Plant Nutrients and Soil Fertility Management

Random forest machine learning for maize yield and agronomic efficiency prediction in Ghana

Maize (Zea mays) is an important staple crop for food security in Sub-Saharan Africa. However, there is need to increase production to feed a growing population. In Ghana, this is mainly done by increasing acreage with adverse environmental consequences, rather than yield increment per unit area. Accurate prediction of maize yields and nutrient use efficiency in production is critical to making informed decisions toward economic and ecological sustainability. We trained the random forest machine learning algorithm to predict maize yield and agronomic efficiency in Ghana using soil, climate, environment, and management factors, including fertilizer application. We calibrated and evaluated the performance of the random forest machine learning algorithm using a 5 × 10-fold nested cross-validation approach. Data from 482 maize field trials consisting of 3136 georeferenced treatment plots conducted in Ghana from 1991 to 2020 were used to train the algorithm, identify important predictor variables, and quantify the uncertainties associated with the random forest predictions. The mean error, root mean squared error, model efficiency coefficient and 90 % prediction interval coverage probability were calculated. The results obtained on test data demonstrate good prediction performance for yield (MEC = 0.81) and moderate performance for agronomic efficiency (MEC = 0.63, 0.55 and 0.54 for AE-N, AE-P and AE-K, respectively). We found that climatic variables were less important predictors than soil variables for yield prediction, but temperature was of key importance to yield prediction and rainfall to agronomic efficiency. The developed random forest models provided a better understanding of the drivers of maize yield and agronomic efficiency in a tropical climate and an insight towards improving fertilizer recommendations for sustainable maize production and food security in Sub-Saharan Africa.

Making partners in the city: impact of urban soil P enrichment on the partnership between an invasive herb and arbuscular mycorrhizal fungi in a tropical city

The mutualistic relationship between plants and arbuscular mycorrhizal (AM) fungi is essential for optimal plant nutrition, enabling plants to better withstand biotic and abiotic stressors and enhancing survival, reproduction, and colonization of new environments. Activities, such as soil enrichment or compaction, may decrease the benefits of AM fungi for plants, potentially reducing interactions in urban environments. Here, we examine this prediction by studying how urbanization alters AM interactions with the invasive herb Ruellia nudiflora (Acanthaceae). We collected soil and plants from deep urban sites (DUS; e.g., sidewalks), open urban sites (OUS; parks), and rural sites (RS) to analyse soil nutrient content, plant morphology, AM colonization rates, spore density, richness, and diversity. Contrary to predicted, DUS had the lowest soil nutrient concentration, except for phosphorus, reducing AM colonization. This supports the prediction of reduced AM interactions in urban environments. We also found that potassium affects the AM association. Urban plants had smaller and more compact root systems compared to their rural counterparts, but there were no discernible differences in AM fungi communities between urban and rural environments. Phosphorus enrichment in sidewalks is the main driver of reductionof R. nudiflora-AM fungi interactions in Mérida. More studies are needed to gain a better understanding of how AM fungi contribute to plant colonization in urban environments.

Determination of soil quality index in areas with high erosion risk and usability in watershed rehabilitation applications

Erosion is an important environmental issue threatening natural resources and ecosystems, especially soil and water. Soil losses occur in many parts of the world due to erosion at different degrees, and various rehabilitation plans have been carried out to reduce these losses. However, soil protection applications are generally carried out by considering only the essential characteristics of the soil. This may decrease the chance of success of rehabilitation applications. The present study aimed to determine the soil quality index (SQI) by weighting the soil quality parameters according to the analytical hierarchy process (AHP) in the Çapakçur microcatchment (Bingöl, Türkiye) where soil loss is high. Accordingly, 428 soil samples were taken from the study area and analyzed. The soil losses in the Çapakçur watershed were calculated employing the revised universal soil loss equation (RUSLE). To determine the soil quality index, a total of 20 indicators were used, including (i) physical soil properties, (ii) chemical soil properties, and (iii) soil nutrient content. Soil quality index results are divided into classes between 1 and 5. As a result of the study, the annual total amount of soil lost from the microcatchment was calculated as 96,915.20 tons, and the yearly average amount of soil lost from the unit area was calculated as 10.14 tons ha^-1. According to SQI, the largest area in the microcatchment was Class-2 (weak), with 39.49%, whereas the smallest area was 1.4% (the most suitable). However, it was determined that there was a significant negative relationship between SQI and soil erodibility. Considering the SQI distribution of the area in the planning of soil protection and erosion prevention practices in watershed rehabilitation studies may increase success.

Health risk implications of iron in wastewater soil-food crops grown in the vicinity of peri urban areas of the District Sargodha

Irrigation using sewage water can be beneficial, as it can increase the productivity of crops but has negative consequences on crops, soil contamination, and human health. It contains a variety of toxins, such as chemicals and heavy metals, which damage the soil and crops. In this regard, the aim of the research was to assess the potential health hazards of iron (Fe) metal in food crops (leafy and root crops) treated with wastewater (T_1), canal water (T_2), and tube well water (T_3). Water, soil, and edible components of food crops were collected at random from three distinct locations. Fe concentration in samples was estimated using atomic absorption spectrophotometer, following wet digestion method. The Fe concentrations, ranged from 0.408 to 1.03 mg/l in water, 31.55 to 187.47 mgkg-1 in soil and 4.09 to 32.583 mgkg-1 in crop samples; which were within permissible limits of the World Health Organization (WHO). There was a positive correlation between soils and crops. The bioconcentration factor, enrichment factor (EF), daily intake of metals (DIM), health risk index (HRI), and target hazard quotient (THQ) all values were <1, except for a pollution load index >1, which indicated soil contamination, but there was no Fe toxicity in crops, no health risk, and no-carcinogenic risk for these food crops in humans. To prevent the excessive accumulation of Fe metal in the food chain, regular monitoring is needed.

A suitable organic fertilizer substitution ratio could improve maize yield and soil fertility with low pollution risk

Organic fertilizer substitution (OFS) is an effective strategy for reducing the chemical fertilizer usage; however, the effects of different OFS ratios (OFSRs) on maize yield, soil fertility, and heavy metal pollution risk are still unclear. Therefore, determining a suitable OFSR is important. Through the pot experiment, no fertilizer (CK) and organic fertilizer substituting 0% (CF, chemical fertilizer alone), 8% (OF8), 16% (OF16), and 24% (OF24) of the chemical N fertilizer were set to investigate the effects of different OFSRs on maize growth and yield, soil properties (available nutrients, carbon fractions, and carbon pool indices), and nutrients and heavy metals in grain and soil. The results showed that OF8, OF16, and OF24 improved soil fertility by increasing soil organic carbon (SOC, by 10.05-16.26%) and its fractions, most middle- and micro-nutrients content, and carbon pool management index (CPMI, by 17.45-30.31%) compared with CF, while improving grain nutritional quality. However, they increased heavy metals content in grain and soil and their Nemerow comprehensive pollution index (NCPI, by 4.06-16.56% in grain and 2.55-5.57% in soil) but did not cause pollution. Among them, throughout the growth period, only OF8 treatment increased soil available nitrogen (AN), phosphorus (AP), and potassium (AK) content by 3.04-11.15%, 7.11-8.05%, and 0.12-6.05%, respectively, compared with CF, which thus significantly promoted maize growth and increased yield (by 35.65%); the NCPI of grain and soil was however lower than that OF16 and OF24. In conclusion, substitution ratio of 8% was considered ideal for promoting maize growth, improving yield and soil fertility, with a low pollution risk. The results of this study would aid in guiding the scientific application of OFS technology to agricultural production, thereby contributing to resource utilization of organic waste and sustainable agricultural development.

A tale of two players: the role of phosphate in iron and zinc homeostatic interactions

This minireview details the impact of iron-phosphate and zinc-phosphate interactions in plants and provides perspectives for further areas of research regarding nutrient homeostasis. Iron (Fe) and zinc (Zn) are among the most important micronutrients for plant growth and have numerous implications for human health and agriculture. While plants have developed efficient uptake and transport mechanisms for Fe and Zn, emerging research has shown that the availability of other nutrients in the environment influences the homeostasis of Fe and Zn within plants. In this minireview, we present the current knowledge regarding homeostatic interactions of Fe and Zn with the macronutrient phosphorous (P) and the resulting physiological responses to combined deficiencies of these nutrients. Fe and P interactions have been shown to influence root development, photosynthesis, and biological processes aiding Fe uptake. Zn and P interactions also influence root growth, and coordination of Zn-dependent transcriptional regulation contributes to phosphate (Pi) transport in the plant. Understanding homeostatic interactions among these different nutrients is of critical importance to obtain a more complete understanding of plant nutrition in complex soil environments.

Valorization of anaerobic digestion digestate: A prospect review

Anaerobic digestion is recognized as promising technology for bioenergy production from biowaste, with huge quantity of digestate being produced as the residual waste. The digestate contains substantial amounts of organic and inorganic matters that be considered highly risky contaminants to the receiving environments if not properly treated, but also potential renewable resources if are adequately recovered. This prospect review summarized the current research efforts on digestate valorization, including aspects of resource recovery and the proposed applications, particularly on the conversion techniques and economic feasibility. The prospects for digestate valorization were highlighted at the end of this review.

Chlorophyll fluorescence as a tool for nutrient status identification in rapeseed plants

In natural conditions, plants growth and development depends on environmental conditions, including the availability of micro- and macroelements in the soil. Nutrient status should thus be examined not by establishing the effects of single nutrient deficiencies on the physiological state of the plant but by combinations of them. Differences in the nutrient content significantly affect the photochemical process of photosynthesis therefore playing a crucial role in plants growth and development. In this work, an attempt was made to find a connection between element content in (i) different soils, (ii) plant leaves, grown on these soils and (iii) changes in selected chlorophyll a fluorescence parameters, in order to find a method for early detection of plant stress resulting from the combination of nutrient status in natural conditions. To achieve this goal, a mathematical procedure was used which combines principal component analysis (a tool for the reduction of data complexity), hierarchical k-means (a classification method) and a machine-learning method-super-organising maps. Differences in the mineral content of soil and plant leaves resulted in functional changes in the photosynthetic machinery that can be measured by chlorophyll a fluorescent signals. Five groups of patterns in the chlorophyll fluorescent parameters were established: the 'no deficiency', Fe-specific deficiency, slight, moderate and strong deficiency. Unfavourable development in groups with nutrient deficiency of any kind was reflected by a strong increase in F _o and ΔV/Δt ₀ and decline in φ _Po, φ _Eo δ _Ro and φ _Ro. The strong deficiency group showed the suboptimal development of the photosynthetic machinery, which affects both PSII and PSI. The nutrient-deficient groups also differed in antenna complex organisation. Thus, our work suggests that the chlorophyll fluorescent method combined with machine-learning methods can be highly informative and in some cases, it can replace much more expensive and time-consuming procedures such as chemometric analyses.