years old growing in poor, acidic soils with varying levels of moisture and organic carbon and nitrogen content. Wood from trees that grew in wetter and richer soil had better physical properties. Of the 95 parameters analyzed, 80 showed significant differences in favor of this wood. These differences included wider rings that averaged 3.25 mm, a higher density of 662.71 kg/m3 at 12% humidity, and 1.5 times higher content of extractives. Gas chromatography with mass spectrometry revealed the presence of 44 extractives. Out of these, six had antifungal properties and were found in the wood of trees grown in richer soil, corresponding to 62.93% of the peaks area of all identified substances. Only three were found in trees grown in poorer soil, corresponding to 8.68% of the peaks area respectively. The wood of trees grown in more fertile soil was also less prone to fungal decay, which was generally low. Only Trametes versicolor caused a mass loss of more than 10% of the wood in both sites out of the four basidiomycete species tested. The results indicate that even slight variations in soil fertility and moisture can benefit black cherry, leading to differences in wood features and properties in its exotic range.

Application of geoinformation systems in the agricultural complex

Precision farming, encompassing coordinated farming, adaptive landscape farming, and precision farming, represents a scientific paradigm in agricultural production that capitalizes on the inherent variability in soil fertility within localized areas. By tailoring practices to specific soil characteristics, precision farming optimizes profitability through the targeted application of fertilizers and plant protection products. Serving as the epitome of flexible landscape farming, precision farming integrates advanced agricultural technologies to achieve heightened productivity levels. Data collected through precision farming practices informs critical aspects of agricultural management, including strategic planting, precise calculation of input quantities, improved crop forecasting, and enhanced financial planning. Successful implementation of precision farming hinges on a thorough consideration of local soil attributes and climatic conditions, underscoring the importance of site-specific adaptation in modern agricultural strategies.

Soil test crop response based site specific integrated nutrient management in mungbean

Imbalanced and excessive use of chemical fertilizers has brought many soil fertility problems. Soil test crop response (STCR) approach aims to estimate precise fertilizer requirement for target yield under varying soil test values and gaining prominence for site-specific nutrient management. A field experiment to estimate STCR based fertilizer requirement for targeted yield of mungbean was conducted during 2019–2020 at G.B. Pant University of Agriculture and Technology, Pantnagar, India. Three fertility gradient plots of varying soil fertility levels were created by applying graded dose of N, P2O5 and K2O and growing wheat as an exhaust crop. Each of three soil fertility gradient plot was divided into 24 subplots (21 plots with different combinations of varying levels of N, P2O5, K2O and FYM, remaining 3 plots as controls) and mungbean was grown as main crop. Results revealed the requirement of 6.72, 0.883, and 3.52 kg of N, P and K, respectively for producing one quintal of mungbean grain. Soil, FYM, fertilizer and fertilizer along with FYM contributed 28.06%, 5.94%, 113.22%, and 135.01% for N; 24.73%, 3.18%, 15.1%, and 18.85% for P; 13.19%, 7.21%, 39.52%, and 55.84% for K, respectively. This information is used to develop STCR based fertilizer prescription equations along with ready reckoners of fertilizer doses at different soil fertility levels and yield targets of mungbean for providing balanced fertilization. The maximum response of mungbean in terms of grain and protein yield was obtained with the application of 20:50:30 kg ha−1 N: P2O5: K2O, respectively.

Spatial-temporal variability in under-canopy soil fertility and nutritional contents of cashew trees in Makonde Plateau of southeastern Tanzania

This study was conducted out to evaluate the soil fertility in under the cashew tree canopy and nutritional contents of cashew trees on the Makonde Plateau in southeast Tanzania. Seven villages were included, all of which were geographically close to one another yet had the same agro-ecology. The cashew plant samples were taken above the same longitudinal segment as the soil samples, which were taken in under canopies of cashew trees. Nitrogen, Phosphorus, Potassium, Magnesium, Calcium, and micro nutrients (Iron, Zinc, Sodium, and Copper) were all examined in the samples. However, soils were analysed for Organic Carbon, Organic Matter, and pH with soil samples taken at two different depths of 0–30 cm and 30–50 cm, these tests were conducted during the wet and dry seasons. The results confirmed that Calcium, Magnesium, Sodium, and Iron varied significantly with soil depth, as well as with soil depth, seasons, and their interactions, for Organic Carbon and Organic Matter. During the wet season, Nitrogen, Phosphorus, and Zinc concentrations in cashew trees were all statistically higher. Nitrogen and Phosphorus were significantly strong and positively (r = 0.95) correlated in cashew plants with respect to all other nutrients, suggesting synergistic effects. These results imply that macro nutrients including Nitrogen, Phosphorus, Potassium, Magnesium, and Calcium, and micronutrients such as Iron, Zinc, Sodium and Copper limit cashew production in the research area. It is necessary to determine site-specific recommendations and dosages for these nutrients.

Spatial Analysis of Soil Fertility Using Geostatistical Techniques And Artificial Neural Networks

Information on the spatial variation of soil fertility attributes is an essential input for precision agriculture and soil management decision making. In this study, soil fertility assessment was carried out through the spatial distribution of thematic maps of individual properties and the subsequent integration into a digital mapping model of local fertility classes, as fundamental bases for the implementation of fertilization and amendment plans adjusted to soil status and crop requirements. For the evaluation of fertility, a systematic surface sampling was carried out in 70 sites in the "Agronomy" production field of the National University of the Central Plains "Romulo Gallegos", El Castrero sector, Juan German Roscio municipality, Guárico state, Venezuela. Ten soil variables were analyzed: pH (1:2.5), electrical conductivity (1:5), organic matter, available phosphorus, assimilable potassium, available calcium and magnesium, and the relative amounts of sand, silt and clay. Soil property maps were produced by geostatistical analysis and interpolation by ordinary kriging, and artificial intelligence techniques based on an artificial neural network classification system were applied to generate soil fertility classes using the Fuzzy Kohonen Clustering Network (FKCN) algorithm by interpolating the values of the membership function for each of the classes. The reliability of the individual maps of each soil variable was obtained by cross validation with a reliability level higher than 90%, with the exception of the variables % Clay and % Silt that presented a reliability higher than 85%. The integration of the soil attribute maps and the combination of the values of belonging to each class produced a map integrated by five soil fertility categories. The final model of digital soil fertility classes presented a reliability equivalent to 86%, which indicated a high degree of homogeneity within the soil classes obtained for fertility purposes.

Soil Bacterial Community Structure and Function Prediction of Millet/Peanut Intercropping Farmland in the Lower Yellow River

The objective of this study was to explore the microecological variability in farmland soil fertility in response to millet-peanut intercropping patterns by clarifying the effects of millet-peanut 4:4 intercropping on soil bacterial community structure and its diversity, as well as to provide a reference basis for promoting ecological restoration and arable land quality improvement in the lower Yellow River farmland. The Illumina MiSeq high-throughput sequencing technology and QIIME 2 platform were used to analyze the differences in bacterial community composition and their influencing factors in five soils[sole millet (SM), sole peanut (SP), intercropping millet (IM), intercropping peanut (IP), and millet-peanut intercropping (MP)] and to predict their ecological functions. The results showed that the α-diversity of intercropping soil bacterial communities differed from that of monocropping, though not significantly, whereas the β-diversity was significantly different (P<0.05). A total of 7081 ASVs were obtained from all soil samples, classified into 34 phyla, 109 orders, 256 class, 396 families, 710 genera, and 1409 species, of which 727 ASVs were shared, accounting for 24.5% to 27.8% in five soil species. The bacterial communities of millet-peanut intercropping and its monocropping soils were similar in phylum composition but varied in relative abundance. All five soils were dominated by the Actinobacteria, Proteobacteria, Acidobacteria, and Chloroflexi, with a relative abundance of 79.40%-81.33%. Soil organic carbon and alkaline nitrogen were the most important factors causing differences in the structures of the five soil bacterial communities at the phylum and genus levels, respectively. The PICRUSt functional prediction revealed that the relative abundance of primary functional metabolism was the largest (78.9%-79.3%), and the relative abundance of secondary functional exogenous biodegradation and metabolism fluctuated the most (CV=3.782%). In terms of the BugBase phenotype, the relative abundance of oxidative stress-tolerant bacteria increased in intercropping millet or peanut soils compared to that in the corresponding monocultures and significantly increased in intercropping millet soils compared to that in sole millet (P<0.05). Oxidative stress-tolerant, Gram-positive, and aerobic phenotypes were highly significantly positively correlated with each other (P<0.01), and all three showed highly significant negative correlations with potential pathogenicity and Gram-negative and anaerobic phenotypes (P<0.01). This showed that millet-peanut intercropping resulted in differences in soil bacterial community diversity, abundance, and metabolic functions and the possibility of reducing the occurrence of potential soil diseases. It can be used to regulate the soil microbiological environment to promote ecological restoration and sustainable development of farmland in the lower Yellow River.

Cultivation of Cassava (Manihot Esculentus C.) With Selected Legumes for Growth, Yield and Economic Advantages as Climate Change Mitigations

Cassava (Manihot esculentus Crantz.) is a monoecious perennial shrub that is extensively cultivated as an annual crop in tropical and subtr++=opical regions for its edible, starchy, tuberous roots. It is ideal for intercropping with short-duration crops, which are often harvested before the cassava canopy closes. Thus, intercropping cassava, especially with legumes, is an important way for poor farmers to provide additional crop yield during the early growth stage. Moreover, the incorporation of legumes into cassava-based cropping systems may offer one of the most feasible ways of enhancing protein intake and nutritional security of farming households. Accordingly, a 1-year field study on cassava intercropped with three legumes (haricot bean, mung bean, and soybean) in southern Ethiopia was conducted to determine the effect of intercropping on the growth, yield, and economic advantage of the cassava. The experiment was laid out in a randomized complete block design (RCBD) with three replications. The results revealed that there were significant (P&lt;0.05) differences for cassava plant height, leaf number, stem width, number of storage roots per plant, storage root weight, dry matter, and root yield due to cassava legume intercropping. Cassava- haricot bean intercropping increased root yield by 28% as compared to cassava-mung bean (21%), and cassava-soybean (18 %), respectively. And it can improve land-use efficiency by 31% as compared to cassava-soybean and cassava-mung bean intercropping. The partial budget analysis also revealed that cassava planted with haricot bean gave the highest economic benefit of 199,250 Birr ha-1. Further researches on relative planting time of legumes with cassava and soil fertility variation under sole cassava and intercropping systems are important for improving productivity of growers under the study area.

Assessing Spatial Variation and Driving Factors of Available Phosphorus in a Hilly Area (Gaozhou, South China) Using Modeling Approaches and Digital Soil Mapping

Soil fertility plays a crucial role in crop growth, so it is important to study the spatial distribution and variation of soil fertility for agricultural management and decision-making. However, traditional methods for assessing soil fertility are time-consuming and economically burdensome. Moreover, it is hard to capture the spatial variation of soil properties across continuous geographic space using the conventional methods. As key techniques of digital soil mapping (DSM), spatial interpolation techniques have been widely applied in soil surveys and analysis in recent years, since they can predict soil properties at unknown points in continuous space based on limited sample points. However, further research is needed on spatial interpolation models for DSM in regions with variable climates and complex terrains, which are characterized by strong spatial variation in both environmental variables and soil fertility. In this study, taking a typical hilly area in a subtropical monsoon climate, i.e., Gaozhou, Guangdong Province, China, as an example, the performances of four popular spatial interpolation models (Random Forest (RF), Ordinary Kriging, Inverse Distance Weighting, and Radial Basis Function) for digital soil mapping on available phosphorus (AP) are compared. Based on RF, the spatial variation and its driving factors of the AP of Gaozhou are then analyzed. Furthermore, by selecting three typical truncation lines from different directions, the correlations between environmental variables and AP in different spatial positions are demonstrated. The root mean square error (RMSE) results of the above four models are 32.01, 32.08, 32.74, and 33.08, respectively, which indicate that the RF has a higher interpolation accuracy. Based on the mapping results of RF, the minimum, maximum, and mean values of AP in the study area are 38.90, 95.24, and 64.96 mg/kg, respectively. The high-value areas of AP are mainly distributed in forested and orchard areas, while the low-value areas are primarily found in urban and cultivated areas in the eastern and western regions. Vegetation and topography are identified as the key factors shaping the spatial variations of AP in the study area. Furthermore, the spatial heterogeneity of the influence strength of altitude and EVI is revealed, providing a new direction for further research on DSM in the future, i.e., spatial interpolation models considering the spatial heterogeneity of the influence of environmental variables.

Improving spatial predictions of Eucalypt plantation growth by combining interpretable machine learning with the 3-PG model

Accurate predictions of forest plantation growth provide forest managers with improved forest inventory estimates, forest valuation, and timely harvest schedules. Forest process-based models are increasingly used for quantifying current and potential productivity, yield gaps, and factors limiting growth, such as climate variability, soil characteristics, and water deficit. Improvements in the availability and resolution of spatial and temporal data combined with advancements in machine learning algorithms provide new opportunities to improve model predictions. This study shows how interpretable machine learning (ML) can be used to independently predict site soil fertility rating (FR) and incorporate these results into the 3-PG forest process-based model to accurately predict plantation growth. Four ensemble decision tree machine learning models—random forest trees, extremely randomized trees, gradient boost, and XG boost—were trained and compared using spatial cross-validation across the study area. FR predictions were estimated in relation to the influencing soil type and terrain characteristics, and interpretable ML methods were used to show how input feature permutations may relate to the soil fertility predictions. The results show the explanatory variables are similar across the selected ML models, with the strongest influencing variables being water leaching index, site aspect, and the silt and sand soil texture properties. The extremely randomized tree models showed the overall best performance, with only a small variation in performance across the four ML models. The method was applied to Eucalyptus nitens plantations covering over 63,000 ha in north-west Tasmania, Australia. The results using the predicted FR spatial grid with 3-PG show a strong correlation with observed growth for tree diameter and stand volume (R2 tree diameter at breast height = 0.97, RMSE = 0.85 m; R2 stand volume = 0.96, RMSE = 23.1 m3 ha−1) obtained from 161 permanent sample inventory plots ranging from 3 to 31 years old. This method has practical utility for other study sites to calibrate forest plantation soil fertility rating, in both the spatial and point-scale 3-PG model, where spatial data of soil characteristics are available. The derived soil fertility grid can provide valuable insights into the spatial variability of soil fertility in unknown areas.

A comparative study on single and multiple trait selections of equatorial grown maize hybrids

Maize (Zea mays L.) production in tropical equatorial regions faces significant challenges due to agroclimatic and soil fertility variability, necessitating the evaluation of maize hybrid adaptability and phenotypic stability across diverse agroecosystems. This study compares the effectiveness of the additive main effects and multiplicative interaction (AMMI) and multi-trait genotype-ideotype distance (MGIDI) models for identifying superior maize hybrids well-suited to the equatorial climate. Fifteen genotypes, including 13 hybrid candidates and two popular commercial varieties (BISI 2 and NASA 29), were analyzed in 10 distinct environments in Indonesia over three consecutive years (2018–2020). The ANOVA method used in the AMMI model analyzed variance into three major components, with PCA analysis indicating that environments (E), genotypes (G), and their interaction (G × E) had a highly significant effect on yield (p &amp;lt; 0.001). Two hybrids, HM04 (CI301032/G102612) and HM02 (CI272022/G102612), displayed high adaptability and stability across various environments, with significantly higher yields than the grand mean by AMMI analysis. Additionally, HM10 (MAL03/CLYN231) and HM09 (G102612/CLYN231) were narrowly adapted to the ME-1 and ME-2 mega-environments, indicating they are best suited for these specific environments. Similar to AMMI, the MGIDI model suggested HM04 (MGIDI index = 1.74) and HM02 (MGIDI index = 1.76) as the two highest-performing hybrids, determined by their yield and nine other traits. Using the multiple trait combination index as a tool to assess the performance of these hybrids enabled researchers to determine the most effective traits for each genotype. The two models are recommended and may be integrated for comprehensive data interaction analysis, which simplifies the process of delineating genotypes with the environment and enables stakeholders to select desired traits while considering their strengths and weaknesses.

Effects of Organic and Inorganic Fertilizers on Soil Nutrient Conditions in Rice Fields with Varying Soil Fertility

The majority of crop-growing areas in China have low or medium fertility levels, which limits the yield of crops grown in those areas. Fertilizer application can improve soil quality, but the effects of such treatments vary depending on the base soil fertility. However, the specific differences associated with the application of different fertilizer types to soils of varying fertility levels have yet to be clearly delineated. Here, the influences of several fertilizer types on physical, chemical, and biological soil indicators were assessed in rice fields in the red soil area of Hunan Province with varying base fertility levels: Hehua (low fertility), Dahu (medium fertility), and Longfu (high fertility). Four treatments were applied to these fields: no fertilizer, standard fertilizer, 60% chemical fertilizer + 40% organic fertilizer, and 100% chemical fertilizer. Across the three sites and treatment groups, the largest increases in total nitrogen and phosphorus contents were in Hehua and Longfu, respectively. Soil organic matter content increased most significantly in Hehua. Application of any type of fertilizer increased the total and fast-acting nutrient content in the low-yielding fields, whereas organic fertilizers increased the nutrient content and soil biological indicators more than chemical fertilizer alone did; the effect of organic fertilizer application on the combined enzyme activity of the soil was also higher than that of chemical fertilizers alone. Overall, these experiments provide a theoretical basis and technical support for rational fertilizer application and improvement of Hunan’s red soil quality based on the natural soil fertility levels.

Evaluating the use of nitrogen and phosphorous fertilization as crop management options for maize adaptation to climate change in the Nigeria savannas

Poor soil fertility and climate variability are major constraints to maize production in the Nigeria savannas. The application of nitrogen (N) and phosphorus (P) as adaptation strategy may enhance maize yield under climate change. In this study, the already calibrated and validated CERES-maize model in DSSAT was used to simulate the response of maize varieties to N and P in three agroecological zones. Similarly, the model, coupled with data for representative concentration pathways (RCP4.5 and RCP8.5) scenarios, was applied to simulate maize yields for mid-century and end-of-century periods and to estimate the effect of use of N and P as a strategy for maize adaptation to climate change. Results of a 30-year sensitivity analysis showed that the optimum grain yields were obtained with application of 150 kg N + 30 kg P ha–1 to the two varieties in Kano and Zaria. In Abuja, the optimum grain yields were obtained with the application of 150 kg N ha–1 + 30 kg P ha–1 to SAMMAZ–15 and 120 kg N ha–1 + 30 kg P ha–1 to SAMMAZ–16. When P is not applied, the simulation results show that across all N rates, maize yield would decrease by 25%–52% and 32%–52% for the mid- and end-of-century, respectively, under RCP4.5 for both varieties. There would be a greater reduction under RCP8.5, with a decrease of 32%–59% and 52%–69% under mid- and end-of-century scenarios, respectively. When P is applied at 30 kg ha−1, the reduction in yield due to climate change is lower. Under RCP4.5, the yield would decrease by 9%–15% and 11%–21% for the mid- and end-of-century, respectively. There would be a reduction of 12%–21% and 32%–41% for mid-century and end-of-century, respectively, under RCP8.5 scenario. This suggests that the application of optimum P could reduce the impact of yield loss due to climate change.

Soil fertility thresholds driven by sand content indicate drylands degradation phases on the Tibetan Plateau

AbstractUnderstanding the process of dryland degradation is crucial to combating desertification and maintaining land sustainability. To identify important indicators of dryland degradation, we conducted a field survey of 46 drylands spanning an aridity gradient across the Tibetan Plateau. Our random forest model suggested that soil sand content was the most important predictor of variation in soil fertility (carbon, nitrogen, and phosphorus). Most soil fertility showed a nonlinear and abrupt decline with increasing soil sand content, and displayed three sequential thresholds. First, soil organic carbon sharply decreased beyond a soil sand content threshold of 47%. Second, exceeding a subsequent threshold of 70%, the total nitrogen and light fraction carbon declined dramatically with increasing soil sand content. Finally, soil heavy fraction carbon, inorganic carbon, and total carbon were drastically reduced when the soil sand content exceeded 80%. The three soil fertility thresholds along the gradient of the soil sand content indicated a sequence of dryland degradation phases. Our findings provide an approach for detecting the severity of dryland degradation and suggest vulnerable areas of degradation on the Tibetan Plateau.