Effect of reduced tillage under various mulch types on soil fertility and yield of an organic pepper crop

The effects of mulching on soil characteristics and plant development for pepper crops conducted in organic farming were studied in the Sahel coast of Tunisia. The types of mulching used for tests are straw mulching, compost mulching, and plastic mulching which were compared with bare soil without mulching (control). The soil characteristics under these different mulching systems were evaluated by soil physical and biological parameters such as penetration resistance at 0-60 cm layer, microbial biomass, organic matter, and water content at 0-20 cm layer. Measurements were performed every week for 50 days. The obtained results showed that compost mulching led to a better growth rate and improved the structural and water state of cultivated soil by decreasing its resistance to penetration and increasing its organic matter content. It can be concluded that the "Beldi" organic pepper crop under conservation tillage and compost mulching has presented the best combination thus leading to motivating results such as the positive effects of soil physical properties and microbial biomass on the final crop yield

Prediction of organic potato yield using tillage systems and soil properties by artificial neural network (ANN) and multiple linear regressions (MLR)

Tillage aims to prepare the soil with the adequate treatment to create the ideal and most favorable conditions for cultivation. To evaluate the effect of tillage systems on soil environment, it is mandatory to measure the modifications in physical, chemical and biological properties. In recent decades, artificial intelligence systems were used for developing predictive models to simplify, estimate and predict many farming processes. They are also employed to optimize performance and control risks. These systems have become true virtual helpers, and more so when integrated with predictive analytics. In the present study, the effects of tillage systems on soil properties and crop production and the predictive capabilities of multiple linear regressions (MLR) and artificial neural networks (ANN) are evaluated to estimate organic potato crop yield including soil microbial biomass (MB), soil resistance to penetration, soil organic matter (OM) and tillage system. Potato yield was found to be significantly impacted by tillage and soil properties. The results showed that MLR model estimated crop yield more accuracy than ANN model. Correlation coefficient and root mean squared (RMSE) were 0.97 and 0.077 between the measured and the estimated data by the ANN model, respectively. Generally, the ANN model showed greater potential in determining the relationship between potato yield, tillage and soil properties

Effect of reduced tillage under various mulch types on soil fertility and yield of an organic pepper crop

The effects of mulching on soil characteristics and plant development for pepper crops conducted in organic farming were studied in the Sahel coast of Tunisia. The types of mulching used for tests are straw mulching, compost mulching, and plastic mulching which were compared with bare soil without mulching (control). The soil characteristics under these different mulching systems were evaluated by soil physical and biological parameters such as penetration resistance at 0-60 cm layer, microbial biomass, organic matter, and water content at 0-20 cm layer. Measurements were performed every week for 50 days. The obtained results showed that compost mulching led to a better growth rate and improved the structural and water state of cultivated soil by decreasing its resistance to penetration and increasing its organic matter content. It can be concluded that the "Beldi" organic pepper crop under conservation tillage and compost mulching has presented the best combination thus leading to motivating results such as the positive effects of soil physical properties and microbial biomass on the final crop yield

Effects of tractor forward velocity on soil compaction under different soil water contents in Tunisia

Africa is the second continent suffering from soil compaction; studies of this phenomenon must be multiplied in order to overcome this problem. Very few studies have been conducted in Tunisia to understand soil compaction, its causes and its effect on soil properties. The research was conducted on experimental field at the Higher Institute of Agronomy of Chott Mariam, Sousse, Tunisia. The main objective of this study was to evaluate the effect of different speed of tractor compaction on soil, that is, no compaction (C0), speed 1 (C2) = 4 km h-1, speed 2 (C3) = 9 km h-1 on the hydraulic and physical properties of a silt loam texture under three natural moisture conditions: H0, H1 (15 days later), and H2 (30 days later). Each test run was limited to one pass. Undisturbed soil cores were collected in the topsoil (0-10 cm), at 10-20 cm and in the subsoil (20-30 cm) below the trace of the wheel at site. Soil compaction level was determined by penetration resistance using a penetrologger. Bulk density was then determined to evaluate the impact of the two tractor frequency passages at the three moisture conditions on soil compaction. For initial soil (C0), bulk density was 1.38 Mg m-3. After the tractor pass, the highest degree of compaction was observed with tractor speed 1 (C1) which signi?cantly changed soil bulk density resulting in values of up to 1.74 Mg m-3 in the topsoil and compacted subsoil under H1, which is significantly above the critical value of 1.6 Mg m-3 for soils with clay content below 17.5%. The high degree of compaction signi?cantly affected penetration resistance of topsoil. The results demonstrate that different degrees of soil compaction under different moisture levels could greatly influence physical properties in different ways. Even under relatively low water contents, that is, below field capacity, substantial top soil compaction was induced after one tractor pass

Development of Artificial Neural Networks to Predict the Effect of Tractor Speed on Soil Compaction Using Penetrologger Test Results

African agriculture is adversely impacted by arable soil compaction, the degree of which is affected by the speed at which the tractor is maneuvered on the fields, which affects the degree of soil compaction. However, there is no reliable, existing mathematical correlation between the extent of compaction on the one hand, and the tractor speed/s and soil moisture levels on the other. This paper bridges this gap in knowledge by resorting to the artificial neural networks (ANNs) method to predict the effects of tractor speed and soil moisture on the state of soil compaction. The models were ‘trained’ with penetration resistance (CPR) and bulk density test data obtained from field measurements. The resulting correlation coefficient (R = 0.9) showed good compliance of the prediction made with the ANN models with on-field data. It follows, thereby, that the model developed by the authors in this study can be effectively used for predicting the effects of speed, soil density, and moisture content on compaction of alluvial, poorly developed soil with much greater precision, thereby providing guidance to farmers around the world