The Pattern Recognition of Small-Scale Privately-Owned Forest in Ciamis Regency, West Java, Indonesia

Small-scale Privately-owned Forest (SSPF) has various patterns identification, based on the stand structure and species composition. The recognition and classification of the SSPF cropping patterns are required for further planning and policy development. Therefore, this study aims to classify the cropping pattern of SSPF in Ciamis Regency, West Java Province, Indonesia. The data were collected by observing the stand structure and species composition of 150 plots of land, encompassing three Sub-districts representing the central, northern, and southern regions of Ciamis Regency. The four categorical variables include tree species composition, age, spatial distribution, and intercropping pattern. While the two continuous variables were stand density and basal area. The patterns obtained were classified based on a Two-Step Cluster algorithm with log-likelihood distance measure, and auto clustering using Schwarz's Bayesian Information Criterion, validated by silhouette index. In addition, a multicollinearity test was conducted to reduce redundancy in using variable sets. The results showed that, the improvement of the cluster quality based on the silhouette index value, was achievable by excluding the tree spatial distribution variable, which exhibits multicollinearity. The cropping patterns were classified into three categories, namely tree crops, mixed-tree lots, and agrisilviculture for group-1, group-2, and group-3, respectively. Group-1 consisted of stands with one or two commercial tree species, and in several cases, were intercropped. Group-2 contained uneven-aged mixed-tree stands without any crops. While Group-3 consisted of an intercropping system of uneven-aged mixed-tree stands and crops. The results suggest further analysis, in order to relate the cropping patterns with the socio-economic characteristics of the landowners, as well as the strategies for the development of a sustainable SSPF.

What Farm Size Sustains a Living? Exploring Future Options to Attain a Living Income From Smallholder Farming in the East African Highlands

Smallholder farming in sub-Saharan Africa keeps many rural households trapped in a cycle of poor productivity and low incomes. Two options to reach a decent income include intensification of production and expansion of farm areas per household. In this study, we explore what is a “viable farm size,” i.e., the farm area that is required to attain a “living income,” which sustains a nutritious diet, housing, education and health care. We used survey data from three contrasting sites in the East African highlands—Nyando (Kenya), Rakai (Uganda), and Lushoto (Tanzania) to explore viable farm sizes in six scenarios. Starting from the baseline cropping system, we built scenarios by incrementally including intensified and re-configured cropping systems, income from livestock and off-farm sources. In the most conservative scenario (baseline cropping patterns and yields, minus basic input costs), viable farm areas were 3.6, 2.4, and 2.1 ha, for Nyando, Rakai, and Lushoto, respectively—whereas current median farm areas were just 0.8, 1.8, and 0.8 ha. Given the skewed distribution of current farm areas, only few of the households in the study sites (0, 27, and 4% for Nyando, Rakai, and Lushoto, respectively) were able to attain a living income. Raising baseline yields to 50% of the water-limited yields strongly reduced the land area needed to achieve a viable farm size, and thereby enabled 92% of the households in Rakai and 70% of the households in Lushoto to attain a living income on their existing farm areas. By contrast, intensification of crop production alone was insufficient in Nyando, although including income from livestock enabled the majority of households (73%) to attain a living income with current farm areas. These scenarios show that increasing farm area and/or intensifying production is required for smallholder farmers to attain a living income from farming. Obviously such changes would require considerable capital and labor investment, as well as land reform and alternative off-farm employment options for those who exit farming.

Cropping pattern of Konkan region on the basis of probabilistic rainfall and soil characteristics

ABSTRACT- Cropping pattern at any place of humid tropics basically depends on soils and efficient management of abundant rainfall. Climate of Konkan region IS warm and humid. Rainfall is very high and varies usually from 200 to 350 cm. Rainfall probability has been computed at different levels of 16 stations of this region by fitting Gamma distribution model to weekly rainfall total. The existing cropping pattern was studied in relation with assured rainfall at different probability levels. Suitable cropping patterns based on assured rainfall and soils of the region have been suggested to increase production.

Effects of Cropping Pattern on Carbon Sequestration And Aggregate Stability of Soil in Long Term Agricultural Fields in Khulna, Bangladesh

Effects of cropping pattern on soil carbon sequestration and their aggregate stability in long term agricultural fields was investigated in 2018. Four cropping patterns were selected that have been cultivated for last ten years. Results showed that Soil organic carbon (SOC) value was improved for vegetable field from 4.06 to 9.11 g/kg and carbon stock (20.14 Mg C ha/yr) as well as soil carbon sequestration rate was the highest in vegetable field (1.12 Mg C ha/yr). The logarithmic relationship between the C input and C sequestration rate showed the strong correlation (r = 0.72, p < 0.05). In terms of aggregate stability, vegetable field put the best result (0.41 mm) (p > 0.05). The straight-line relation between aggregate stability and Cstock established that they are strongly correlated (r = 0.81, p < 0.05). Finally, results indicated that Vegetable-Vegetable-Vegetable cropping pattern was the best soil carbon sequester along with the best aggregate stability. Bangladesh J. Bot. 50(4): 1029-1034, 2021 (December)

Pelatihan Agro Eco-System Analysis Petani Ketela Pohon dalam Teknik Pengelolaan Organisme Pengganggu Tanaman

<em><strong>Training on Agro Eco-System Analysis for Cassava Farmers in Plant Pest Organism Management Techniques</strong>. </em>Pest population fluctuations in cassava plants tend to increase and spread rapidly in drought fields and a monoculture cropping pattern with close spacing therefore the presence of pests planted is highly dependent on agro-ecosystem conditions. Therefore ecological-based pest control is very necessary. To maintain the stability of the plant ecosystem, basic skills are needed in conducting agroecosystem analysis (AESA). Based on the analysis results obtained recommendations for appropriate ecosystem management for each growing season and facilitate farmers in determining good cultivation techniques regarding pest control, cropping patterns, soil and water conservation as well as natural enemies that are appropriate for their plants. AESA activities are carried out so that farmers understand and are skilled in managing their cassava plantations because Randotonda Village is a producer of "Nuabosi" cassava which is known as a regional superior product. The activity is carried out in a participatory manner by directly involving the participating farmers as observers, fact seekers and decision-makers for the management of their agroecosystems through discussion and manifesting current real conditions with the hope that in the future they can manage their cropping agroecosystems properly. The highest increase in farmer understanding occurred in natural enemy components of 91.67% while the average increase in farmer understanding for all agro-ecosystem components was 57.14%. All participants were able to perform AESA very well which was indicated by the ability of farmers to make recommendations for managing cassava agroecosystems for the next planting season.

Impacts of Rice Field Winter Planting on Soil Organic Carbon and Carbon Management Index

To tackle with the problem of prevailing farmland abandonment in winter, 5 treatments includes Chinese milk vetch-double cropping rice (CRR), rape-double cropping rice (RRR), garlic-double cropping rice (GRR), winter crop multiple cropping rotation (ROT), winter fallow control (WRR) were set up. By measuring soil total organic carbon, active organic carbon and its components and calculating the soil carbon pool management index in 0~15 cm and 15~30 cm soil layers in the early and late rice ripening stage. The effects of different winter planting patterns on the changes of soil organic carbon and carbon pool management index were discussed. In order to provide theoretical basis for the optimization and adjustment of winter planting pattern of double cropping rice field in the middle reaches of Yangtze River. The results showed that soil total organic carbon, active organic carbon and its components in different winter cropping patterns were increased, and ROT and CRR treatments were more beneficial to the accumulation of soil total organic carbon, active organic carbon and its components as well as the improvement of soil carbon pool management index, which should be preferred in the adjustment of cropping patterns.

Optimal Land And Water Allocation Model For The Maximization of Net Agricultural Return In Drought Prone Regions

In this study, the land and water resources allocation model was developed to determine optimal cropping patterns and water resources allocations at different rainfall probability exceedance levels (PEs) to ensure maximum agricultural return in the Hormat-Golina valley irrigation command area, Ethiopia. To account the uncertainty of rainfall variability, the monthly dependable rainfall was estimated at three levels of reliability (20, 50 and 80% PEs) which are representing wet, normal and dry seasons based on regional experience. The irrigation water demand which was used as an input to the optimization model was estimated at each level of reliability by using CROPWAT model. The net annual returns of optimal cropping patterns were estimated as 181, 179 and 175 million Ethiopia Birr at 20 %, 50 % and 80 % PEs, respectively. The result of the optimal cropping pattern indicates that, the net annual return of the command area was increased to 45.75%, 45.84% and 47.01% than the Government targeted at 20%, 50% and 80% PEs, respectively. The findings reveal that the optimal land and water resources allocation model is very useful to the planners and decision makers to maximize the agricultural return particularly in areas where land and water resources are limited.

Changes of Extreme Agro-Climatic Droughts and Their Impacts on Grain Yields in Rain-Fed Agricultural Regions in China over the Past 50 Years

Climate change and climate extremes and their impacts on agriculture, water resources, and ecosystems have become important issues globally. Agricultural sustainability and food security are facing unprecedented challenges due to the increasing occurrence of extreme climatic events, including, notably, extreme droughts in recent years in China. In this study, a threshold determination model of extreme agro-climatic droughts (EADs) was built based on the cumulative probability distribution functions (CDF) of an agricultural drought index—the consecutive days without available precipitation (CDWAP). The CDWAP was established by combining meteorological data with the characteristics of cropping patterns and the water requirement in different growing periods of crops. The CDF of CDWAP was obtained based on the relationship of CDWAP and its occurrence frequency. Based on the model, the spatial pattern of the thresholds of EADs and the threshold exceedance time series of EADs in 500 meteorological stations were obtained, and then changes in the frequencies and intensities of EADs in China and their impacts on grain yields in rain-fed regions during the past 50 years were analyzed. The results follow: (1) The threshold value of EADs in China gradually increased from southeast to northwest. The stations of the highest value were located in the Northwest China, with the CDWAP more than 60 days, while the lowest value was in the middle reaches of the Yangzi River, with the CDWAP less than 16 days. (2) The frequencies and intensities of the EADs increased mostly in the east areas of the Hu Huanyong line, which was also the main agricultural production region in China. The North China (NC) and Southwest China (SW) regions showed the highest increasing rates of the EADs; their frequencies and intensities were 11.3% and 2.2%, respectively, for the NC region, and 9.3% and 2.7%, respectively, for the SW region. (3) Case studies in the NC, SW, and SE regions indicated that there was a negative correlation between grain yields and EAD frequency and intensity; i.e., the low grain yields often occurred in the year with relatively higher frequency or/and stronger intensity of EADs. The correlation coefficients of grain yield and EAD were generally greater than that of merely extreme climatic droughts; therefore, the study of EAD is necessary when researching the impacts of extreme drought events on grain yield.

Toward a Simple and Generic Approach for Identifying Multi-Year Cotton Cropping Patterns Using Landsat and Sentinel-2 Time Series

The sustainable development goals of the United Nations, as well as the era of pandemics have introduced serious challenges for agricultural production and management. Precise management of agricultural practices based on satellite-borne remote sensing has been considered an effective means for monitoring cropping patterns and crop-farming patterns. Therefore, we proposed a simple and generic approach to identify multi-year cotton-cropping patterns based on time series of Landsat and Sentinel-2 images, with few ground samples that covered many years, a simple classification algorithm, and had a high classification accuracy. In this approach, we extended the size of training samples using active learning, and we employed a random forest algorithm to extract multi-year cotton planting patterns based on dense time series of Landsat and Sentinel-2 data from 2014 to 2018. We created annual crop cultivation maps based on training samples with an accuracy greater than 95.69%. The accuracy of multi-year cotton cropping patterns was 96.93%. The proposed approach was effective and robust in identifying multi-year cropping patterns, and it could be applied in other regions.

Estimating Actual Evapotranspiration over Croplands Using Vegetation Index Methods and Dynamic Harvested Area

Advances in estimating actual evapotranspiration (ETa) with remote sensing (RS) have contributed to improving hydrological, agricultural, and climatological studies. In this study, we evaluated the applicability of Vegetation-Index (VI) -based ETa (ET-VI) for mapping and monitoring drought in arid agricultural systems in a region where a lack of ground data hampers ETa work. To map ETa (2000–2019), ET-VIs were translated and localized using Landsat-derived 3- and 2-band Enhanced Vegetation Indices (EVI and EVI2) over croplands in the Zayandehrud River Basin (ZRB) in Iran. Since EVI and EVI2 were optimized for the MODerate Imaging Spectroradiometer (MODIS), using these VIs with Landsat sensors required a cross-sensor transformation to allow for their use in the ET-VI algorithm. The before- and after- impact of applying these empirical translation methods on the ETa estimations was examined. We also compared the effect of cropping patterns’ interannual change on the annual ETa rate using the maximum Normalized Difference Vegetation Index (NDVI) time series. The performance of the different ET-VIs products was then evaluated. Our results show that ETa estimates agreed well with each other and are all suitable to monitor ETa in the ZRB. Compared to ETc values, ETa estimations from MODIS-based continuity corrected Landsat-EVI (EVI2) (EVIMccL and EVI2MccL) performed slightly better across croplands than those of Landsat-EVI (EVI2) without transformation. The analysis of harvested areas and ET-VIs anomalies revealed a decline in the extent of cultivated areas and a loss of corresponding water resources downstream. The findings show the importance of continuity correction across sensors when using empirical algorithms designed and optimized for specific sensors. Our comprehensive ETa estimation of agricultural water use at 30 m spatial resolution provides an inexpensive monitoring tool for cropping areas and their water consumption.